  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.
