Unsupervised Pattern Discovery in Sparsely Sampled Clinical Time Series

Transcription

1 Unsupervised Pattern Discovery in Sparsely Sampled Clinical Time Series David Kale Virtual PICU Children s Hospital LA dkale@chla.usc.edu Benjamin M. Marlin Department of Computer Science University of Massachusetts Amherst marlin@cs.umass.edu

2 Pediatric intensive (critical) care

3 Outline PICU Data in the EHR B A C Modeling, Learning, Inference Experiments and Results Conclusion

4 PICU data Physiologic measurements (e.g., vital signs) Manually verified observations High-frequency measurements Waveforms Lab results (e.g., glucose) Clinical assessments (e.g., cognitive function) Medications and treatments Clinical notes Diagnoses Outcomes Billing codes

5 PICU data in commercial EHRs Physiologic measurements (e.g., vital signs) Manually verified observations High-frequency measurements Waveforms Lab results (e.g., glucose) Clinical assessments (e.g., cognitive function) Medications and treatments Clinical notes Diagnoses (often buried in free text notes) Outcomes Billing codes

6 PICU observational time series Physiologic measurements (e.g., vital signs) Manually verified observations Waveforms High-frequency measurements Lab results (e.g., glucose) Clinical assessments (e.g., cognitive function) Medications and treatments Clinical notes Diagnoses (often buried in free text notes) Outcomes Billing codes

7 PICU observational time series High-dimensional (~100 s variables), sparse Episodic (begins at admission, ends at discharge) Large number of individual patients Manually entered or verified by clinical staff HR RR Admit Discharge

8 Alignment Observations begin at time of admission, not at onset of illness HR Admit Discharge HR Admit Discharge

9 Episodes vary in length Duration varies from hours to months Length of Stay (days) (from a data set of 10,599 episodes)

10 Irregular sampling Sparse, irregular sampling in time domain Measurement times not aligned Varies within/across episodes across variables HR RR Admit Discharge

11 Irregular sampling Varies widely from hourly to daily Variable Msmts per day Pulse Oximetric saturation (SpO2) Heart Rate (HR) Respiratory Rate (RR) Systolic Blood Pressure (SBP) Diastolic Blood Pressure (DBP) End-tidal Carbon Dioxide (ETCO2) Temperature (Temp) Total Glascow Coma Score (TGCS) Capillary Refill Rate (CRR) Urine Output (UO) 9.50 Fraction Inspired Oxygen (FIO2) 5.17 Glucose (Gluc) 2.06 ph 1.50

12 Bias and interpretation Evidence of sample selection bias (e.g., more likely to record abnormal) More subtle: human interpreted observations HR RR Admit Discharge

13 Missing not-at-random Evidence of non-random missing data HR ETCO2 Admit Discharge

14 Missing not-at-random NMAR fairly common

15 Outline PICU Data in the EHR B A C Modeling, Learning, Inference Experiments and Results Conclusion

16 B A C Clustering episodes Prioritize simplicity, tractable learning / inference Model with mixture of Gaussians Truncate episodes, bin by hour, assume MAR Empirical Bayesian prior on parameters Kernel-based smoothing prior on mean parameters hour SBP HR Temp Details in Marlin, Kale, Khemani, Wetzel. IHI Unsupervised Pattern Discovery in Electronic Health Care Data Using Probabilistic Clustering Models

17 Generative process For each episode n, sample a cluster assignment Z n =k from the prior distribution over clusters. For each variable v, sample each time point x nvt t=1..24 from the Gaussian distribution associated with cluster k. HR

18 Evaluation The model is (very) wrong, but is it useful? Are the clusters associated with recognizable physiologic and diagnostic patterns that have prognostic significance?

19 Outline PICU Data in the EHR B A C Modeling, Learning, Inference Experiments and Results Conclusion

20 Mortality

21 Neurologic diagnoses Distribution of diagnostic categories across clusters: Neurologic Proportion in cluster Cluster

22 2 2 Low blood pressure Prolonged cap refill High heart rate High respiratory rate Low SpO2 Low ph High mortality (~40%) Shock? MUCMD.ORG SABAN RESEARCH CENTER AUGUST

23 Outline PICU Data in the EHR B A C Modeling, Learning, Inference Experiments and Results Conclusion

24 Broader picture Rich data set, impactful problem area, many potential research directions Goal: decision support via similar patients Data sets: VPICU data will be published MIMIC2: Meaningful Use of Complex Medical Data (MUCMD) August 9-12, Children s Hospital LA Last year s talks:

25 Thanks to our extended team! Randall Wetzel, M.D. Roby Khemani, M.D. Paul Vee, MBA Ricky Nguyen, MS Dan Crichton, M.S. Chris Mattmann, Ph.D. Andrew Hart, M.S. Benjamin Marlin, Ph.D. Chris Shelton, Ph.D. Busra Celikkaya Yan Liu, Ph.D.

26 Outline PICU Data in the EHR B A C Modeling, Learning, Inference Experiments and Results Conclusion Appendix

27 Mortality prediction Bernoulli Model: Associates a fixed mortality rate ¼ k with each cluster k. Very simple. Logistic Model: Associates a logistic regression model with each cluster k. Features are maximum and minimum values of each of the 13 physiological variables.

28 Mortality prediction

29 Mortality prediction Bernoulli cluster models approach performance of logistic model 5, 10 cluster logistic model outperform logistic model

30 Graphical model

31 Modeling Basics

32 Modeling Basics Expectation Maximization

33 Modeling Basics ML: MAP:

34 Irregular sampling Significant differences between variables (SpO 2 : ~hourly, glucose: ~daily)

35 Effects of interventions Interventions alter disease process, underlying physiology, resulting observations HR RR Admit I1 I2 I3 Discharge

36 Respiratory diagnoses Distribution of diagnostic categories across clusters: Respiratory 473 Proportion in cluster Cluster

37 3 7 High blood pressure Brisk cap refill Low heart rate Low respiratory rate High SpO2 High TGCS Normal glucose Low mortality (~0%) MUCMD.ORG SABAN RESEARCH CENTER AUGUST

38 Derived similarity scores