PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization

TL;DR

PainPoints is a novel framework utilizing large language models to accurately classify chronic pain sub-types from patient interviews and generate customizable clinical summaries through expert-guided facet-based summarization.

Contribution

The paper introduces PainPoints, a new framework that combines sentence-level classification and expert-guided summarization for improved diagnosis and documentation of chronic pain conditions.

Findings

Achieved an AUC of 0.83 in pain sub-type classification

Developed a novel facet-based summarization approach

Demonstrated interpretability with a sufficiency-based method

Abstract

Chronic pain is a pervasive disorder which is often very disabling and is associated with comorbidities such as depression and anxiety. Neuropathic Pain (NP) is a common sub-type which is often caused due to nerve damage and has a known pathophysiology. Another common sub-type is Fibromyalgia (FM) which is described as musculoskeletal, diffuse pain that is widespread through the body. The pathophysiology of FM is poorly understood, making it very hard to diagnose. Standard medications and treatments for FM and NP differ from one another and if misdiagnosed it can cause an increase in symptom severity. To overcome this difficulty, we propose a novel framework, PainPoints, which accurately detects the sub-type of pain and generates clinical notes via summarizing the patient interviews. Specifically, PainPoints makes use of large language models to perform sentence-level classification of the text obtained from interviews of FM and NP patients with a reliable AUC of 0.83. Using a sufficiency-based interpretability approach, we explain how the fine-tuned model accurately picks up on the nuances that patients use to describe their pain. Finally, we generate summaries of these interviews via expert interventions by introducing a novel facet-based approach. PainPoints thus enables practitioners to add/drop facets and generate a custom summary based on the notion of "facet-coverage" which is also introduced in this work.