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Visualization of longitudinal clinical trajectories using a graph-based approach
| Content Provider | ACM Digital Library |
|---|---|
| Author | Garbarino, Alexander Chen, Jian Caban, Jesus J Dabek, Filip |
| Abstract | The adoption of electronic health records (EHRs) and the increased participation of hospitals and clinics in health information exchange systems have resulted in unique longitudinal data that describes a patient's clinical trajectory. In-depth analysis of that information is important to better understand the general course to recovery or the evolution of a particular disease. Unfortunately, modelling and understanding the progression of a disease is still a challenging task given that most often patients take vastly different paths after being diagnosed with a specific disease or condition. The different trajectories patients follow, the individual temporal events that each patient goes through, the uncertainties associated with clinical diagnoses, and the irregular time intervals between clinical diagnoses present challenges to researchers trying to analyze the common trajectories of a set of N patients. This paper presents a graph-based visualization method to interactively analyze the longitudinal clinical trajectory of a group of patients. The system allows users to select a specific set of events or conditions, filter the data based on different thresholds, and compare different cohorts while using an interactive virtual space that expands as the user continues to analyze and explore the data. The system has been tested with a dataset of over 89,000 patients and 8.7 million clinical events. |
| Starting Page | 1 |
| Ending Page | 7 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781450336710 |
| DOI | 10.1145/2836034.2836039 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-10-25 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Healthcare data Longitudinal events Visual analytics Data visualization |
| Content Type | Text |
| Resource Type | Article |
