Ive, Julia;
Olukoya, Olatomiwa;
Funnell, Jonathan P;
Booker, James;
Lam, Sze HM;
Reddy, Ugan;
Noor, Kawsar;
... Marcus, Hani J; + view all
(2025)
AI assisted prediction of unplanned intensive care admissions using natural language processing in elective neurosurgery.
npj Digital Medicine
, 8
, Article 549. 10.1038/s41746-025-01952-0.
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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.
| Type: | Article |
|---|---|
| Title: | AI assisted prediction of unplanned intensive care admissions using natural language processing in elective neurosurgery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41746-025-01952-0 |
| Publisher version: | https://doi.org/10.1038/s41746-025-01952-0 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| Keywords: | Health services, Neurology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10212831 |
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