How New Tools In Data And AI Are Being Used In Healthcare And Medicine

How New Tools In Data And AI Are Being Used In Healthcare And Medicine

Publication date: Sep 03, 2019

Many startups are using modern data and AI technologies to tackle problems related to workflow optimization and automation, demand forecasting, treatment and care, diagnostics, drug discovery, personalized medicine, and many other areas.

We will focus on recent progress in foundational topics that the healthcare and medical community care about: access to high-quality labeled data, developing ML models that cut across organizations, and data markets and networks.

Medical data comes in many forms: images, audio, unstructured text, and occasionally even structured data.

Regardless of the form, the problems that affect medical data aren’t fundamentally different from the problems in other industries: missing data, corrupt values, suspicious outliers, typographic errors, lack of labels, and more.

Researchers also have used Holoclean on data sets related to healthcare (hospital dataset used in data cleaning benchmarks) and public health (food inspection dataset).

As more and more medical databases become available, labelling the data to train machine learning models become ever more critical.

This is true for repositories composed of medical imaging data, genomics, and other data types.

Data is often labelled when it is created (for example, medical images labelled with a patient ID and a diagnosis); services like Mechanical Turk are often used to classify unlabeled data.

GE has claimed that most medical data never gets analyzed–but this data still may be useful for training if it can be properly tagged.

In addition to Holoclean, Christopher RcE9 and his collaborators have released an open source data programming tool called Snorkel.

Snorkel automates the work of creating training data sets by labelling data programmatically, and then using machine learning to classify and even transform images: In a nutshell, data programming techniques provide ways to -manufacture” data that we can feed to various learning and predictions tasks (even for ML data quality solutions).

The rise in tools for data programming occurs at a time when other researchers are exploring -small sample learning” (machine learning tools that rely on smaller amounts of labeled data) for biomedical image analysis.

Besides labelling data for use in machine learning, data programming can be used to extract knowledge and information buried within existing data sources.

Snorkel has been used to create training data for a machine reading system that automatically collects and synthesizes genetic associations and makes them available in a structured database.

At O’Reilly’s Artificial Intelligence conference in Beijing, Ion Stoica, director of UC Berkeley’s RISELab, described new projects that allowed organizations to cooperate without actually sharing data as coopetitive learning.

Concepts Keywords
AI Organizations markets networks
Algorithm Huge healthcare
Analytic Kinds applications
Android Millions healthcare
Aortic Valve Investments healthcare
Apple Watch Individual systems
Artificial Intelligence Fancy algorithms
Automation Deep learning algorithms
Beijing Imaging
Biomedical Kinds intelligent systems
Business Intelligence Android
Census Deep learning algorithms
Computer Science Machine learning
Coopetition Artificial intelligence
Coopetitive Training algorithms
Correlation Cybernetics
Cryptography Articles
Deep Learning Learning
Democratization Cognition
Diabetic Emerging technologies
Drug Discovery Academic disciplines
Economics Machine learning
Engineering Federated learning
Ephemeral Artificial intelligence
Error Detection Deep learning
Financial Services Genomics
Fraud Cryptography
Garbage Drug discovery
Genetic Business intelligence
Genome MRI
Genomics
Healthcare
Heart Attack
Hospital
Image Analysis
Imagine
Intel
Interact
Jordan
Linear Regression
Logistics
London
Lorica
Lung
Machine Learning
Mechanical Turk
Medicine
Michael Jordan
Microeconomics
Missing Data
MRI
Open Source
Optimization
Outliers
Patent
Personalized Medicine
Pneumonia
Privacy
Provenance
Radiologists
Radiology
Recommendation Engine
Recording
Regulatory Compliance
San Jose
Social Media
Stroke
TensorFlow
Unstructured Text
Vice President
WIPO
Workflow
X Rays

Semantics

Type Source Name
drug DRUGBANK Methyltestosterone
disease MESH privacy
gene UNIPROT LAT2
gene UNIPROT EAF2
gene UNIPROT CYREN
disease MESH malformations
disease MESH pneumonia
disease DOID pneumonia
gene UNIPROT PIMREG
gene UNIPROT SET
gene UNIPROT PTPN5
gene UNIPROT LARGE1
disease MESH diagnosis
disease DOID cancer
disease MESH cancer
gene UNIPROT KCNK3
drug DRUGBANK Tropicamide
gene UNIPROT SLC35G1
gene UNIPROT DESI1
disease DOID stroke
disease DOID heart attack
disease MESH stroke
disease MESH heart attack
drug DRUGBANK Etoperidone
gene UNIPROT FICD
disease MESH community
gene UNIPROT FANCE
gene UNIPROT ELOVL6
disease DOID face
gene UNIPROT IMPACT

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