A Novel Temporal Similarity Measure for Patients Based on Irregularly Measured Data in Electronic Health Records
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1 ACM-BCB 2016 A Novel Temporal Similarity Measure for Patients Based on Irregularly Measured Data in Electronic Health Records Ying Sha, Janani Venugopalan, May D. Wang Georgia Institute of Technology Oct. 3 th, 2016
2 Outline Introduction and Motivation Temporal Similarity Measure Experiment Design and Results Discussion and Future Work
3 Outline Introduction and Motivation Temporal Similarity Measure Experiment Design and Results Discussion and Future Work
4 Background With increasing adoption of electronic health record (EHR) systems, millions of patients have their medical histories digitized and archived in a structured form The immense amount of EHR data serves as unique resources for clinical decision support (CDS) Finding similar patients to a target patient To derive diagnostic and prognostic information for guiding the treatment of the target patient Personalized medicine
5 Motivation Medical history as a sequence of time-stamped events Inpatients are monitored based on their health status [1] The choice of specific measurements The order of specific measurements The frequency of measurements e.g. patients are monitored more intensively when their health is deteriorating Most existing patient-similarity measures did not utilize all the aforementioned temporal information [2-5] Rich information hidden in measurement patterns
6 Outline Introduction and Motivation Temporal Similarity Measure Experiment Design and Results Discussion and Future Work
7 Decision Support for Case-based Reasoning ICU database A target patient Patient 1 Patient 2 Patient n Ranking by Temporal Similarity Facilitate clinicians decision making After similarity ranking Target patient Patient 1 Patient n
8 Concept Modification of Smith-Waterman Algorithm [7] Dynamic programming Determining similar regions between two strings Tolerant of missing or extra events Patient 1 Patient 2 A B C 2h 20h A C 15h Output: Patient 1 = A 2h B 20h C Patient 2 = A h C
9 Matrix Representation Patient 1 Patient 2 A B C 2h 20h A C 15h A 2h B 20h C A 2h B 20h C A A 0 15h h 0 C C 0 Match : *Δt Mismatch: -3 Gap: *Δt Output: Patient 1 = A 2h B 20h C Patient 2 = A h C
10 Outline Introduction and Motivation Temporal Similarity Measure Experiment Design and Results Discussion and Future Work
11 Patient Data Extraction Table 1 Top 10 most frequent Laboratory Items in MIMIC-II Laborator y Item Identifier in MIMIC-II # of Records Total Normal Abnormal Hematocrit 50, ,604 73, ,202 Potassium 50, , ,082 67,096 Sodium 50, , ,852 83,377 Creatinine 50, , , ,475 Platelets 50, , , ,968 Urea nitrogen 50, , , ,008 Chloride 50, , , ,370 Bicarbonat e 50, , , ,930 Anion gap 50, , ,916 32,349 Leukocytes 50, , , ,087 Total 502,177 5,308,608 3,411,746 1,896,862 Table 2 Top 10 most frequent Laboratory Items in the CHOA dataset Laboratory Item # of Records Total Normal Abnormal POC glucose 58,639 19,711 38,928 Oxygen Saturation 49,260 21,487 27,773 Arterial POC ph 49,256 21,620 27,636 Arterial POC pco2 49,246 26,477 22,769 Arterial POC Po2 49,246 5,617 43,629 Sodium 43,603 33,872 9,731 POC ionized calcium 43,194 28,614 14,580 Potassium 42,985 28,139 14,846 Calcium 42,727 29,555 13,172 Glucose 42,629 26,712 15,917 Total 470, , ,981
12 Visualization of an Alignment of Patients History
13 Predictive power of the similarity measure Hypothesis: Patients sharing similar lab-test trajectories in the past tend to share similar lab-test trajectories in the future Reasoning: Patients with higher similarity scores (by our approach) of past lab-test trajectories have higher similarity scores of future lab-test trajectories Given a target patient, we can construct a simple linear regression models: SimilarityScore future =a+ b SimilarityScore past Ho: b=0 Ha: b 0 p-value < 0.05?
14 Proving the predictive power Actual Baseline
15 Predictive Power of Our Novel Similarity Measure
16 Case study: Lab testing vs 48-hr Mortality Lab tests are informative for a diagnosis of AKI Mortality rate : 27% A target patient diagnosed with acute kidney injury (AKI) K most similar AKI patients to the target patients based on 48-hr-lab-test trajectories Mortality prediction MIMIC2 Database Majority Voting of similar patients
17 Case study: Lab testing vs 48-hr Mortality Lab tests are informative for a diagnosis of sepsis Mortality rate : 16% A target patient diagnosed with sepsis and severe sepsis K most similar sepsis patients to the target patients based on 48-hr-lab-test trajectories Mortality prediction CHOA Database Majority Voting of similar patients
18 Case study: Lab testing vs 48-hr Mortality Compare to two non-temporal similarity measures The Jaccard Index [9] : J A, B = AA BB AA BB Presence or absence of specific abnormal lab-test results Cosine [10] : cos θθ = AA BB AA BB Number of occurrences of specific abnormal lab-test results
19 Result Sensitivity Varying K of KNN
20 Result F-measure Varying K of KNN MIMICII Acute Kidney Injury CHOA Database Sepsis
21 Result Sensitivity Varying Prediction window
22 Result F-measure Varying Prediction Window MIMICII Acute Kidney Injury CHOA Database Sepsis
23 Outline Introduction and Motivation Temporal Similarity Measure Concept and Visualization Experiment Design and Results Discussion and Future Work
24 Discussion Our novel similarity measure has predictive power, better than that of other non-temporal measures The assumption that patients are monitored more intensively when their health is deteriorating, should be experimentally validated or consulted with clinicians Clinical protocols may vary hospital by hospital Penalize a match between two time intervals according to their absolute difference May hinder the application of this method to measurements spanning over multiple time scales: days for ICU care and years for outpatients
25 Future work Combine non-temporal features, i.e., demographics, with temporal features, e.g., lab tests, medication, diagnosis, I/O events Feature selection before similarity analysis Solve more real-world clinical problems
26 Reference 1. R. Pivovarov, D. J. Albers, J. L. Sepulveda, and N. Elhadad, "Identifying and mitigating biases in EHR laboratory tests," Journal of biomedical informatics, vol. 51, pp , G. Hripcsak, D. J. Albers, and A. Perotte, "Parameterizing time in electronic health record studies," Journal of the American Medical Informatics Association, vol. 22, p , J. Lee, D. M. Maslove, and J. A. Dubin, "Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric," Plos One, vol. 10, p. e , A. Gottlieb, G. Y. Stein, E. Ruppin, R. B. Altman, and R. Sharan, "A method for inferring medical diagnoses from patient similarities," BMC medicine, vol. 11, p. 194, J. Sun, F. Wang, J. Hu, and S. Edabollahi, "Supervised patient similarity measure of heterogeneous patient records," ACM SIGKDD Explorations Newsletter, vol. 14, pp , K. Ng, J. Sun, J. Hu, and F. Wang, "Personalized predictive modeling and risk factor identification using patient similarity," AMIA Summits on Translational Science Proceedings, vol. 2015, p. 132, M. Saeed, M. Villarroel, A. T. Reisner, G. Clifford, L.-W. Lehman, G. Moody, et al., "Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): a public-access intensive care unit database," Critical care medicine, vol. 39, p. 952, T. F. Smith and M. S. Waterman, "Identification of common molecular subsequences," Journal of molecular biology, vol. 147, pp , P. Jaccard, Nouvelles recherches sur la distribution florale, R. Baeza-Yates and B. Ribeiro-Neto, Modern information retrieval vol. 463: ACM press New York, M. Rahman, F. Shad, and M. C. Smith, "Acute kidney injury: a guide to diagnosis and management," American family physician, vol. 86, pp , R. Bellomo, J. A. Kellum, and C. Ronco, "Acute kidney injury," The Lancet, vol. 380, pp , B. Goldstein, B. Giroir, and A. Randolph, "International pediatric sepsis consensus conference: Definitions for sepsis and organ dysfunction in pediatrics*," Pediatric critical care medicine, vol. 6, pp. 2-8, R. S. Watson, J. A. Carcillo, W. T. Linde-Zwirble, G. Clermont, J. Lidicker, and D. C. Angus, "The epidemiology of severe sepsis in children in the United States," American journal of respiratory and critical care medicine, vol. 167, pp , 2003.
27 Acknowledgements Bio-MIBLab Emory - Georgia Tech Cancer Center for Nanotechnology Excellence Institute for People and Technology (IPaT), Georgia Tech
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