Developing and Evaluating an AI-Assisted Prediction Model for Unplanned Intensive Care Admissions following Elective Neurosurgery using Natural Language Processing within an Electronic Healthcare Record System
Journal:
arXiv
Published Date:
Mar 13, 2025
Abstract
Introduction: Timely care in a specialised neuro-intensive therapy unit (ITU)
reduces mortality and hospital stays, with planned admissions being 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) and predict ITU
admissions for elective surgery patients. Methods: This study analysed the EHRs
of elective neurosurgery patients from University College London Hospital
(UCLH) using NLP. Patients were categorised into planned high dependency unit
(HDU) or ITU admission; unplanned HDU or ITU admission; or ward / overnight
recovery (ONR). The Medical Concept Annotation Tool (MedCAT) was used to
identify SNOMED-CT concepts within the clinical notes. We then explored the
utility of these identified concepts for a range of AI algorithms trained to
predict ITU admission. Results: The CogStack-MedCAT NLP model, initially
trained on hospital-wide EHRs, underwent two refinements: first with data from
patients with Normal Pressure Hydrocephalus (NPH) and then with data from
Vestibular Schwannoma (VS) patients, achieving a concept detection F1-score of
0.93. This refined model was then used to extract concepts from EHR notes of
2,268 eligible neurosurgical patients. We integrated the extracted concepts
into AI models, including a decision tree model and a neural time-series model.
Using the simpler decision tree model, we 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%. Conclusion: The NLP model, refined for
accuracy, has proven its efficiency in extracting relevant concepts, providing
a reliable basis for predictive AI models to use in clinically valid
applications.
