Prof. Dr. Erwin Böttinger

Data-driven patient subgroup identification and outcome prediction in unspecific back pain patients

Jan Philipp Sachs

Unspecific back pain is one of the most burdensome and expensive health conditions people and economies are facing worldwide. It represents a condition where no clear underlying pathology can be identified. Thus, disease mechanisms, complicating medical conditions, and differentiation in its treatment are subject to a more in-depth investigation. This project aims at identifying new data-driven subtypes and at learning about the treatment processes of patients with unspecific back pain. To that end, a large database of electronic health records is used to perform phenotyping, process mining and clustering on a back pain-specific subset of these data. A focus of the evaluation is to identify patterns that lead to unfavorable outcomes such as opioid usage, frequent recurrence and chronification of the symptoms.