Nice, especially with regards to how machine learning links to its own devices. I am noting Amazon Comprehend.
Amazon Opens Its Internal Machine Learning Courses to All for Free
in TechCrunch By Connie Loizos
Amazon is opening the internal machine learning courses it uses to train its own engineers for the first time to people outside the company, for free. According to the company's Matt Wood, Amazon has made available 30 different courses comprising more than 45 hours of training for developers, data scientists, data platform engineers, and business professionals. Wood said every course "starts with the fundamentals, and builds on those through real-world examples and labs, allowing developers to explore machine learning through some fun problems we have had to solve at Amazon. These include predicting gift wrapping eligibility, optimizing delivery routes, or predicting entertainment award nominations using data from IMDB (the Internet Movie Database)." Wood also said the coursework helps streamline best practices, and shows trainees how to begin work on a variety of Amazon Web Services (AWS) machine learning services, such as Amazon SageMaker, AWS DeepLens, Amazon Rekognition, Amazon Lex, Amazon Polly, and Amazon Comprehend. ... "
Link to Courses.
Showing posts with label Amazon Comprehend Medical. Show all posts
Showing posts with label Amazon Comprehend Medical. Show all posts
Friday, November 30, 2018
(Updated) Amazon Extracting Information from Unstructured Medical Text
Announced at Amazon Invent this week. This was just pointed out to me. (Via Walter Riker) I had heard of this being used in conjunction with assistant style interfaces, How it might be accurate enough to purpose is unclear. Worth a look.
Amazon Comprehend Medical
Extract information from unstructured medical text accurately and quickly
No machine learning experience required
Amazon Comprehend Medical is a natural language processing service that makes it easy to use machine learning to extract relevant medical information from unstructured text. Using Amazon Comprehend Medical, you can quickly and accurately gather information, such as medical condition, medication, dosage, strength, and frequency from a variety of sources like doctors’ notes, clinical trial reports, and patient health records.
One of the important ways to improve patient care and accelerate clinical research is by understanding and analyzing the insights and relationships that are “trapped” in free-form medical text, including hospital admission notes and a patient’s medical history.
Today this is achieved by writing and maintaining a set of customized rules for natural language processing software, which are complicated to build, time-consuming to maintain, and fragile. A change to a single classification code name, for example, can impact dozens of hard-coded rules and failing to update a single one of them can result in missed or incorrect data. Machine learning can change all that with models that can reliably understand the medical information in unstructured text, identify meaningful relationships, and improve over-time. ... "
(Update) Another piece on the same system:
Amazon launches patient data-mining service to assist docs
Through its Amazon Web Services platform, Amazon is offering an A.I. engine that can cull useful information from millions of unstructured electronic files, including patient electronic medical records.
By Lucas Mearian Senior Reporter, Computerworld
See also: Amazon has now opened its internal training for this and other AWS AI systems to all for free, examining. See: https://eponymouspickle.blogspot.com/2018/11/amazon-opens-it-internal-machine.html
Amazon Comprehend Medical
Extract information from unstructured medical text accurately and quickly
No machine learning experience required
Amazon Comprehend Medical is a natural language processing service that makes it easy to use machine learning to extract relevant medical information from unstructured text. Using Amazon Comprehend Medical, you can quickly and accurately gather information, such as medical condition, medication, dosage, strength, and frequency from a variety of sources like doctors’ notes, clinical trial reports, and patient health records.
One of the important ways to improve patient care and accelerate clinical research is by understanding and analyzing the insights and relationships that are “trapped” in free-form medical text, including hospital admission notes and a patient’s medical history.
Today this is achieved by writing and maintaining a set of customized rules for natural language processing software, which are complicated to build, time-consuming to maintain, and fragile. A change to a single classification code name, for example, can impact dozens of hard-coded rules and failing to update a single one of them can result in missed or incorrect data. Machine learning can change all that with models that can reliably understand the medical information in unstructured text, identify meaningful relationships, and improve over-time. ... "
(Update) Another piece on the same system:
Amazon launches patient data-mining service to assist docs
Through its Amazon Web Services platform, Amazon is offering an A.I. engine that can cull useful information from millions of unstructured electronic files, including patient electronic medical records.
By Lucas Mearian Senior Reporter, Computerworld
See also: Amazon has now opened its internal training for this and other AWS AI systems to all for free, examining. See: https://eponymouspickle.blogspot.com/2018/11/amazon-opens-it-internal-machine.html
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