Machine Learning in Modern Medicine with Erin LeDell at Stanford Med
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Transcript of Machine Learning in Modern Medicine with Erin LeDell at Stanford Med
H2O.ai Machine Intelligence
H2O.ai
H2O Company
H2O Software
• Team: 35. Founded in 2012, Mountain View, CA• Stanford Math & Systems Engineers
• Open Source Software • Ease of Use via Web Interface• R, Python, Scala, Spark & Hadoop Interfaces• Distributed Algorithms Scale to Big Data
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Scientific Advisory CouncilDr. Trevor Hastie
Dr. Rob Tibshirani
Dr. Stephen Boyd
• John A. Overdeck Professor of Mathematics, Stanford University• PhD in Statistics, Stanford University• Co-author, The Elements of Statistical Learning: Prediction, Inference and Data Mining• Co-author with John Chambers, Statistical Models in S• Co-author, Generalized Additive Models • 108,404 citations (via Google Scholar)
• Professor of Statistics and Health Research and Policy, Stanford University• PhD in Statistics, Stanford University• COPPS Presidents’ Award recipient• Co-author, The Elements of Statistical Learning: Prediction, Inference and Data Mining• Author, Regression Shrinkage and Selection via the Lasso• Co-author, An Introduction to the Bootstrap
• Professor of Electrical Engineering and Computer Science, Stanford University• PhD in Electrical Engineering and Computer Science, UC Berkeley• Co-author, Convex Optimization• Co-author, Linear Matrix Inequalities in System and Control Theory• Co-author, Distributed Optimization and Statistical Learning via the Alternating Direction
Method of Multipliers
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Agenda• Motivation• An Intro to Machine Learning• Machine Learning in Medicine• H2O Machine Learning• Live Software Demo
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Data-Driven
Preventative Care
Precision Medicine
• “The Future of Medicine is Precision Health” • President Obama’s Precision Medicine Initiative• Similar efforts at Stanford, UC Berkeley, UCSF
Personalized
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Genomic Data
Genomics pioneer, Jun Wang, is one of China’s most famous scientists.
Dr. Wang recently stepped down as head of BGI to create an AI health-monitoring system that would identify relationships between individual human genomic data, physiological traits (phenotypes) and lifestyle choices in order to provide advice on healthier living and to predict, and prevent, disease.
Source: BGI
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Wearables
ABI Research has projected that by 2016, wearable wireless medical device sales will reach more than 100 million devices annually.
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Open mHealth
• FitBit• HealthVault• Jawbone• RunKeeper• Withings• HealthKit• HL7 EHR
Open mHealth is a nonprofit start-up breaking down the barriers to integration and bringing clinical meaning to digital health data.
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What is Machine Learning?
What it is: ✤ “Field of study that gives computers the ability to learn without being explicitly programmed.” (Samuel, 1959)
✤ “Machine learning and statistics are closely related fields. The ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.” (Jordan, 2014)
✤ M.I. Jordan also suggested the term data science as a placeholder to call the overall field.
Unlike rules-based systems which require a human expert to hard-code domain knowledge directly into the system, a machine learning algorithm learns how to make decisions from the data alone.
What it’s not:
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Classification
Clustering
Machine Learning Overview
• Predict a real-valued response (viral load, weight)• Gaussian, Gamma, Poisson and Tweedie • MSE and R^2
• Multi-class or Binary classification• Ranking• Accuracy and AUC
• Unsupervised learning (no training labels)• Partition the data / identify clusters• AIC and BIC
Regression
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Machine Learning Workflow
Source: NLTK
Example of a supervised machine learning workflow.
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ML Model Performance
Test & Train • Partition the original data (randomly) into a training set and a test set. (e.g. 70/30)
• Train a model using the “training set” and evaluate performance on the “test set” or “validation set.”
• Train & test K models as shown.
• Average the model performance over the K test sets.
• Report cross-validated metrics.
• Regression: R^2, MSE, RMSE• Classification: Accuracy, F1, H-measure• Ranking (Binary Outcome): AUC, Partial AUC
K-foldCross-validation
Performance Metrics
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What is Deep Learning?
What it is: ✤ “A branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures, composed of multiple non-linear transformations.” (Wikipedia, 2015)
✤ Deep neural networks have more than one hidden layer in their architecture. That’s what’s “deep.”
✤ Very useful for complex input data such as images, video, audio.
Deep learning architectures, specifically artificial neural networks (ANNs) have been around since 1980, so they are not new. However, there were breakthroughs in training techniques that lead to their recent resurgence (mid 2000’s). Combined with modern computing power, they are quite effective.
What it’s not:
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What is Ensemble Learning?
What it is: ✤ “Ensemble methods use multiple learning algorithms to obtain better predictive performance that could be obtained from any of the constituent learning algorithms.” (Wikipedia, 2015)
✤ Random Forests and Gradient Boosting Machines (GBM) are both ensembles of decision trees.
✤ Stacking, or Super Learning, is technique for combining various learners into a single, powerful learner using a second-level metalearning algorithm.
Ensembles typically achieve superior model performance over singular methods. However, this comes at a price — computation time.
What it’s not:
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What is Data Science?
Problem Formulation
• Identify an outcome of interest and the type of task: classification / regression / clustering
• Identify the potential predictor variables• Identify the independent sampling units
• Conduct research experiment (e.g. Clinical Trial)• Collect examples / randomly sample the population• Transform, clean, impute, filter, aggregate data• Prepare the data for machine learning — X, Y
• Modeling using a machine learning algorithm (training)• Model evaluation and comparison• Sensitivity & Cost Analysis
• Translate results into action items• Feed results into research pipeline
Collect & Process Data
Machine Learning
Insights & Action
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Machine Learning in Medicine
• Predict Viral Failure in AIDS patients• Parkinson’s disease progression prediction from
mobile phone accelerometer data• Personalized diagnostics
• Clinical research: Identify which genes are associated with breast cancer relapse
• Prognosis: Predict probability of survival in 5 years
• Real-time predictions using data from wearables• Medication adherence monitoring
• Clinical research: MRI and PET scans & Deep Learning
• Cellular image analysis: genotype, phenotype, classification, identification, cell tracking
DiagnosticTesting
Oncology
MedicalImaging
Remote PatientMonitoring
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Predict Viral Failure
Problem Formulation
• Outcome of interest: Viral failure in HIV patients (0/1)• Machine Learning task: Ranking (measured by AUC)• Sampling units: Multiple viral load tests per patient —
pooled repeated measures data
Collect & Process Data
Machine Learning
Insights & Action
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Predict Viral Failure
Problem Formulation
• Outcome of interest: Viral failure in HIV patients (0/1)• Machine Learning task: Ranking (measured by AUC)• Sampling units: Multiple viral load tests per patient —
pooled repeated measures data
• MEMS data collected from electronic pill boxes• Two cohorts: Uganda (UARTO) & US (MACH14)• Feature generation: Generate time-sensitive features
that capture information about medication adherence
Collect & Process Data
Machine Learning
Insights & Action
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Predict Viral Failure
Problem Formulation
• Outcome of interest: Viral failure in HIV patients (0/1)• Machine Learning task: Ranking (measured by AUC)• Sampling units: Multiple viral load tests per patient —
pooled repeated measures data
• MEMS data collected from electronic pill boxes• Two cohorts: Uganda (UARTO) & US (MACH14)• Feature generation: Generate time-sensitive features
that capture information about medication adherence
• Modeling using a machine learning algorithm (training)• Model evaluation and comparison• Sensitivity & Cost Analysis
Collect & Process Data
Machine Learning
Insights & Action
H2O.ai Machine Intelligence
Predict Viral Failure
Problem Formulation
• Outcome of interest: Viral failure in HIV patients (0/1)• Machine Learning task: Ranking (measured by AUC)• Sampling units: Multiple viral load tests per patient —
pooled repeated measures data
• MEMS data collected from electronic pill boxes• Two cohorts: Uganda (UARTO) & US (MACH14)• Feature generation: Generate time-sensitive features
that capture information about medication adherence
• Modeling using a machine learning algorithm (training)• Model evaluation and comparison• Sensitivity & Cost Analysis
• Results suggest that 25-31% of viral load tests could be avoided while maintaining sensitivity for failure detection at or above 95%, for a cost savings of $16-29 per person-month.
Collect & Process Data
Machine Learning
Insights & Action
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H2O Software
H2O is an open source, distributed, Java machine learning library.
APIs are available for:R, Python, Scala & JSON
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H2O Software Overview
Speed Matters!
No Sampling
Interactive UI
Cutting-Edge Algorithms
• Time is valuable• In-memory is faster• Distributed is faster• High speed AND accuracy
• Scale to big data• Access data links• Use all data without sampling
• Web-based modeling with H2O Flow• Model comparison
• Suite of cutting-edge machine learning algorithms• Deep Learning & Ensembles• NanoFast Scoring Engine
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Current Algorithm Overview Statistical Analysis
• Linear Models (GLM) • Cox Proportional Hazards • Naïve Bayes
Ensembles
• Random Forest • Distributed Trees • Gradient Boosting Machine • R Package - Super Learner
Ensembles
Deep Neural Networks
• Deep Learning • Auto-encoder • Anomaly Detection • Deep Features • Feed-Forward Neural Network
Clustering
• K-Means
Dimension Reduction
• Principal Component Analysis
Solvers & Optimization
• Generalized ADMM Solver • L-BFGS (Quasi Newton
Method) • Ordinary Least-Square Solver • Stochastic Gradient Descent
Data Munging
• Plyr • Integrated R-Environment • Slice, Log Transform
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H2O on Amazon EC2
H2O can easily be deployed on an Amazon EC2 cluster. The GitHub repository contains example scripts.
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Breast Cancer DiagnosticsProblem
Data
• Goal is to accurately diagnose breast masses solely on a Fine Needle Aspiration (FNA).
• Binary outcome: Malignant vs. Benign
• Predictor variables describe characteristics of the cell nuclei present in the FNA image.
• radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, fractal dimension (10 attributes).
• Calculate mean, standard error and “worst” for each for a total of 30 features.
• Each training example is labeled as “Malignant” or “Benign.”
Source: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29
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Predict Malignancy
Problem Formulation
• Outcome of interest: Malignancy (0/1)• Machine Learning task: Binary Classification• Sampling units: Randomly sampled patients who
have had a fine needle aspirate (FNA) image taken.
• Each patient in the training set contributes a digitized image of a FNA of a breast mass.
• Nuclear feature extraction: In each image, calculate the mean, standard error and largest values for attributes such as “radius” and “texture.”
• Train several machine learning models (in H2O)• Model evaluation and comparison• Models are highly accurate (98-99%)
• This work was turned into software called “Xcyt”, which estimates of the probability of malignancy for each case.
• The Xcyt system has been used at University of Wisconsin Hospitals.
Collect & Process Data
Machine Learning
Insights & Action
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Where to learn more?
• H2O Online Training (free): http://learn.h2o.ai• H2O Slidedecks: http://www.slideshare.net/0xdata• H2O Video Presentations: https://www.youtube.com/user/0xdata• H2O Community Events & Meetups: http://h2o.ai/events• Machine Learning & Data Science courses: http://coursebuffet.com
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Questions?
@ledell on Twitter, GitHub [email protected]
http://www.stat.berkeley.edu/~ledell