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Published on: September 19, 2025

Automated Identification of Cardiopulmonary Disease Cases for Preoperative Risk Stratification Using Machine

Ishan Aggarwal¹, Christopher Rhee, Mamta Chura

¹From the Department of Anesthesiology and Perioperative Medicine, Medical College of Georgia, Augusta University, Augusta, Georgia.

|April 15, 2026

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A clinical insight bot efficiently extracts cardiovascular risk signals from preoperative notes, improving anesthetic planning. This AI tool offers a 100x efficiency gain, reducing manual review time while maintaining high precision and low false positive rates.

Area of Science:

Artificial Intelligence in Medicine
Clinical Informatics
Natural Language Processing

Background:

Preoperative chart review is time-consuming and error-prone, especially for cardiopulmonary conditions impacting anesthetic planning.
Current methods struggle to efficiently identify critical cardiovascular risk signals from extensive documentation.

Purpose of the Study:

To develop and evaluate a guideline-aligned "clinical insight bot" to automatically surface perioperative cardiovascular risk signals from free-text clinical notes.
To improve the efficiency and accuracy of preoperative risk assessment for noncardiac surgery.

Main Methods:

Analyzed 1000 de-identified cases from the PhysioNet MIMIC database.
Utilized regex-based NLP for medical terminology extraction and TF-IDF/semantic embeddings for text feature encoding.

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Last Updated: Apr 17, 2026

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Trained and compared four machine learning models (Logistic Regression, Random Forest, SVM, Naive Bayes) using stratified fivefold cross-validation.

Main Results:

A linear Support Vector Machine (SVM) achieved the best performance (F1 score ≈ 0.71) in classifying "cardiopulmonary-only" versus "mixed/other" cases.
The model demonstrated high precision (0.94) and a very low false positive rate (≈0.6%), with false negatives as the primary error.
The pipeline processed documents rapidly, yielding an estimated 100x efficiency gain compared to manual review.

Conclusions:

A guideline-aligned "clinical insight bot" can effectively transform unstructured preoperative notes into actionable prompts for cardiovascular risk signals.
The bot's high precision and low false positive rate support safe integration into anesthesiology workflows, minimizing alert fatigue.
Future work includes multicenter validation and structured data fusion to enhance sensitivity and evaluate clinical outcomes.