the frontiers of machine learning in health and behavioral

The Frontiers of Machine Learning in Health and Behavioral Science

Tufts University, Nov 15, 2012 Benjamin M. Marlin

Advanced Machine Learning Methods for Mobile Health Research

Benjamin M. MarlinCollege of Information and Computer Sciences

University of Massachusetts [email protected]

Wireless Health Workshops Oct 25, 2016

Machine Learning Methods for Mobile Health Research

Wireless Health – Oct 25, 2016 Benjamin M. Marlin



Center for

ScienceData

Affiliations



Collaborators and Sponsors

Dr. Robert MalisonYale UniversitySchool of Medecine

Malai NatarajanUMass CS PhD candidate

Abhinav ParateUMass CS PhD graduateNow at HP Labs

Roy AdamsUMass CS PhD candidate

Prof. Edison ThomazUT AustinECE

Prof. Santosh KumarU of MemphisComputer Science

Nazir SaleheenU of Memphis PhD CandidateComputer Science

Prof. Deepak GanesanUMass AmherstComputer Science



Wearable Sensors

Accelerometer

ECG

TemperatureRespiration

Accelerometer

GPS

GyroscopeMagnetometer

Microphone

Scene Camera

Gaze Tracker

Eye Metrics

Accelerometer

GPS

GSRGyroscope

PPG



The Role of Machine Learning in Mobile Health ResearchPart 1:A Primer on Detection and Detector LearningPart 2:

Leveraging Domain Knowledge with Structured PredictionPart 3:Addressing Lab-to-field GeneralizationThrough Domain AdaptationPart 4:



Part 1The Role of Machine Learning in

Mobile Health Research



Data

Patient Population

Health & Behavior

SensorSystems



Data Analytics



AnalyticsQuestions

PredictionWhat will happen in the future?

DetectionWhat is happening right now?

ControlHow to direct a process to a desired state?

CausationWhat is the causal structure of a process?



Detectors

Data

Sensors

Subject

Monitoring



Detectors

Data

Sensors

Subject

Interventions



Detectors

Data

Sensors

Subject

Causal Analysis

EMA/Labs



Part 2A Primer on Detection and Detector Learning



The Detection Problem: Suppose we have a dynamical system in which an event of interest either occurs or does not occur at each time instance t. Given a feature vector xt∈ℝD that partially describes the state of the system at time t, infer whether the event occurred at time t or not.



Duration

Stre

tch

Feature Space



A Detection Function: Given a feature vector xt∈ℝD

that partially describes the state of a dynamical system at time t, a detection function f: ℝD→ {0,1}. 0 indicates the event of interest did not occur, and 1 indicates that the event of interest did occur.

f( )=



Detection Function in Feature Space

Duration

Stre

tch



Detection Model: A detection model is a set of functions F where for each f∈ F, f: ℝD→ {0,1}. It is typical for the set F to consist of a single function f(x,w)=fw(x) that depends a vector of parameters w∈ ℝK: F = {fw | w∈ ℝK}.

fw(x) =

8><

>:1 ... if

DX

d=1

wdxd > w0

0 ... otherwise



Detection Model in Feature Space

F={ }



Detector Learning Problem: Given a data setD={(xi,yi)|1<i<N} consisting of feature vectors xi∈ℝD

and event labels yi∈ {0,1}, select a function f: ℝD→{0,1} from F that maps feature vectors x ∈ℝD to their event labels as accurately as possible. For a parametric model fw, this problem reduces to finding the best model parameters w*.

w⇤ = argminw

NX

n=1

[fw(xn) 6= yn]



Detector Learning Problem: Given a data setD={(xi,yi)|1<i<N} consisting of feature vectors xi∈ℝD

and event labels yi∈ {0,1}, select a function f: ℝD→{0,1} from F that maps feature vectors x ∈ℝD to their event labels as accurately as possible. For a parametric model fw, this problem reduces to finding the best model parameters w*.

w⇤ = argmin

w

NX

n=1

loss(fw(xn), yn)



Classification Regression

Clustering Dimensionality Reduction

Supe

rvis

edU

nsup

ervi

sed

Learning to detect and

predict.

Learning to organize and represent.

Detection as Classification



Detector Learning Example

Duration

Stre

tch



Collect Data

Collect Labels

LearnDetector

Extract Features

Test

ing Collect

DataApply

DetectorExtract

FeaturesEvaluate

Performance

ChooseModel

Collect Labels

Trai

ning

Depl

oy

Collect Data

ApplyDetector

Extract Features

MonitoringIntervention

Analysis

CleanData

CleanData

CleanData

Full Detector Learning Process



Detector Learning Challenges in mHealth

Driving Question: How can we increase the accuracy of learned detectors while keeping,

energy, cost and subject burden low?



1. Structured Prediction: Leverage domain knowledge regarding structure of event labels to improve detection accuracy.

2. Domain Adaptation: Use learning protocols that can account for limited ecological validity to improve lab-to-field generalization.

Machine Learning Solutions



Part 3Leveraging Domain Knowledge with

Structured Prediction



• In many time series detection problems, there is structure in the event label values.

• If we can identify the constrains that this structure imposes on sequences of event labels, we can improve detection accuracy by jointly detecting events at multiple times.

• This is a detection strategy best suited for offline data analysis.

Basic Ideas



• Coherence: Event labels that are close in time are more likely to take the same value.

• Transitions: Transitions between some pairs of event types (eg: A-B) are more likely than transitions between other types (eg: A-C).

• Sequences: Events are more likely to occur in some sequences than others (eg: A-B-C-A-B-…).

• Hierarchy: Occurrence of high-level activities change the likelihood that different types of lower-level events occur.

Types of Structures



Case Study 1: Preferred Transitions for ECG

Natarajan, Annamalai, Edward Gaiser, Gustavo Angarita, Robert Malison, Deepak Ganesan, and Benjamin Marlin. "Conditional Random Fields for Morphological Analysis of Wireless ECG Signals." Proceedings of the 5th Annual conference on Bioinformatics, Computational Biology and Health Informatics. Newport Beach, CA: ACM, 2014.



Data Collection



Challenges

http://www.physionet.org/physiotools/ecgpuwave/

High uncertainty due to noise and strictly local reasoning lead to labeling errors that corrupt features.



Independent Detection

Y1

X1

Y2

X2

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X3

Y4

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Y5

X5



Proposed Solution: Structured Prediction

Y1

X1

Y2

X2

Y3

X3

Y4

X4

Y5

X5



Proposed Solution: Structured Prediction

Y1

X1

Y2

X2

Y3

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X4

Y5

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Y7

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P Q R S T N P



Linear Chain CRF Probabilistic Model

Joint Label Distribution

Partition Function



Linear Chain CRF Probabilistic Model

Energy Function

Feature potentials

Transition potentials



• Both MAP and marginal inference are O(L) using sum-product or max-product belief propagation (Lafferty, McCallum, Pereira, 2001).

• We fit the model parameters using the standard maximum likelihood approach. The optimization problem is convex and relies on marginal inference as a sub-routine.

Linear Chain CRF Inference and Learning



Experimental Protocol: ECG Morphology1. Over-generate candidate peak locations using

peak detector.

2. Manually label randomly chosen clusters of peaks for each subject (~3000 peaks/subject, ~10 peaks/cluster).

3. Split clusters into train and test sets. Train models on train set. Evaluate accuracy on test set.

4. Consider both within subject and across subject protocols, CRF and MLR models.



Results: ECG Labeling

Within Subjects Across Subjects



Results: ECG Labeling vs Training Set Size



Case Study 2: Hierarchical Detection with Gestures

Challenges:

• Wrist worn actigraphy provides weak evidence for occurrence of gestures of interest like eating and smoking.

• Activity sessions are characterized by higher-order interactions between gesture labels.

Adams, Roy, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, and Benjamin Marlin. "Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams." International Conference on Machine Learning. 2016.



Proposed Model

Events variables



Proposed Model

Events variables

Segmentvariables



Example Joint Labeling and Segmentation



Adding Inter-Label Duration Segments



Segmentation Constraint:

Nesting Constraint:

Global Segmentation Coordination Factors



Probabilistic Model

Segmentation and Nesting Factors (l>1)

Joint Label and Segment Distribution



Probabilistic Model

Feature Factors

Event Cardinality Factors



Probabilistic Model

Positional Factors

Segment Label Alternation



MAP Inference in this model can be performed using a dynamic program in O(L2) time, similar to the semi-markov CRF (Sarawagi and Cohen 2004).

Inference and Learning

We fit the model parameters using loss augmented maximum-margin methods (Tsochantaridis et al. 2005).



Related Models

Y1 Y2 Y3 Y4

Y2 Y3 Y4Y11 111

Y122 Y34

2

(a)

(b)

Y2 Y3 Y4Y11 111

Y122 Y34

2Y232

Y132 Y24

2

Y142

(c)

(d)

(e)

(f)

(a)Linear-chain CRF (LC-CRF)(b)Tree structured CRF (T-CRF)(c)Hierarchical Nested Segmentation model (HNS)



Experimental Protocol

1. Gesture, inter-gesture, and session labels obtained from video record or direct observation.

2. Bottom-level segmentation of sensor time series into fixed-length windows (6s, eating), or using adaptive segmentation (smoking).

3. Extract features from windows.

4. Leave-one-subject out evaluation protocol.

5. Compare to tree-structured CRF and MLR models.



Dataset DetailsPM T MP RQS

Behavior Smoking Eating Smoking SmokingModality Chest

bandWristaccel.

Chest band

Wrist accel.



30 7 03 5QS

0.1

0.3

0.5

0.7

0.9

F1L57-C5FH1S

Event Labeling Results



30 7 03 5QS0.3

0.5

0.7

0.9

F17-C5FH1S

Session Labeling Results



Part 4Addressing Lab-to-field Generalization

Through Domain Adaptation



• Due to the issues with collecting high-quality event labels in the field, many mHealth event detection studies collect carefully labeled data in the lab, and then deploy learned detectors in the field.

• Because ecological validity is often very limited in the lab there is often a significant gap in performance (or even applicability) between the lab and the field.

• In machine learning, this problem is called domain shift. Lab data represent the source domain, while field data represent the target domain.

Basic Ideas



• Prior Probability Shift: The relative occurrence of different types of events changes from the source to the target domain.

• Covariate Shift: The distribution of feature values associated with an event type changes from the source domain to the target domain.

• Label Granularity Shift: The temporal granularity of the labels changes from the source to the target domain.

Types of Domain Shift



Case Study 4: Domain Adaptation for Cocaine Detection

Natarajan, Annamalai, Gustavo Angarita, Edward Gaiser, Robert Malison, Deepak Ganesan, and Benjamin Marlin. "Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG." 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2016



Study Protocol


Wireless Health – Oct 25, 2016 Benjamin M. Marlin•68

Prior Probability Shift

Lab Field



Covariate Shift

Lab Field



Assessing Covariate Shift



Correcting Domain Shift with Re-Weighting

w⇤ = argminw

NX

n=1

�(xm, yn)[fw(xn) 6= yn]

We can correct for prior probability shift and covariate shift by re-weighting lab data to better match statistics of field data when learning detectors.



Prior Probability Shift Correction

Lab Field

=x

Weights



Covariate Shift Correction

Lab Field

=x

Weights



Calculating Multivariate Covariate Shift Weights



Label Granularity Shift• In our study protocol, we have event labels for

cocaine use that are accurate at the minute level.

• In the field, we have self-reported intervals of cocaine use, which are not reliable. We also have daily utox measurements, which are considered ground truth, but are temporally .

• Both the lab cocaine event labels and the utoxlabels provide ground truth for presence of cocaine use, but there is a significant shift in the temporal granularity of the labels.



Label Granularity Shift Correction



• Zephyr BioHarness chest band sensor paired to a smartphone.

• Extracted 24 ECG features per 5 minute sliding window

• Cocaine detection and urine test prediction model: penalized l2 logistic regression.

• 37 field days (28 days +ve urine test)

Study Details



Results



Conclusions

1. Probabilistic structured prediction models lead to performance improvements for mHealthproblems by using global reasoning to combat uncertainty due to low power, low cost sensing.

2. Domain adaptation methods can help to mitigate ecological validity issues in lab-to-field study designs.



Thank You!



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