AI assisted prediction of unplanned intensive care admissions using natural language processing in elective neurosurgery

Abstract

Timely care in a specialised neuro-intensive therapy unit (ITU) reduces mortality and hospital stays. Planned admissions to ITU following surgery are safer than unplanned ones. However, post-operative care decisions remain subjective. This study used artificial intelligence (AI), specifically natural language processing (NLP) to analyse electronic health records (EHRs) of elective neurosurgery patients from University College London Hospital (UCLH) and predict ITU admissions. Using a refined CogStack-MedCAT NLP model, we extracted clinical concepts from 2268 patient records and trained AI models to classify admissions into ward and ITU. The Random Forest model achieved a recall of 0.87 (CI 0.82-0.91) for ITU admissions, reducing the proportion of unplanned ITU cases missed by human experts from 36% to 4%. Interpretability analysis confirmed the use of clinically relevant concepts. The study highlights the opportunity for AI to aid in allocating resources for neurosurgical patients but requires further research and integration into practice.

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