Benjamin M. Marlin Department of Computer Science University of Massachusetts Amherst January 21, 2011

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1 Modeling and Prediction With ICU Electronic Health Records Data Department of Computer Science University of Massachusetts Amherst January 21, 2011

2 The Big Picture: History of Medical Records The first use of centralized, paper medical charts began in The first electronic medical records systems were introduced in the 1970s. Today, an increasing number of hospitals are adopting the use of electronic records.

3 The Big Picture: EHR Adoption Rates Percent of non-federal acute care hospitals with adoption of at least a Basic EHR

4 The Big Picture: Meaningful Use As the amount of electronic health records data increases, there is a growing opportunity to leverage this data to improve the quality of care that patients receive. Prediction of length of stay, diagnosis, mortality outcomes, leading to improved clinical decisions. Better ways of accessing data like physiology-based patient similarity search.

5 Cognitive Prosthesis

6

7

8 Outline Introduction Electronic Health Records Data B A C Modeling, Learning & Inference Discussion

9 EHRs: What s in them? The data contained in EHRs is collected during the course of routine treatment. An EHR includes information like: Diagnosis and some outcome information (eg: died while in hospital) Medications ordered and administered (including time stamps and dose) Laboratory analysis and radiologic information Clinical notes with assessments In the case of ICU EHRs, physiologic data

10 EHRs: Physiological Data The physiological data in an EHR is a multivariate time series that starts when the patient is admitted and ends when the patient is discharged. HR RR Admit Discharge

11 EHRs: The vpicu Dataset Variable The vpicu data set contains measurements of 13 variables from over 10,500 episodes. 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 EHRs: The vpicu Dataset The length of stay per patient episode varies from a few hours to a few years. Length of Stay (days)

13 EHRs: The vpicu Dataset The time series for different patients are usually not aligned in any meaningful way. HR Admit Discharge HR Admit Discharge

14 EHRs: The vpicu Dataset The data is very sparsely and irregularly sampled in the time domain and the measurement times are not aligned at all. HR RR Admit Discharge

15 EHRs: The vpicu Dataset Different variables have vastly different sampling frequencies from an average of once an hour to an average of once per day.

16 EHRs: The vpicu Dataset Different patients have vastly different sampling frequencies for the same variable.

17 EHRs: The vpicu Dataset Some variables may be subject to sample selection bias for a variety of reasons, including observation of abnormal values. HR RR Admit Discharge

18 EHRs: The vpicu Dataset Some variables may be subject to nonrandom missingness for a variety of reasons, including diagnosis hypotheses. HR ETC Admit Discharge

19 EHRs: The vpicu Dataset Some variables may be subject to nonrandom missingness.

20 EHRs: The vpicu Dataset The application of interventions alters the underlying physiology and needs to be taken into account. HR RR Admit I1 I2 I3 Discharge

21 Outline Introduction Electronic Health Records Data B A C Modeling, Learning & Inference Discussion

22 Modeling: Grand Challenge Sparsity Irregular Sampling Alignment Selection Bias NMAR Interventions

23 Patient Clustering

24 Patient-Patient Similarity

25 Clustering: Simplifications B A C We dealt with irregular sampling issues using temporal discretization (binning). We dealt with the resulting missing observations by assuming MAR. We ignored temporal alignment issues. We dealt with interventions and length of stay variations by modeling 24H of data only.

26 Clustering: Simplifications? ? ?? 0

27 Clustering: Dealing with Sparsity ML: MAP:

28 Clustering The model is wrong, but is it useful? Are the clusters associated with recognizable physiologic and diagnostic patterns that have prognostic significance?

29 Clustering: Cluster Analysis

30 Clustering: Cluster Analysis Low blood pressure Prolonged cap refill High heart rate High respiratory rate Low SaO2 Low ph Low TGCS Shock and depressed cognitive function

31 Clustering: 24H Mortality Prediction

32 Patient-Patient Similarity: Simplifications B A C We re dealing with irregular sampling directly using continuous time models. We re still treating totally missing data as MAR and ignoring temporal alignment, sample selection bias and interventions.

33 Patient-Patient Similarity: Gaussian Processes

34 Patient-Patient Similarity: Gaussian Processes

35 Patient-Patient Similarity: Bhattacharyya We can measure the similarity between two Gaussian processes posteriors at any time point t by computing the Bhattacharyya coefficient: 1/2 1/2

36 Patient-Patient Similarity: Bhattacharyya

37 Patient-Patient Similarity: Examples

38 Preliminary Tests: Spectral Embedding

39 Outline Introduction Electronic Health Records Data B A C Modeling, Learning & Inference Discussion

40 Discussion There is a hierarchy of increasingly sophisticated models that could be applied to this data. The direct similarity approach can incorporate many possible (and necessary) extensions: Alignment Selection bias NMAR Interventions? Additional data sources impacting similarity

41 Discussion An advantage of the direct similarity approach is that it can be used in the solution to many machine learning algorithms: Dimensionality reduction Clustering Classification Regression We can use these tools to drive data visualization and information retrieval as well as prediction.

42 Collaborators and Students Dr. Randall Wetzel Professor of Pediatrics and Anesthesiology; The Anne O'M. Wilson Professor of Critical Care Medicine; Director, Critical Care Medicine Childrens Hospital Los Angeles; Director, The Laura P. and Leland K. Whittier Virtual Pediatric ICU. Dr. Robinder Khemani Anesthesiology and Critical Care Medicine, Childrens Hospital Los Angeles and The Laura P. and Leland K. Whittier Virtual Pediatric ICU. David Kale The Laura P. and Leland K. Whittier Virtual Pediatric ICU. Steve Li UMass CS PhD student Gregory W. Koch UMass REU student /Austin College

43 Thank You

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