Evidence map›Paper›PMID 40858789›Full record

ArticleNPJ digital medicine2025

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

Julia Ive, Olatomiwa Olukoya, Jonathan P Funnell, James Booker, Sze H M Lam, Ugan Reddy, Kawsar Noor, Richard Jb Dobson, Astri M V Luoma, Hani J Marcus

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Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Julia Ive *UCL Institute of Health Informatics, University College London, London, UK. j.ive@ucl.ac.uk.
Olatomiwa Olukoya *Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
Jonathan P FunnellDepartment of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
James BookerDepartment of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
Sze H M LamDepartment of Neuroanaesthesia and Neurocritical Care, National Hospital for Neurology and Neurosurgery, London, UK.
Ugan ReddyUCL Queen Square Institute of Neurology, University College London, London, UK.
Kawsar NoorUCL Institute of Health Informatics, University College London, London, UK.
Richard Jb DobsonUCL Institute of Health Informatics, University College London, London, UK.
Astri M V LuomaDepartment of Neuroanaesthesia and Neurocritical Care, National Hospital for Neurology and Neurosurgery, London, UK.
Hani J MarcusDepartment of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID40858789
PMCPMC12381004

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