Mapping Patient Trajectories: Understanding and Visualizing Sepsis Prognostic Pathways from Patients Clinical Narratives
This work addresses the need for personalized sepsis care by providing insights into disease progression for healthcare providers, though it is incremental as it builds on existing NLP methods applied to clinical data.
The paper tackled the problem of understanding sepsis progression by developing prognostic pathways from clinical narratives, identifying patient subgroups based on comorbidities and using SHAP for explanations, which revealed dynamic trajectories and factors influencing disease transitions.
In recent years, healthcare professionals are increasingly emphasizing on personalized and evidence-based patient care through the exploration of prognostic pathways. To study this, structured clinical variables from Electronic Health Records (EHRs) data have traditionally been employed by many researchers. Presently, Natural Language Processing models have received great attention in clinical research which expanded the possibilities of using clinical narratives. In this paper, we propose a systematic methodology for developing sepsis prognostic pathways derived from clinical notes, focusing on diverse patient subgroups identified by exploring comorbidities associated with sepsis and generating explanations of these subgroups using SHAP. The extracted prognostic pathways of these subgroups provide valuable insights into the dynamic trajectories of sepsis severity over time. Visualizing these pathways sheds light on the likelihood and direction of disease progression across various contexts and reveals patterns and pivotal factors or biomarkers influencing the transition between sepsis stages, whether toward deterioration or improvement. This empowers healthcare providers to implement more personalized and effective healthcare strategies for individual patients.