Prediction of American Society of Anesthesiologists Physical Status Classification from preoperative clinical text narratives using natural language processing

ABSTRACT:

Background

Electronic health records (EHR) contain large volumes of unstructured free-form text notes that richly describe a patient’s health and medical comorbidities. It is unclear if perioperative risk stratification can be performed directly from these notes without manual data extraction. We conduct a feasibility study using natural language processing (NLP) to predict the American Society of Anesthesiologists Physical Status Classification (ASA-PS) as a surrogate measure for perioperative risk. We explore prediction performance using four different model types and compare the use of different note sections versus the whole note. We use Shapley values to explain model predictions and analyze disagreement between model and human anesthesiologist predictions.

Methods

S

SUBMITTER: Chung P 

PROVIDER: S-EPMC10476287 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature