Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.

ABSTRACT:

Purpose

Identifying cancer symptoms in electronic health record (EHR) narratives is feasible with natural language processing (NLP). However, more efficient NLP systems are needed to detect various symptoms and distinguish observed symptoms from negated symptoms and medication-related side effects. We evaluated the accuracy of NLP in (1) detecting 14 symptom groups (ie, pain, fatigue, swelling, depressed mood, anxiety, nausea/vomiting, pruritus, headache, shortness of breath, constipation, numbness/tingling, decreased appetite, impaired memory, disturbed sleep) and (2) distinguishing observed symptoms in EHR narratives among patients with cancer.

Methods

We extracted 902,508 notes for 11,784 unique patients diagnosed with cancer and developed a gold standard corpus of 1,112

SUBMITTER: Albashayreh A 

PROVIDER: S-EPMC12493229 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature