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

2025-03-13

AI Summary

This study addresses the prediction of unplanned ICU admissions among patients undergoing elective neurosurgical procedures. Method: We propose an NLP-driven, interpretable predictive framework featuring a novel two-stage MedCAT fine-tuning strategy (NPH → VS) to enhance clinical concept recognition accuracy in neurosurgical text (F1 = 0.93); integrate SNOMED-CT ontology mapping with domain-informed clinical feature engineering; and uniquely combine temporal models (LSTM/Transformer) with decision trees to jointly capture longitudinal dynamics and ensure clinical interpretability. Contribution/Results: The decision tree component achieves a recall of 0.87, reducing human missed-detection rates from 36% to 4%. These results demonstrate the framework’s clinical utility for perioperative risk stratification and ICU resource optimization.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Predict unplanned ICU admissions post-elective neurosurgery using AI.
• Analyze EHRs with NLP to improve post-operative care decisions.
• Reduce missed unplanned ICU cases through AI-assisted prediction models.

Innovation

• AI-assisted prediction model using NLP
• MedCAT for SNOMED-CT concept identification
• Decision tree model with high recall accuracy