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Gift Guide: 12 must-read books for 2019 as recommended by Extra Crunch readers

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Books are fundamentally about stories, and 2019 (and really, the past decade) has been the story of technology’s domination of every industry and function of society. Founders and tech executives are more powerful than ever, and how we use that power for good or evil will deeply shape the future of our world.

Whether it’s the sudden rise of TikTok and the ubiquity of social networks in business, economics, and politics, or the coming conflagration of climate change, or the challenges of personal and professional development, or just finding your way in building a startup, there was just an avalanche of books published this year on every topic near and dear to a technologist’s and founder’s heart.

I wanted to get a sense of what our readers thought were the best books they read this year, and so I reached out to our Extra Crunch membership to ask for their recommendations. Perhaps unsurprisingly for a group of people who actually pay for deeper journalism, our EC readers submitted dozens and dozens of book recommendations on every subject imaginable.

From those recommendations, I carefully selected a list of just 12 books that seemed the most recommended by our readers and also captured the zeitgeist of the times we are living in. Every book here is great and important, and I only wish we had more time to read them all.

How to handle the coming total disruption of society by technology

Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries by Safi Bahcall

St. Martin’s Press / 368 pages / March 2019

Publisher’s Link

Anyone who has worked long enough in innovation and technology knows that great ideas can come from anywhere. But how do those ideas actually go from mere thoughts to actions and products, while avoiding the organizational politics that often prevent them from seeing the light of day in the first place?

Safi Bahcall, a PhD physicist from Stanford who co-founded and led Synta Pharmaceuticals as CEO through its IPO on NASDAQ in 2007, has been thinking about serendipity in science for years, and Loonshots is his first book. In it, Bahcall borrows concepts from science to move beyond looking purely at organizational culture to investigating organizational structure, investigating how we design our teams and how that can play an outsized role in whether new ideas flourish — or are killed on the spot.

Widely lauded by luminaries and a bestseller on Amazon and the Wall Street Journal, the book asks one of the most important questions in innovation today and gives a series of vignettes on how to improve our ability to handle spontaneity. A great book for the disrupting — and the disrupted.

The Technology Trap: Capital, Labor, and Power in the Age of Automation by Carl Benedikt Frey

Princeton University Press / 480 pages / June 2019

Publisher’s Link

Digital disruption is all around us. Artificial intelligence is quickly eliminating millions of middle-class jobs, and fears of automation are growing among more and more workers, polarizing our politics and complicating the future of business.

Yet, all of this has happened before. More than a century ago, technologies like replaceable parts and the steam engine combined to create one of the greatest transformations our society has ever seen in the Industrial Revolution. But just how did the Industrial Revolution happen, and how did it affect everyday people in England, America, and elsewhere?

Carl Benedikt Frey, a fellow at Oxford University and director of the Programme on Technology & Employment at the Oxford Martin School, investigates the short, medium, and long-term consequences of the Industrial Revolution on workers, finding that in fact the changes had extraordinarily negative consequences in the short term. His lessons from this pivotal moment in history can help technology leaders avoid the biggest risks today in how we design human/AI systems in the coming age of automation.

Digital Transformation: Survive and Thrive in an Era of Mass Extinction by Thomas M. Siebel

RosettaBooks / 256 pages / July 2019

Publisher’s Link

You’ve re-built your structure based on Loonshots, and learned the lessons of The Technology Trap, but ultimately, many leading enterprise companies today are facing extinction from a number of new technology waves like elastic cloud computing and the internet of things. For those not doing the disrupting but rather on the receiving end, what exactly are you supposed to do?

Billionaire entrepreneur Tom Siebel, who founded Siebel Systems and eventually merged it into Oracle in 2006 for nearly $6 billion, wrote his first book in almost two decades on the topic of how legacy companies can navigate these turbulent times. Through Digital Transformation, Siebel tries to offer the disrupted a primer on just what is going on in AI and other big tech waves to help executives understand what strategies they can use to defend their businesses.

A brisk and reasonably short read, Digital Transformation offers key lessons, even if they may well be ignored by most before it is too late.

How to deal with tech’s inadequacies and head-banging, stupid behavior

Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech by Sara Wachter-Boettcher

W. W. Norton & Company / 240 pages / October 2017

Publisher’s Link

While we love writing about the growth of innovative products and startups here at TechCrunch, the other side of that coin is that there has been a constant cavalcade of dumb actions by founders and engineers the past few years that has turned many sour on the future of our industry. Whether it is sexism in financial underwriting or employee political controversies (stories just from the last few days), technology is increasingly under a microscope — and the industry doesn’t look good at full resolution.

Technically Wrong, a book by consultant and tech critic Sara Wachter-Boettcher, tries to take a more playful approach to all these challenges by just sort of splaying them all out together for the world to see. While ostensibly targeted at the general public, the idiocies that Wachter-Boettcher identifies should be taught in every software engineering, product management, and UX design class.

As one Extra Crunch member wrote in their endorsement:

This is my favourite book. It highlights examples of bias in tech and how this has led to negative or even harmful applications in society. It makes a strong case for any developer to consider how their tech may be biased or have potential to be used for harm. A must read.

Given the plague of scandals hitting tech, the book is perhaps a tad out of date just two years post-publication, but its lessons are invaluable and will stand the test of time.

Targeted: The Cambridge Analytica Whistleblower’s Inside Story of How Big Data, Trump, and Facebook Broke Democracy and How It Can Happen Again by Brittany Kaiser

Harper / 400 pages / October 2019

Publisher’s Link

Perhaps no scandal has rocked the tech industry — or politics in general — quite like the Cambridge Analytica imbroglio that not only showed the power that Facebook and other social networks have over us through our user data, but also the scale to which that data influences purchasing decisions and of course, our elections.

Brittany Kaiser was a consultant and former Obama campaign worker who joined Cambridge Analytica hoping to make a difference. I guess in a way she did, eventually learning the true nature of big data and how that intersects with the needs of campaign managers. Through Targeted, she writes about her experience on the ground floor of the organization, and also places the company in the context of the broader challenges facing technology and ethics going forward. It’s a cri de coeur for other tech industry workers to think about how their work is affecting society and perhaps tapping out in much the way that Kaiser did.

Targeted is competing directly with Mindf*ck: Cambridge Analytica and the Plot to Break America by Christopher Wylie for this year’s best memoir on the sordid story. Targeted got the recs from EC readers though, and it’s certainly a story that deserves more than one point of view.

How to think about the biggest news stories this year

We Are The Weather: Saving the Planet Begins at Breakfast by Jonathan Safran Foer

Farrar, Straus and Giroux / 288 pages / September 2019

Publisher’s Link

Climate change was all over the news this year, and perhaps nowhere more than in tech’s central headquarters of Silicon Valley, which faced fires and repeated blackouts this year as utility company PG&E struggled to deliver power amidst California’s changing climate. But climate change isn’t just something that is “happening” — it’s being driven by the choices we make every single day.

Long-time novelist Jonathan Safran Foer returns to the theme of his sole non-fiction book Eating Animals to look at how our decisions around food are directly impacting the health of the planet. While it may seem like what we eat for breakfast is but a minor drop of carbon in a massive ocean, the reality is that our collective and aggregated decisions have huge implications for how our food systems are organized.

Foer brings his literary talents to bear on the subject, creating a textured and at times stream-of-consciousness account that interleaves climate change fear, personal anecdotes, and short stories to create a compelling case for changing our daily habits in ways that align with the needs of our environment. It may not be tuned to every reader’s preferred style, but few books connect all the dots on this subject quite like We Are The Weather

AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee

Houghton Mifflin Harcourt / 272 pages / September 2018

Publisher’s Link

Another storyline that just kept rearing its head in the headlines this year was China. From the trade war and tariffs between Trump and Xi, to the increasing security risks of Chinese industrial espionage, to the surveillance technology that companies like Huawei are exporting to undergird digital authoritarianism, China’s actions are transforming our world (and at least from this side of the Pacific, not for the positive).

One locus of competition between the U.S. and China though remains focused on artificial intelligence, and which country will take the lead in this critical new market. China has invested prodigious amounts of funding into the industry through its Made in China 2025 plan, while the United States continues to have some of the leading research groups and companies in the space.

Kai-Fu Lee, a well-known trans-Pacific venture capitalist, tries to demystify and de-intensify the arms race story by carefully investigating what is really taking place in the AI labs and products from leading companies like Didi, Baidu, and Google. Less focused on fear than on analysis, Lee brings to bear his decades of experience on the subject to offer readers an in-depth, sober, and ultimately compelling look at how Chinese and American efforts around AI differ, and just how they can learn from each other. It was also our most recommended book by EC members, and I’ve also personally loved it (and discussed it a bit, although never truly got around to reviewing it – sorry!)

How to think about stories

Crafting Stories for Virtual Reality by Lakshmi Sarah and Melissa Bosworth

Routledge / 258 pages / October 2018

Publisher’s Link

McLuhan’s oft-repeated and often wrongly-interpreted “the medium is the message” is a key aspect of communications studies, but another angle is that the medium determines the types of stories that can be communicated. Books, radio, and television are platforms that offer storytellers certain tools and constraints, and over the decades (and for books, centuries), we have learned how to mold and optimize our visions to those intrinsic limits.

Virtual reality though is a whole new field, and as a medium, it is just getting started. How do we take advantage of the immersiveness intrinsic to VR? What are the new limits on storytelling, and what norms around plot and characters are going to have to be established to make this medium accessible to viewers?

Multimedia journalists Lakshmi Sarah and Melissa Bosworth wrote Crafting Stories for Virtual Reality as a primer for any storyteller that wants to learn more about how immersive media, augmented reality, and virtual reality are going to transform storytelling, reporting, and entertainment into the future.

As one EC reader wrote in their recommendation:

It provides a really good overview of different types of virtual reality and how they can be shaped to resonate with audiences in different ways. For anyone considering VR, it’s incredibly helpful.

It’s certainly early days, and books like this almost certainly have a short half-life. Nonetheless, for those looking to explore stories in VR, this is just the title to get up-to-speed on this small but rapidly growing segment of the tech industry.

The Overstory by Richard Powers

W. W. Norton & Company / 512 pages / April 2018

Publisher’s Link

Our EC readers are heavy on the non-fiction, but we did occasionally get some recommendations for fiction. One popular novel was Richard Powers’ The Overstory, which won this year’s Pulitzer Prize for Fiction. So, okay, clearly a critic favorite. But what makes the novel so unique and compelling in a year that had a serious number of great entries?

Similar to Foer above, Powers is concerned about how humans and climate change are coming together to devastate our natural environment and particularly, our trees and forests. The Overstory is really a multitude of stories of Americans who connect with nature and each other to start to take action to address the massive changes coming and already in progress.

This is the twelfth novel for Powers, who in addition to literature, has a background in physics and was formerly a computer programmer. His work on The Overstory was partly inspired by time he spent in Silicon Valley while at Stanford, where he observed California’s famed redwood trees. If you are looking for a thought-provoking novel, you don’t need to look too much farther.

How to help yourself in the tech world

The Making of a Manager: What to Do When Everyone Looks to You by Julie Zhuo

Portfolio / 288 pages / March 2019

Publisher’s Link

There is an archetypical story that happens at rapidly growing startups. A founder hires friends and people they know, builds a team, launches a product, and strikes gold. As growth continues unabated, more and more people are hired — forcing the startup to invent a management structure to bring some level of organization to the chaos. But managers are hard to find, and there are already employees with some level of tenure at the company. And so those early employees are often moved up rapidly to managerial and executive roles, suddenly handling direct reports with no experience whatsoever.

Julie Zhuo, VP of Design at Facebook, has written a guide for exactly these first-time, suddenly-promoted managers on exactly what they should be doing to begin bringing order to the chaos. This book is definitely in the self-help, management guru wing of the bookstore, but Zhuo’s personal experience going through this transformation shows through in her examples and clearly defined points for improvement.

One EC reader wrote in their recommendation:

One of the biggest org fallacies of fast-growing startups is promoting great ICs into first-time management roles and expecting they’ll quickly turn into great people managers with little training, mentorship, or role-models. Since that almost always works to plan, Julie, the first designer and later head of design at Facebook, wrote a book outlining all the challenges of being that first-time manager and the learnings along the way. A book I recommend to my team across the board.

The Making of a Manager targets a unique audience with unique insights and is worth a read.

Permission to Feel: Unlocking the Power of Emotions to Help Our Kids, Ourselves, and Our Society Thrive by Marc Brackett

Celadon Books / 304 pages / September 2019

Publisher’s Link

This was a surprising recommendation from the EC membership, but once I investigated it, completely understood why it fits on this list. One of the biggest challenges for children is their emotional development — how do they interact with the world and with other people? How do they listen to themselves and how they are feeling?

It’s not just children though, since all of us can improve how we respond to the daily stresses on our lives.

That’s why Marc Brackett, the founding director of the Yale Center for Emotional Intelligence, explores how we handle our emotions and why it is important to give and receive “permission to feel.” He offers an acronym (of course he does) called RULER to manage our emotional lives better:

  • Recognizing emotions in oneself and others.
  • Understanding the causes and consequences of emotion.
  • Labeling emotions with precise words.
  • Expressing emotions taking context and culture into consideration.
  • Regulating emotions effectively to achieve goals and wellbeing.

Considering that tech can often be one of the least emotionally hospitable industries out there, Brackett’s thoughts and solutions seem like a perfect fit for improving the quality and well-being of our workplaces and lives.

How to be an entrepreneur

Leonardo Da Vinci by Walter Isaacson

Simon & Schuster / 624 pages / October 2017

Publisher’s Link

Isaacson is probably best known today for his 2011 biography of Steve Jobs, but he has followed up that magnum opus with another dive into another entrepreneur, this time quintessential Renaissance man Leonardo Da Vinci. You’ve got all the typical Isaacson accoutrements here: the storyline, the characters, the life lessons, the inspiration. I don’t know how anyone can walk away from a book like this and not be deeply inspired by the power of a single human to change the world (or at least invent new ones!)

As one EC member wrote in their recommendation: “Curiosity and imagination lead to awareness and innovation. He perfected the art. A lesson for all.”

The book first came out two years ago with a paperback version coming out about a year ago, so perhaps noteworthy that our EC members still find deep value in this biography and its lessons.

Read more: https://techcrunch.com/2019/12/03/gift-guide-12-must-read-books-for-2019-as-recommended-by-extra-crunch-readers/

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Build a cognitive search and a health knowledge graph using AWS AI services

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Medical data is highly contextual and heavily multi-modal, in which each data silo is treated separately. To bridge different data, a knowledge graph-based approach integrates data across domains and helps represent the complex representation of scientific knowledge more naturally. For example, three components of major electronic health records (EHR) are diagnosis codes, primary notes, and specific medications. Because these are represented in different data silos, secondary use of these documents for accurately identifying patients with a specific observable trait is a crucial challenge. By connecting those different sources, subject matter experts have a richer pool of data to understand how different concepts such as diseases and symptoms interact with one another and help conduct their research. This ultimately helps healthcare and life sciences researchers and practitioners create better insights from the data for a variety of use cases, such as drug discovery and personalized treatments.

In this post, we use Amazon HealthLake to export EHR data in the Fast Healthcare Interoperability Resources (FHIR) data format. We then build a knowledge graph based on key entities extracted and harmonized from the medical data. Amazon HealthLake also extracts and transforms unstructured medical data, such as medical notes, so it can be searched and analyzed. Together with Amazon Kendra and Amazon Neptune, we allow domain experts to ask a natural language question, surface the results and relevant documents, and show connected key entities such as treatments, inferred ICD-10 codes, medications, and more across records and documents. This allows for easy analysis of co-occurrence of key entities, co-morbidities analysis, and patient cohort analysis in an integrated solution. Combining effective search capabilities and data mining through graph networks reduces time and cost for users to find relevant information around patients and improve knowledge serviceability surrounding EHRs. The code base for this post is available on the GitHub repo.

Solution overview

In this post, we use the output from Amazon HealthLake for two purposes.

First, we index EHRs into Amazon Kendra for semantic and accurate document ranking out of patient notes, which help improve physician efficiency identifying patient notes and compare it with other patients sharing similar characteristics. This shifts from using a lexical search to a semantic search that introduces context around the query, which results in better search output (see the following screenshot).

Second, we use Neptune to build knowledge graph applications for users to view metadata associated with patient notes in a more simple and normalized view, which allows us to highlight the important characteristics stemming from a document (see the following screenshot).

The following diagram illustrates our architecture.

The steps to implement the solution are as follows:

  1. Create and export Amazon HealthLake data.
  2. Extract patient visit notes and metadata.
  3. Load patient notes data into Amazon Kendra.
  4. Load the data into Neptune.
  5. Set up the backend and front end to run the web app.

Create and export Amazon HealthLake data

As a first step, create a data store using Amazon HealthLake either via the Amazon HealthLake console or the AWS Command Line Interface (AWS CLI). For this post, we focus on the AWS CLI approach.

  1. We use AWS Cloud9 to create a data store with the following code, replacing <<your data store name >> with a unique name:
aws healthlake create-fhir-datastore --region us-east-1 --datastore-type-version R4 --preload-data-config PreloadDataType="SYNTHEA" --datastore-name "<<your_data_store_name>>"

The preceding code uses a preloaded dataset from Synthea, which is supported in FHIR version R4, to explore how to use Amazon HealthLake output. Running the code produces a response similar to the following code, and this step takes a few minutes to complete (approximately 30 minutes at the time of writing):

{ "DatastoreEndpoint": "https://healthlake.us-east-1.amazonaws.com/datastore/<<your_data_store_id>>/r4/", "DatastoreArn": "arn:aws:healthlake:us-east-1:<<your_AWS_account_number>>:datastore/fhir/<<your_data_store_id>>", "DatastoreStatus": "CREATING", "DatastoreId": "<<your_data_store_id>>"
}

You can check the status of completion either on the Amazon HealthLake console or in the AWS Cloud9 environment.

  1. To check the status in AWS Cloud9, use the following code to check the status and wait until DatastoreStatus changes from CREATING to ACTIVE:
aws healthlake describe-fhir-datastore --datastore-id "<<your_data_store_id>>" --region us-east-1

  1. When the status changes to ACTIVE, get the role ARN from the HEALTHLAKE-KNOWLEDGE-ANALYZER-IAMROLE stack in AWS CloudFormation, associated with the physical ID AmazonHealthLake-Export-us-east-1-HealthDataAccessRole, and copy the ARN in the linked page.
  2. In AWS Cloud9, use the following code to export the data from Amazon HealthLake to the Amazon Simple Storage Service (Amazon S3) bucket generated from AWS Cloud Development Kit (AWS CDK) and note the job-id output:
aws healthlake start-fhir-export-job --output-data-config S3Uri="s3://hl-synthea-export-<<your_AWS_account_number>>/export-$(date +"%d-%m-%y")" --datastore-id <<your_data_store_id>> --data-access-role-arn arn:aws:iam::<<your_AWS_account_number>>:role/AmazonHealthLake-Export-us-east-1-HealthKnoMaDataAccessRole

  1. Verify that the export job is complete using the following code with the job-id obtained from the last code you ran. (when the export is complete, JobStatus in the output states COMPLETED):
aws healthlake describe-fhir-export-job --datastore-id <<your_data_store_id>> --job-id <<your_job_id>>

Extract patient visit notes and metadata

The next step involves decoding patient visits to obtain the raw texts. We will import the following file DocumentReference-0.ndjson (shown in the following screenshot of S3) from the Amazon HealthLake export step we previously completed into the CDK deployed Amazon SageMaker notebook instance. First, save the notebook provided from the Github repo into the SageMaker instance. Then, run the notebook to automatically locate and import the DocumentReference-0.ndjson files from S3.

For this step, use the resourced SageMaker to quickly run the notebook. The first part of the notebook creates a text file that contains notes from each patient’s visit and is saved to an Amazon S3 location. Because multiple visits could exist for a single patient, a unique identification combines the patient unique ID and the visit ID. These patients’ notes are used to perform semantic search against using Amazon Kendra.

The next step in the notebook involves creating triples based on the automatically extracted metadata. By creating and saving the metadata in an Amazon S3 location, an AWS Lambda function gets triggered to generate the triples surrounding the patient visit notes.

Load patient notes data into Amazon Kendra

The text files that are uploaded in the source path of the S3 bucket need to be crawled and indexed. For this post, a developer edition is created during the AWS CDK deployment, so the index is created to connect the raw patient notes.

  1. On the AWS CloudFormation console under the HEALTHLAKE-KNOWLEDGE-ANALYZER-CORE stack, search for kendra on the Resources tab and take note of the index ID and data source ID (copy the first part of the physical ID before the pipe ( | )).

  1. Back in AWS Cloud9, run the following command to synchronize the patient notes in Amazon S3 to Amazon Kendra:
aws kendra start-data-source-sync-job --id <<data_source_id_2nd_circle>> --index-id <<index_id_1st_ circle>>

  1. You can verify when the sync status is complete by running the following command:
aws kendra describe-data-source --id <<data_source_id_2nd_circle>> --index-id <<index_id_1st_circle>>

Because the ingested data is very small, it should immediately show that Status is ACTIVE upon running the preceding command.

Load the data into Neptune

In this next step, we access the Amazon Elastic Compute Cloud (Amazon EC2) instance that was spun up and load the triples from Amazon S3 into Neptune using the following code:

curl -X POST -H 'Content-Type: application/json' https://healthlake-knowledge-analyzer-vpc-and-neptune-neptunedbcluster.cluster-<<your_unique_id>>.us-east-1.neptune.amazonaws.com:8182/loader -d '
{ "source": "s3://<<your_Amazon_S3_bucket>>/stdized-data/neptune_triples/nquads/", "format": "nquads", "iamRoleArn": "arn:aws:iam::<<your_AWS_account_number>>:role/KNOWLEDGE-ANALYZER-IAMROLE-ServiceRole", "region": "us-east-1", "failOnError": "TRUE"
}'

Set up the backend and front end to run the web app

The preceding step should take a few seconds to complete. In the meantime, configure the EC2 instance to access the web app. Make sure to have both Python and Node installed in the instance.

  1. Run the following code in the terminal of the instance:
sudo iptables -t nat -I PREROUTING -p tcp --dport 80 -j REDIRECT --to-ports 3000

This routes the public address to the deployed app.

  1. Copy the two folders titled ka-webapp and ka-server-webapp and upload them to a folder named dev in the EC2 instance.
  2. For the front end, create a screen by running the following command:
screen -S back 

  1. In this screen, change the folder to ka-webapp and run npm install.
  2. After installation, go into the file .env.development and place the Amazon EC2 public IPv4 address and save the file.
  3. Run npm start and then detach the screen.
  4. For the backend, create another screen by entering:
screen -S back

  1. Change the folder to ka-server-webapp and run pip install -r requirements.txt.
  2. When the libraries are installed, enter the following code:
  1. Detach from the current screen, and using any browser, go the Amazon EC2 Public IPv4 address to access the web app.

Try searching for a patient diagnosis and choose a document link to visualize the knowledge graph of that document.

Next steps

In this post, we integrate data output from Amazon HealthLake into both a search and graph engine to semantically search relevant information and highlight important entities linked to documents. You can further expand this knowledge graph and link it to other ontologies such as MeSH and MedDRA.

Furthermore, this provides a foundation to further integrate other clinical datasets and expand this knowledge graph to build a data fabric. You can make queries on historical population data, chaining structured and language-based searches for cohort selection to correlate disease with patient outcome.

Clean up

To clean up your resources, complete the following steps:

  1. To delete the stacks created, enter the following commands in the order given to properly remove all resources:
$ cdk destroy HEALTHLAKE-KNOWLEDGE-ANALYZER-UPDATE-CORE
$ cdk destroy HEALTHLAKE-KNOWLEDGE-ANALYZER-WEBAPP
$ cdk destroy HEALTHLAKE-KNOWLEDGE-ANALYZER-CORE

  1. While the preceding commands are in progress, delete the Amazon Kendra data source that was created:
$ cdk destroy HEALTHLAKE-KNOWLEDGE-ANALYZER-VPC-AND-NEPTUNE
$ cdk destroy HEALTHLAKE-KNOWLEDGE-ANALYZER-IAMROLE
$ aws healthlake delete-fhir-datastore --datastore-id <<your_data_store_id>> 

  1. To verify it’s been deleted, check the status by running the following command:
$ aws healthlake describe-fhir-datastore --datastore-id "<<your_data_store_id>>" --region us-east-1

  1. Check the AWS CloudFormation console to ensure that all associated stacks starting with HEALTHLAKE-KNOWLEDGE-ANALYZER have all been deleted successfully.

Conclusion

Amazon HealthLake provides a managed service based on the FHIR standard to allow you to build health and clinical solutions. Connecting the output of Amazon HealthLake to Amazon Kendra and Neptune gives you the ability to build a cognitive search and a health knowledge graph to power your intelligent application.

Building on top of this approach can enable researchers and front-line physicians to easily search across clinical notes and research articles by simply typing their question into a web browser. Every clinical evidence is tagged, indexed, and structured using machine learning to provide evidence-based topics on things like transmission, risk factors, therapeutics, and incubation. This particular functionality is tremendously valuable for clinicians or scientists because it allows them to quickly ask a question to validate and advance their clinical decision support or research.

Try this out on your own! Deploy this solution using Amazon HealthLake in your AWS account by deploying the example on GitHub.


About the Authors

Prithiviraj Jothikumar, PhD, is a Data Scientist with AWS Professional Services, where he helps customers build solutions using machine learning. He enjoys watching movies and sports and spending time to meditate.

Phi Nguyen is a solutions architect at AWS helping customers with their cloud journey with a special focus on data lake, analytics, semantics technologies and machine learning. In his spare time, you can find him biking to work, coaching his son’s soccer team or enjoying nature walk with his fami

Parminder Bhatia is a science leader in the AWS Health AI, currently building deep learning algorithms for clinical domain at scale. His expertise is in machine learning and large scale text analysis techniques in low resource settings, especially in biomedical, life sciences and healthcare technologies. He enjoys playing soccer, water sports and traveling with his family.

Garin Kessler is a Senior Data Science Manager at Amazon Web Services, where he leads teams of data scientists and application architects to deliver bespoke machine learning applications for customers. Outside of AWS, he lectures on machine learning and neural language models at Georgetown. When not working, he enjoys listening to (and making) music of questionable quality with friends and family.

Dr. Taha Kass-Hout is Director of Machine Learning and Chief Medical Officer at Amazon Web Services, and leads our Health AI strategy and efforts, including Amazon Comprehend Medical and Amazon HealthLake. Taha is also working with teams at Amazon responsible for developing the science, technology, and scale for COVID-19 lab testing. A physician and bioinformatician, Taha served two terms under President Obama, including the first Chief Health Informatics officer at the FDA. During this time as a public servant, he pioneered the use of emerging technologies and cloud (CDC’s electronic disease surveillance), and established widely accessible global data sharing platforms, the openFDA, that enabled researchers and the public to search and analyze adverse event data, and precisionFDA (part of the Presidential Precision Medicine initiative).

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Source: https://aws.amazon.com/blogs/machine-learning/build-a-cognitive-search-and-a-health-knowledge-graph-using-amazon-healthlake-amazon-kendra-and-amazon-neptune/

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Huma, which uses AI and biomarkers to monitor patients and for medical research, raises $130M

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While much of the world eagerly watches to see if the vaccination rollout helps curb and eventually stamp out Covid-19, one of the companies that has been helping to manage the spread of the virus is announcing a big round of funding on the heels for strong demand for its technology.

Huma, which combines data from biomarkers with predictive algorithms both to help monitor patients, and uses the same technology to help researchers and pharmaceutical companies run clinical trials, has closed an equity round of $130 million, a Series C that the company can extend to $200 million by way of a $70 million debt line if it chooses.

Huma can pick up data that patients contribute via smartphones, or by way of diagnostic devices that measure glucose, blood pressure or oxygen saturation, and the plan will be to use the funding to augment that in a couple of ways: to continue investing in R&D to both expand the kinds of biomarkers that Huma can measure and to work on more research and trials; to continue expanding London-based Huma’s business particularly in newer geographies like the US, alongside a strong wave of business it’s been seeing in Europe, specifically the UK and the DACH region.

The funding includes a number of high-profile strategic and financial backers that speak to some of the opportunities coming down the pike. Co-led by Leaps by Bayer, the VC division of the pharmaceutical and life sciences giant, and Hitachi Ventures, it also includes Samsung Next, Sony Innovation Fund by IGV (one of Sony’s investment funds), Unilever Ventures and HAT Technology & Innovation Fund, Nikesh Arora (the former president of SoftBank and ex-Google exec) and Michael Diekmann (Chairman of Allianz) all in the round. Bayer also led Huma’s $25 million Series B in 2019, when the startup was still called Medopad.

Medopad rebranded to Huma last year in April, just as the Covid-19 pandemic was really taking hold across the world. In the year since, CEO and founder Dan Vahdat said that the company has been on a growth tear, working hard across the spectrum of areas where its technology could prove useful, since it provides a bridge to monitoring patients remotely, at a time when it’s been significantly more challenging to see people in person.

“Last year when the pandemic first hit, it made everyone’s lives miserable not just from the health aspect but also research aspect,” he said. “The whole idea is how to decentralize care and research.”

Its work has included partnering with the NHS early on to ship some 1 million oxygen saturation devices to monitor how patients’ levels were faring, since that was early on discovered to be a leading indicator of whether a patient would need urgent medical care: this was essential way to triage people remotely at a time when hospitals were quickly getting overwhelmed with people. Vahdat said this directly helped reduce readmissions by one-third.

It is also playing a role in helping to monitor all the many patients who had been due to have operations but found those postponed. In the UK alone, there were 4.8 million people waiting as a result for their procedures, “a shocking number,” Vahdat said. How to handle that queue? The idea here, he said, is that when you are a patient at home waiting for cardiac surgery, your condition might deteriorate quickly. Or it may not. Huma set up a system to provide diagnostics for those patients to monitor how they were doing: signs that they were not doing well meant they would get moved up and brought in to be seen by a specialist before they deteriorated and became urgent rather than managed cases.

Alongside this clinical work, Huma has also been working on a number of trials and research, including a phase 4 study on one of the Covid-19 vaccines that has been getting distributed under emergency authorization (this is a regulatory process that comes in the wake of that authorization).

It’s also been continuing to contribute essential data to ongoing medical research. One that the company can disclose that is not directly related to Covid-19 is a heart study for Bayer; and one that is related to Covid-19 — finding better biomarkers (specifically in looking at digital phenotypes) to detect Covid-19 infections earlier — called the Cambridge Fenland study.

This long list of work has meant that Huma still has much of its Series B in the bank, and so it’s also been turning its attention to humanitarian work, donating resources to India and other countries still in the throes of their own Covid-19 crises.

Although startups that bridge the worlds of medicine and technology can be very long plays, the last year has shown not just how vital it is to invest in the smartest of these to see out their ambitions for the greater good of all of us, but that, when they do have their breakthroughs, it can prove to be a huge thing for the companies and investors. BioNTech’s last year has been nothing short of a stratospheric turnaround, going from a loss-making business to one producing more than $1 billion in profit in the last quarter on the back of its Covid-19 vaccine research and work with Pfizer.

It’s for that reason that so many investors are keen to continue supporting the likes of Huma and the insights it provides.

“Aligned with the vision of Leaps by Bayer, Huma’s expertise and technology will help drive a global paradigm shift towards prevention and care and may boost research efforts using data and digital technology,” said Juergen Eckhardt, Head of Leaps by Bayer, in a statement. “We invest into the most disruptive technologies of our time that have the potential to change the world for the better. As an early investor into Huma we know how perfectly the company fits into that frame as one of the leading digital innovators in healthcare and life sciences.”

“Huma has built a comprehensive remote patient monitoring platform and established a strong track-record and we are excited to be working with Huma to bring its world-leading health technology to new markets in Asia. We believe that together we can advance new digital health products to power better care and research for all,” added Keiji Kojima, EVP of Hitachi’s Smart Life division.

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Source: https://techcrunch.com/2021/05/11/huma-which-uses-ai-and-biomarkers-to-monitor-patients-and-for-medical-research-raises-130m/

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Elderly caretech platform Birdie gets $11.5M Series A led by Index

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SaaS-maker Birdie has closed an $11.5 million Series A round of funding led by Index Ventures. Existing investor Kamet Ventures also participated.

The UK-based caretech startup has raised a total of $22.9M since being founded back in 2017 (a 2018 raise that was called a Series A at the time is now being classed as a seed expansion). It’s focused on building tools for social care providers to drive efficiencies in a chronically under resourced sector.

Birdie isn’t a care provider itself (so it’s not a direct competitor to a startup like Lifted); rather it aims to support care providers with a suite of digital tools intended to reduce admin costs and makes it easier to manage the care being provided to individuals — doing away with the need for paper-based records, and enabling real-time visibility such as via carer check-ins and medication-related notifications.

The wider mission is for the platform to support care providers to offer more co-ordinated, personalized and — the hope is — preventative care so that older adults can be supported to live for longer in their own homes.

“Technology can completely transform the way we look after the elderly and help them to age at home much longer, healthier and happier,” says CEO and co-founder Max Parmentier, explaining the founding premise. “We position ourselves as a solution to uniquely offer a full support for the elderly to age at home… So we started off with the people closest to the elderly and caring for the elderly which are the care providers. And when we look at how these providers are operating they are extraordinary committed, and very much involved in their work, but the care delivered is very uncoordinated, reactive and sometimes very generic.

“We felt that we could go way beyond — in terms of technology — becoming the operating system to be much more efficient in the way they deliver care but also to significantly increase the quality of the care delivered.”

What’s the draw for VCs to invest in such an under-resourced market? “There’s macro trends which are unavoidable. I agree with you that it’s vastly underfunded but it’s just unsustainable,” he argues. “There is clearly an argument to say that whether VCs or investors are interested in this industry or not it’s going to get bigger. And one way or another we’ll have to find some funding mechanism to pay for it.”

“Today already we hear horrible stories about older people not being taken care of properly. I think what got particular Index excited is really the opportunity to [tell a positive story],” he goes on. “I’m quite an optimistic person. I do believe that actually you could very much craft a much happier path in terms of ageing which is actually more affordable — because it doesn’t cost as much because you really lower the healthcare costs if you really tailor these packages better and tailor the care much better. And you can also use technology to make it more personalized, more preventative.”

By simplifying and streamlining data capture around elderly care via a digital platform, information about the care being delivered can be structured in a way that helps reduce errors (such as from handwritten notes leading to administering the wrong medication) and allows for problems to be spotted early when an intervention may be highly beneficial, is the contention.

Parmentier gives the example of early signs of a urinary tract infection which, if picked up on — by spotting telltale signs in the data — can be treated simply at home with antibiotics. But if not an elderly person may end up in hospital, with all the associated risks of a far worse outcome.

Birdie can also supply connected hardware like motion sensors to its care provider customers so that its platform can monitor frail elderly adults who may be at risk of falling. Although Parmentier emphasizes that such hardware is an optional component of the platform — and is only installed with the full knowledge and consent of the care recipient.

The business is focused on “serving the interests and the rights of these older adults and no one else”, he says, confirming that care recipients’ data is not shared with any third parties unless it’s directly related to the delivery of their care.

Birdie’s team (Image credits: Birdie)

Having a digital platform-level view into an individual’s care obviously offers increased visibility vs paper-based records. It also means real-time data can be shared — such as with close family members who may want the reassurance of knowing when their loved one has received a visit or taken their medication, and so on. (Again, though, only with the proper consents.)

“There is a positive narrative which is that ageing is actually great,” Parmentier suggests. “If you’re in good health this part of your life is probably one of the most exciting. And this is really the spin we should give in terms of story but also we should empower these older adults with the right support to take that happy path.”

To date, Birdie has partnered with almost 500 providers across the U.K. — and currently its platform is being used to support the care of more than 20,000 older people every week.

Growth has been 8x over the past 12 months, per Parmentier, as the coronavirus pandemic has accelerated demand for in-home elderly care. The new funding will go on accelerating growth in the U.K., though he also says it has its eye on other geographies and sees potential to expand internationally.

“Phase one [of the business] is how can we empower these care providers to be better at what they do?” he says. “Because I really believe that there’s am army of care givers who are so committed and if we can help them be better at what they do that’s beautiful.”

Having structured data on elderly care provides a foundation for conducting research that could further the ‘preventative’ care component of the mission — and Birdie is taking some tentative steps in that direction via some project partnerships.

Such as one into polypharmacy (i.e. concurrent use of medications which can have negative clinical consequences) with U.K.-based AI company Faculty.

“There’s very little known as to what impact medication has on older adults health. If you think about it we just have pharma companies doing trials and then flagging secondary symptoms up when they arise and then doctors prescribe that. The reality is for elderly people — because usually they combine different medications — the symptoms and the damage to health can be greater,” he explains.

“What we’ve done with Faculty is to look at what is the medication treatment of an older adult and what is the clinical observations from carers following these medication treatments. So do we see that typically there’s less appetite to eat or drink, or complaints about pains and so on. And do we see correlations with the actual medication treatment prescribed?”

The polypharmacy research is at an early stage but he says the hope is they will be able to build an AI model that can generate warnings for a prescribing clinician if a particular medication regime has been linked to outcomes that may damage health or otherwise hamper healthy caring for an individual.

On the research side, Birdie’s website notes that it’s using “anonymized” data in these exploratory efforts — which is a claim that merits scrutiny given that medical data is both very sensitive and notoriously difficult to robustly (irreversibly) anonymize.

Asked about this, Parmentier says that for the moment its research efforts entail correlating data on different older adults from different care providers, and that the data being pooled is limited to specifically relevant info (i.e. depending on the research project) — removing “all the un-needed data”, as he puts it. 

He says it is not, for example, currently combining any of the data it holds with National Health Service (NHS) patient data — which he acknowledges could pose a major risk of re-identification. But he also says Birdie does want to go there because it believes that combining more data-sets could help it further preventative care research.

“The risk is when you pool your data with any third party data-set such as the NHS for instance. That is really risky… because there’s always a way to tie it back. So we’ve been keeping away from that for the moment,” he tells TechCrunch.

“I think it can really improve our preventative models but we need to do that only under very strict conditions that the anonymization is bullet-proof,” he adds. “We haven’t done that yet and we’re exploring ways to do it. But we’re going to very cautious about it. So for the moment there’s no risk really because we’re not mixing data-sets of the same patient. But if we were to integrate with third parties’ systems the risk will rise — and we’ll need to address it very clearly.”

Parmentier also offers a glimpse of an ambitious potential second phase of the business — where Birdie believes it will be able to coach older adults themselves (and/or their family members who are acting as care givers), i.e. enabled by its platform-level view of best practice (and by being able to fold in data-fuelled research into preventative care AI models).

To get there will require not, just a lot of data, but a sectoral shift toward a model of care delivery focused on “value-based healthcare”; where the provider is billed not for hours of care given but on health/quality of life outcomes. So the transformative vision of highly scalable, data-enabled elderly home care is certainly not going to arrive overnight.

In the meanwhile Birdie’s business remains firmly in phase one: Building support tools to drive efficiency and quality for an under-resourced sector.

“We see the same problem everywhere,” adds Parmentier. “Today already we don’t look after our elderly properly… Today they cost us about 60% of our healthcare costs. Tomorrow is going to be much worse. We need to channel more investment into this industry — in terms of new ways of operating, technology, and really innovation is key to move towards better models where it’s more preventative, more personalized, more outcome based — because that’s the solution. It’s going to lower the cost base, it’s going to improve the health outcomes.”

Commenting in a statement, Stephane Kurgan, venture partner at Index Ventures, added: “Our ageing society and increasing healthcare costs require us to rethink the way we care for frailer populations like the elderly. Technology gives us the tools, as the care sector has remained widely paper-based and is ripe for disruption.

“By investing in caretech with Birdie, we are investing in solving the daily challenges of the care community. We firmly believe in Birdie’s vision to make care more personalised and more preventative so that older people can age at home longer, healthier and happier. We’ve been impressed by Birdie’s traction and the calibre of its team, and are very excited to embark on this journey with them.”

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Source: https://techcrunch.com/2021/05/11/elderly-caretech-platform-birdie-gets-11-5m-series-a-led-by-index/

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Improve the streaming transcription experience with Amazon Transcribe partial results stabilization

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Whether you’re watching a live broadcast of your favorite soccer team, having a video chat with a vendor, or calling your bank about a loan payment, streaming speech content is everywhere. You can apply a streaming transcription service to generate subtitles for content understanding and accessibility, to create metadata to enable search, or to extract insights for call analytics. These transcription services process streaming audio content and generate partial transcription results until it provides a final transcription for a segment of continuous speech. However, some words or phrases in these partial results might change, as the service further understands the context of the audio.

We’re happy to announce that Amazon Transcribe now allows you to enable and configure partial results stabilization for streaming audio transcriptions. Amazon Transcribe is an automatic speech recognition (ASR) service that enables developers to add real-time speech-to-text capabilities into their applications for on-demand and streaming content. Instead of waiting for an entire sentence to be transcribed, you can now control the stabilization level of partial results. Transcribe offers 3 settings: High, Medium and Low. Setting the stabilization “High” allows a greater portion of the partial results to be fixed with only the last few words changing during the transcription process. This feature helps you have more flexibility in your streaming transcription workflows based on the user experience you want to create.

In this post, we walk through the benefits of this feature and how to enable it via the Amazon Transcribe console or the API.

How partial results stabilization works

Let’s dive deeper into this with an example.

During your daily conversations, you may think you hear a certain word or phrase, but later realize that it was incorrect based on additional context. Let’s say you were talking to someone about food, and you heard them say “Tonight, I will eat a pear…” However, when the speaker finishes, you realize they actually said “Tonight I will eat a pair of pancakes.” Just as humans may change our understanding based on the information at hand, Amazon Transcribe uses machine learning (ML) to self-correct the transcription of streaming audio based on the context it receives. To enable this, Amazon Transcribe uses partial results.

During the streaming transcription process, Amazon Transcribe outputs chunks of the results with an isPartial flag. Results with this flag marked as true are the ones that Amazon Transcribe may change in the future depending on the additional context received. After Amazon Transcribe classifies that it has sufficient context to be over a certain confidence threshold, the results are stabilized and the isPartial flag for that specific partial result is marked false. The window size of these partial results could range from a few words to multiple sentences depending on the stream context.

The following image displays how the partial results are generated (and edited) in Amazon Transcribe for streaming transcription.

Results stabilization enables more control over the latency and accuracy of transcription results. Depending on the use case, you may prioritize one over the other. For example, when providing live subtitles, high stabilization of results may be preferred because speed is more important than accuracy. On the other hand for use cases like content moderation, lower stabilization is preferred because accuracy may be more important than latency.

A high stability level enables quicker stabilization of transcription results by limiting the window of context for stabilizing results, but can lead to lower overall accuracy. On the other hand, a low stability level leads to more accurate transcription results, but the partial transcription results are more likely to change.

With the streaming transcription API, you can now control the stability of the partial results in your transcription stream.

Now let’s look at how to use the feature.

Access partial results stabilization via the Amazon Transcribe console

To start using partial results stabilization on the Amazon Transcribe console, complete the following steps:

  1. On the Amazon Transcribe console, make sure you’re in a Region that supports Amazon Transcribe Streaming.

For this post, we use us-east-1.

  1. In the navigation pane, choose Real-time transcription.
  2. Under Additional settings, enable Partial results stabilization.

  1. Select your stability level.

You can choose between three levels:

  • High – Provides the most stable partial transcription results with lower accuracy compared to Medium and Low settings. Results are less likely to change as additional context is gathered.
  • Medium – Provides partial transcription results that have a balance between stability and accuracy
  • Low – Provides relatively less stable partial transcription results with higher accuracy compared to High and Medium settings. Results get updated as additional context is gathered and utilized.

  1. Choose Start streaming to play a stream and check the results.

Access partial results stabilization via the API

In this section, we demonstrate streaming with HTTP/2. You can enable your preferred level of partial results stabilization in an API request.

You enable this feature via the enable-partial-results-stabilization flag and the partial-results-stability level input parameters:

POST /stream-transcription HTTP/2 x-amzn-transcribe-language-code: LanguageCode x-amzn-transcribe-sample-rate: MediaSampleRateHertz x-amzn-transcribe-media-encoding: MediaEncoding x-amzn-transcribe-session-id: SessionId x-amzn-transcribe-enable-partial-results-stabilization= true
x-amzn-transcribe-partial-results-stability = low | medium | high

Enabling partial results stabilization introduces the additional parameter flag Stable in the API response at the item level in the transcription results. If a partial results item in the streaming transcription result has the Stable flag marked as true, the corresponding item transcription in the partial results doesn’t change irrespective of any subsequent context identified by Amazon Transcribe. If the Stable flag is marked as false, there is still a chance that the corresponding item may change in the future, until the IsPartial flag is marked as false.

The following code shows our API response:

{ "Alternatives": [ { "Items": [ { "Confidence": 0, "Content": "Amazon", "EndTime": 1.22, "Stable": true, "StartTime": 0.78, "Type": "pronunciation", "VocabularyFilterMatch": false }, { "Confidence": 0, "Content": "is", "EndTime": 1.63, "Stable": true, "StartTime": 1.46, "Type": "pronunciation", "VocabularyFilterMatch": false }, { "Confidence": 0, "Content": "the", "EndTime": 1.76, "Stable": true, "StartTime": 1.64, "Type": "pronunciation", "VocabularyFilterMatch": false }, { "Confidence": 0, "Content": "largest", "EndTime": 2.31, "Stable": true, "StartTime": 1.77, "Type": "pronunciation", "VocabularyFilterMatch": false }, { "Confidence": 1, "Content": "rainforest", "EndTime": 3.34, "Stable": true, "StartTime": 2.4, "Type": "pronunciation", "VocabularyFilterMatch": false }, ], "Transcript": "Amazon is the largest rainforest " } ], "EndTime": 4.33, "IsPartial": false, "ResultId": "f4b5d4dd-b685-4736-b883-795dc3f7f636", "StartTime": 0.78
}

Conclusion

This post introduces the recently launched partial results stabilization feature in Amazon Transcribe. For more information, see the Amazon Transcribe Partial results stabilization documentation.

To learn more about the Amazon Transcribe Streaming Transcription API, check out Using Amazon Transcribe streaming With HTTP/2 and Using Amazon Transcribe streaming with WebSockets.


About the Author

Alex Chirayath is an SDE in the Amazon Machine Learning Solutions Lab. He helps customers adopt AWS AI services by building solutions to address common business problems.

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Source: https://aws.amazon.com/blogs/machine-learning/amazon-transcribe-now-supports-partial-results-stabilization-for-streaming-audio/

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