Patient Journey Analysis for Medical Knowledge Discovery and Clinical Decision Making

Physicians use their clinical experience to identify patterns (such as symptoms) that are common among a group of patients, leading to treatment strategies. However, rather than having to base judgement on  few patients, digital medical information allows the inspection of data from thousands if not Millions of patients at the same time. In order to compare patients at this scale, we need to teach computers to form a robust understanding of patient similarity, a non-trivial skill that relies on matching multi-dimensional and multi-modal patient features that are captured at different times and resolutions. These features form unique patient journeys that chronicle healthcare interventions and record patient outcomes. Measuring the similarities among those journeys enables the discovery of common health states that precede disease development. In order to explore and understand these states, we need to create visual tools that make it possible to inspect the patient journeys for a better understanding of possible causes of disease. Contact Prof. Dr.