Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月27日

查看全文
  • 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

摘要

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.