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Towards Personalizing Monitoring with Wearable Sensors
Bobak MortazaviSystems & Technology for Medicine & IoT
[email protected]://stmi.engr.tamu.edu/
1
Outline
• Goal of this talk• Description of End-to-End Systems• Stage-wise learning of recovery
– Risk Models– Remote Sensing– Personal Modeling
2
Goals: Questions to Answer!
1. What improvements to the research infrastructure are needed?
2. What types of training are most important for this type of research?
3. What are the future research needs (methods, analyses and interventions, etc.)?
3
End-to-end systems
• Start with fit patient trending to unfit• Unfit patient trending to heart attack• Heart attack causing damage to the heart muscle• Heart attack needing operation• Post-operation recovery
4
End-to-end systems
• Start with fit patient trending to unfit• Unfit patient trending to heart attack• Heart attack causing damage to the heart muscle• Heart attack needing operation• Post-operation recovery• What are we calling an end-to-end system?
5
End-to-End Clinical
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- Goals:- Model adaptability and robustness for remote health sensing- Task-oriented feature extraction, both personalization and generalization
- Proposed methods:- Context-aware deep modeling - Bayesian uncertainty quantification Gaussian Process model
- Implementations: - The alpha-beta network: a deep mixture of experts- Maximum entropy learning (ICML workshop'19) - Adaptive monitoring (published in AISTATS'19)
Task Oriented Features
Hand Designed Features
End-to-End Remote
End-to-end systems
• Start with fit patient trending to unfit• Unfit patient trending to heart attack• Heart attack causing damage to the heart muscle• Heart attack needing operation• Post-operation recovery
8
ML-based Clinical Outcomes Models
9
• Update CathPCI Major Bleeding Model
• Use ML Techniques and additional data to improve identification of bleeds and non-bleeds
Study Design
• Evaluate if Machine Learning – alone – can naively improve model performance
• Evaluate if increased fidelity in data – alone – can naively improve model performance
• Evaluate a balance of understanding the data and modeling techniques will improve models
Study Aims
Outcomes Model
Data Source
10
Existing Clinical Model
• “Bedside” Model• Variables Sub-selected from Full Post-
PCI model• Want to re-introduce all available
variables
11
Power of Additional Data
Variable Description and CathPCI field
Age Patient age 2050
Age >70 Is patient age >70? (yes/no) 2050
Age ≤70 Is patient age ≤70? (yes/no) 2050
BMI Body mass index 4055, 4060
BMI ≤30 Is BMI ≤30? (yes/no) 4055, 4060
Pre-procedure
hemoglobin
Pre-procedure hemoglobin
(continuous)7320
Pre-procedure
hemoglobin ≤13 g/dL
Pre-procedure hemoglobin ≤13 g/dL
(yes/no) 7320
Pre-procedure
hemoglobin >13 g/dL
Pre-procedure hemoglobin >13 g/dL
(yes/no) 7320
12
Continuous Representation
13(Mortazavi et al 2019, JNO)
Findings and Opportunities
Key Takeaways1. Intelligent selection of variables
is needed.2. More fluid representation of risk
factors presents higher accuracies.
Limitations1. Still a single decision point!
Confounded by future decisions?2. Clinical journal – do
engineering/CS reviewers know how to interpret these?
3. Identified an additional 168 bleeding cases and 949 non-bleeding cases per 100,000 cases.
14
Findings and Opportunities
Key Takeaways1. Intelligent selection of variables
is needed.2. More fluid representation of risk
factors presents higher accuracies.
Limitations1. Still a single decision point!
Confounded by future decisions?2. Clinical journal – do
engineering/CS reviewers know how to interpret these?
3. Identified an additional 168 bleeding cases and 949 non-bleeding cases per 100,000 cases. So What?
15
Goals: Questions to Answer!
1. What improvements to the research infrastructure are needed?Data availability – How do we continue pushing the boundaries on this?
2. What types of training are most important for this type of research?Language/Interaction – How do we encourage joint conferences or overlap in journals?
3. What are the future research needs (methods, analyses and interventions, etc.)?
Allow for analyses that work on the margin/in the special cases
16
EHR-based “Dynamic” modeling
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• Develop models from EHR data for adverse events post-PCI
• Use ML Techniques and timely data!
Study Design
• Evaluate if models can be as accurate with EHR data
• Evaluate challenges in replicating registry-level variables in EHR data
Study Aims
Data Timeline
Dataset: EHR Extraction
18
Results
19(Mortazavi et al 2017, IEEE JBHI)
Findings and Opportunities
Key Takeaways1. Can make decisions from EHR in
timely fashions2. Registries can guide us on data
cleaning in EHRs
Limitations1. Still a single decision point!!!
Confounded by future decisions?2. Engineering Journal – Do clinical
researchers read these?
20
Goals: Questions to Answer!
1. What improvements to the research infrastructure are needed?Data availability – How do we continue pushing the boundaries on this?
2. What types of training are most important for this type of research?Language/Interaction – How do we encourage joint conferences or overlap in journals?
3. What are the future research needs (methods, analyses and interventions, etc.)?
Allow for analyses that work on the margin/in the special casesStart accounting for real-time, prospective use models and analyses that enable data processing for prospective use
21
End-to-end systems
• Start with fit patient trending to unfit• Unfit patient trending to heart attack• Heart attack causing damage to the heart muscle• Heart attack needing operation• Post-operation recovery
22
Heart Failure and Readmissions
• High Readmission rates impose a tremendous burden on patients and healthcare systems. Modeling of recovery has been challenging.
• Understanding Ambulatory BP more predictive of outcomes than clinic-based BP.
• Diet and Exercise are important factors in personal care• Cardiac rehabilitation programs have shown promise but low
adherence– Limited program availability, cost of attending, cost of transportation
all impact performance – Home-based program too hard to follow, or approximate too much
and lose individual feedback and engagement
23
Tele-HF Readmissions
24(Mortazavi et al 2016, Circ: CQO)
Wearable Health
25
Some Prior Work: Developing Required Sensors
26
(Mortazavi et al 2012, IEEE BSN)(Mortazavi et al 2013, IEEE JBHI)(Lee et al 2013, IEEE JBHI)
Personal (Cardiac) Rehabilitation Needs
• Heart Rate, V02, Exercise• Resting Heart Rate, Respiration Rate, and Blood Pressure• Diet• Must understand variety of measurements, intensities, and
context the data is gathered in
27
Activity and Context Tracking
28
Understanding Context
• Provide participants with CGMs• Provide breakfasts with known
carbs/fats/proteins after 8 hours of fasting• Limit physical activity and measure impact
of meals
Study Design
• Understand and measure post-prandial glucose response under varying carbs/fats/proteins.
• Understand subject-to-subject variability to similar meals.
• Develop machine learning model to estimate amount of carbs/fats/proteins from CGM Signals
Study Aims
Smartwatches and Smartphones
Personalized Learning: Context and Uncertainty Quantification
29
(Solis et al 2019, IoTDI)
(Ardywibowo et al 2019, AIStats)(Solis et al., under review – IEEE JBHI)(Pakbin et al., under review - AIStats)
Diet Monitoring
30
Estimating Macronutrients
• Provide participants with CGMs• Provide breakfasts with known
carbs/fats/proteins after 8 hours of fasting• Limit physical activity and measure impact
of meals
Study Design
• Understand and measure post-prandial glucose response under varying carbs/fats/proteins.
• Understand subject-to-subject variability to similar meals.
• Develop machine learning model to estimate amount of carbs/fats/proteins from CGM Signals
Study Aims
Continuous Glucose Monitors
Varying Carbohydrates and Measuring Glucose
31
The glucose response (averaged over all participants) to three meals with known fats/proteins and varied quantity of carbs shows expected larger and longer glucose response
We encode meals as CxPxFxwhere x is
• 1 = low quantity
• 2 = med quantity
• 3 = high quantity 50
100
150
1 6 11 16 21 26 31 36
Bloo
d gl
ucos
e (m
g/dL
)
Time (15 min)
C3P2F2 C2P2F2 C1P2F2
Varying Proteins and Fats
32
50
100
150
1 6 11 16 21 26 31 36
Bloo
d glu
cose
(mg/
dL)
Time (15 min)
C2P3F2 C2P2F2 C2P1F2
50
100
150
1 6 11 16 21 26 31 36
Bloo
d gl
ucos
e (m
g/dL
)
Time (15 min)
C2P2F3 C2P2F2 C2P2F1
Similarly, we find that varying quantities of proteins dulls amplitude and extends duration of recovery to baseline.
Varying quantities of fats dulls amplitudes and extends duration of recovery to baseline.
Subject to Subject Variability!
33
0
50
100
150
200
250
1 6 11 16 21 26 31 36
Guco
se le
vel (
mg/
dL)
Time (15 min intervals)
Sample of individual responses to a meal that represents the average American diet (medium amount of carbs/fats/proteins) shows high subject-to-subject variability!
Estimation
34
Capturing fasting glucose levels, subject body weight, and important features regarding the glucose response shape and duration leads to accurate carbs/fats/proteins modeling:• Correlation coefficients of 0.76, 0.57, and 0.37 respectively.• Balanced accuracy of classifying low vs. medium vs. high amounts
carbs/fats/proteins of 95.1%, 65.1%, and 65.4% respectively
(Huo et al. 2019, IEEE BHI)(Mortazavi et al. Under Review, IEEE JBHI)
Cuffless Blood Pressure
35
Personal Models
• Design Bio-Impedance Sensor• Understand/capture range of BP
measurements• Develop personal models of PTT/PWV to
SBP/DBP• Validate against ABPM
Study Design
• Understand data needs for models to estimate SBP/DBP from PTT/PWV
• Evaluate strengths of Bio-Z approach over PPG
• Develop transfer learning techniques for rapid calibration
Study Aims
Cuffless BP Monitor
BP through Bio-impedance
36(Ibrahim and Jafari 2019, IEEE TBioCas)
IP
DIA
MS
SYS
Diastolic Phase
Systolic Phase
Max. Slope
Inflection Point
Heart Beat
*Bio-Z flipped
PTT Bio-Z2*
Bio-Z1*Points
DetectionFeatures
Extraction DBP Regression
Model
Systolic BP
Diastolic BP
SBP Regression
Model
Bio-Z1Bio-Z2
Bio-Z3Bio-Z4
Radial Artery Ulnar Artery
I*Bio-Z flipped
V
I
Arterial Pulse Wave
Findings and Opportunities
Key Takeaways1. Measuring the context in which
data is captured in remote/personal environments is extremely important.
2. It is important to model subject-to-subject variability in biometric measurements.
3. Have to balance big data ML problems with small data ML problems.
Limitations1. How do we create personal
models without burdening user with massive amounts of data collection?
2. Where is the linkage back to clinical data-driven outcomes models?
37
Goals: Questions to Answer!
• 1. What improvements to the research infrastructure are needed?Data availability – How do we continue pushing the boundaries on this?Data linkage between remote/personal models and EHR/RCT modelsData sharing infrastructure!
• 2. What types of training are most important for this type of research?Language/Interaction – How do we encourage joint conferences or overlap in journals?
• 3. What are the future research needs (methods, analyses and interventions, etc.)?
Allow for analyses that work on the margin/in the special casesStart accounting for real-time, prospective use models and analyses that enable data processing for prospective useN of 1 studies – personalization and longitudinal data collection needsData sharing infrastructure!
38
Treatment?
• Start with fit patient trending to unfit• Unfit patient trending to heart attack• Heart attack causing damage to the heart muscle• Heart attack needing operation• Post-operation recovery
Treatment?
• Start with fit patient trending to unfit• Unfit patient trending to heart attack• Heart attack causing damage to the heart muscle• Heart attack needing operation• Post-operation recovery
Treatment?
• Start with fit patient trending to unfit• Unfit patient trending to heart attack• Heart attack causing damage to the heart muscle• Heart attack needing operation• Post-operation recovery
Acknowledgements
42
Sponsors:
Dr. Harlan Krumholz Dr. Roozbeh Jafari Dr. Erica Spatz
Dr. Sahand Negahban Dr. Emily Bucholz Dr. Chenxi Huang Dr. Nihar Desai Dr. Sanket Dhruva Dr. Wade Schulz
My Students and So Many more!
Goals: Questions to Answer!
• 1. What improvements to the research infrastructure are needed?Data availability – How do we continue pushing the boundaries on this?Data linkage between remote/personal models and EHR/RCT modelsData sharing infrastructure!
• 2. What types of training are most important for this type of research?Language/Interaction – How do we encourage joint conferences or overlap in journals?How do we encourage interdisciplinary needs to catch up to “state of the art” in both fields? Coursework? Postdocs?
• 3. What are the future research needs (methods, analyses and interventions, etc.)?
Allow for analyses that work on the margin/in the special casesStart accounting for real-time, prospective use models and analyses that enable data processing for prospective useN of 1 studies – personalization and longitudinal data collection needsData sharing infrastructure!Emphasis on funding opportunities to foster/enable big team collaborations.
43