MatrixFlow: Temporal Network Visual Analytics to Track Symptom Evolution during Disease Progression
Our paper was presented at AMIA 2012 in Chicago in November 2012. Citation: Adam Perer, Jimeng Sun. MatrixFlow: Temporal Network Visual Analytics to Track Symptom Evolution during Disease Progression. American Medical Informatics Association Annual Symposium (AMIA 2012). Chicago, Illinois. (2012). Objective: To develop a visual analytic system to help medical professionals improve disease diagnosis by providing insights for understanding disease progression. Methods: We develop MatrixFlow, a visual analytic system that takes clinical event sequences of patients as input, constructs time-evolving networks and visualizes them as a temporal flow of matrices. MatrixFlow provides several interactive features for analysis: 1) one can sort the events based on the similarity in order to accentuate underlying cluster patterns among those events; 2) one can compare co-occurrence events over time and across cohorts through additional line graph visualization. Results: MatrixFlow is applied to visualize heart failure (HF) symptom events extracted from a large cohort of HF cases and controls (n=50,625), which allows medical experts to reach insights involving temporal patterns and clusters of interest, and compare cohorts in novel ways that may lead to improved disease diagnoses. Conclusions: MatrixFlow is an interactive visual analytic system that allows users to quickly discover patterns in clinical event sequences. By unearthing the patterns hidden within and displaying them to medical experts, users become empowered to make decisions influenced by historical patterns.
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MatrixFlow: Temporal Network Visual Analytics to Track Symptom Evolution during Disease Progression
- 1. MatrixFlow Temporal Network Visual Analytics to Track Symptom Evolution during Disease Progression Adam Perer Jimeng Sun Healthcare Analytics Research Group IBM T.J. Watson Research Center
- 2. Overview •Visual Analytics for Data-Driven Healthcare Text Mining • To extract symptoms from clinical notes Social Network Analysis • To model co-occurence symptom networks Visualization • To enable clinicians to reach insights
- 3. Challenge •Difficult to Diagnose Diseases •Co-morbidities may mask presence. •Physicians often use clinical knowledge without quantitative data from Electronic Medical Records. •There are few analytical tools to extract meaningful insights.
- 4. Heart Failure •Potentially fatal disease that affects 2% of adults in developed countries •Difficult to manage •No systematic diagnostic criteria •Framingham HF Diagnosis based on •2 major criteria •1 major criteria & 2 minor criteria Heart designed by Catherine Please from The Noun Project
- 5. Symptoms Framingham Criteria Rales Radiographic Cardiomegaly Acute Pulmonary Edema Hepatojugular Reflex Bilateral Ankle Edema Nocturnal Cough Dyspnea on Ordinary Exertion Hepatomegaly Pleural Effusion
- 6. Population •50,625 Patients (Geisinger Clinic PCPs) •4,644 case patients • 1,200 with preserved ejection fraction • 1,615 with reduced ejection fraction • 45,981 control patients
- 7. Text Mining •3.3 million Clinical Notes (4 GB of Text) •NLP used to identify Framingham screenshot of part of a clinical note. criteria •900,000 positive •3.6 million negative •97% of HF cases met criteria extracted •8% of controls met Jimeng Sun, PhD criteria extracted
- 8. Text Mining •3.3 million Clinical Notes (4 GB of Text) •NLP used to identify Framingham screenshot of part of a clinical note. criteria •900,000 positive •3.6 million negative •97% of HF cases met criteria extracted •8% of controls met Jimeng Sun, PhD criteria extracted
- 9. Sequence HF Diagnosis Ankle Edema Medication 1 DO Exertion Medication 2 DO Exertion AP Edema Medication 3
- 10. Sequence HF Diagnosis DO Exertion Ankle Edema DO Exertion AP Edema Medication 1 Medication 2 Medication 3 Year 1 Year 2 Year 3
- 11. Network HF Diagnosis DO Exertion Ankle Edema DO Exertion AP Edema Medication 1 Medication 2 Medication 3 Year 1 Year 2 Year 3
- 12. Network Ankle Edema HF Diagnosis Medication 1 DO Exertion DO Exertion AP Edema Medication 2 Medication 3 Year 1 Year 2 Year 3
- 13. Network Ankle Edema DO Exertion HF Diagnosis Medication 1 Medication 2 DO Exertion AP Edema Medication 3 Year 1 Year 2 Year 3
- 14. Network Ankle Edema DO Exertion DO Exertion AP Edema Medication 1 Medication 2 Medication 3 Year 1 Year 2 Year 3
- 15. Network Year 1 Year 2 Year 3
- 16. xperienced Symptom1 and Symptom 3 in the same time interval, then there would not be an edge Clustering those two events. This co-occurrence network is computed using our advanced network modeling k, Orion10. As our networks now feature edges that have varying edge weights, our matrix visualiza e color of each cell according to a sequential color scale representing the edge weight value (such as e shown at the bottom of Figure 6). •Visualization reveals clusters of g Clinical Event Networks clinical events ualizing matrices, it is important to choose an effective method to sort the order of nodes in order to atterns as possible4. As we wish to reveal clusters of clinical events, we employ a greedy hierarchica optimizing Newman’s modularity metric17. From this algorithm, we are able to obtain a sort order t •Hierarchical Clustering (Newman’s the distance among connected nodes by ordering the nodes according to the cluster tree produced b al clustering algorithm. Figure 4 shows an example of matrix visualization before and after the sorti modularity matrix) In the latter example, well-connected nodes (APEdema, DOExertion, and Medication 3) assemble ge box-like structure in the visualization, which makes the cluster more apparent than in the unsorte •Determines sort order for matrices the left.
- 17. Population •Align by diagnosis date •Aggregate patient event networks •Split by user-chosen intervals Patient 1 A B C Diagnosis Patient 2 B C E Diagnosis Patient 3 A B D Diagnosis Patient 4 C D E Diagnosis January 2012 November 2012
- 18. Population •Align by diagnosis date •Aggregate patient event networks •Split by user-chosen intervals A B C Diagnosis B C E Diagnosis A B D Diagnosis C D E Diagnosis
- 19. Population •Align by diagnosis date •Aggregate patient event networks •Split by user-chosen intervals A B C Diagnosis B C E Diagnosis A B D Diagnosis C D E Diagnosis
- 20. Population •Align by diagnosis date •Aggregate patient event networks •Split by user-chosen intervals
- 21. Population •Align by diagnosis date •Aggregate patient event networks •Split by user-chosen intervals
- 22. Population •Align by diagnosis date •Aggregate patient event networks •Split by user-chosen intervals
- 23. Cohorts Positive Mentions of Framingham Symptoms
- 24. Cohorts •Show temporal trend graphic (e.g. Figure 7 here, but with all 3 matrix
- 25. Cohorts •Show temporal trend graphic (e.g. Figure 7 here, but with all 3 matrix
- 26. Evaluation •4 Medical Scientists •Cardiologist, ER, ER, Epidemiologist • “Help clinicians make earlier diagnoses” • “Help prioritize preventative strategies to avoid onset of HF”
- 27. Insights • “Symptoms of HF are documented months to years preceding clinical diagnosis but are also present in non-HF patients.” • “Rapid increase in HF symptoms are more prominent in cases rather than in matched controls.” • “MatrixFlow can contribute to the further refinement of diagnostic criteria and may allow for earlier prediction of HF in a primary care population.”
- 28. MatrixFlow Thanks! adam.perer@us.ibm.com http://www.research.ibm.com/healthcare Adam Perer and Jimeng Sun Healthcare Analytics Research Group IBM T.J. Watson Research Center