Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
This work addresses the challenge of understanding and predicting personalized disease progression for patients with comorbidities, representing an incremental advancement in medical AI.
The authors tackled the problem of modeling temporal relationships between comorbid diseases from event data by developing deep diffusion processes (DDP) to create dynamic comorbidity networks, achieving accurate risk prediction and intelligible disease pathology representation in cancer registry experiments.
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. Learning such temporal patterns from event data is crucial for understanding disease pathology and predicting prognoses. To this end, we develop deep diffusion processes (DDP) to model "dynamic comorbidity networks", i.e., the temporal relationships between comorbid disease onsets expressed through a dynamic graph. A DDP comprises events modelled as a multi-dimensional point process, with an intensity function parameterized by the edges of a dynamic weighted graph. The graph structure is modulated by a neural network that maps patient history to edge weights, enabling rich temporal representations for disease trajectories. The DDP parameters decouple into clinically meaningful components, which enables serving the dual purpose of accurate risk prediction and intelligible representation of disease pathology. We illustrate these features in experiments using cancer registry data.