Evaluating ChatGPT text-mining of clinical records for obesity monitoring

• URL: http://arxiv.org/abs/2308.01666v1
• Date: Thu, 3 Aug 2023 10:11:42 GMT
• Title: Evaluating ChatGPT text-mining of clinical records for obesity monitoring
• Authors: Ivo S. Fins (1), Heather Davies (1), Sean Farrell (2), Jose R.Torres (3), Gina Pinchbeck (1), Alan D. Radford (1), Peter-John Noble (1) ((1) Small Animal Veterinary Surveillance Network, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK, (2) Department of Computer Science, Durham University, Durham, UK, (3) Institute for Animal Health and Food Safety, University of Las Palmas de Gran Canaria, Las Palmas, Canary Archipelago, Spain)
• Abstract summary: We compare the ability of a large language model (ChatGPT) and a previously developed regular expression (RegexT) to identify overweight anonymised body condition scores (BCS) in veterinary narratives.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Veterinary clinical narratives remain a largely untapped resource for addressing complex diseases. Here we compare the ability of a large language model (ChatGPT) and a previously developed regular expression (RegexT) to identify overweight body condition scores (BCS) in veterinary narratives. Methods: BCS values were extracted from 4,415 anonymised clinical narratives using either RegexT or by appending the narrative to a prompt sent to ChatGPT coercing the model to return the BCS information. Data were manually reviewed for comparison. Results: The precision of RegexT was higher (100%, 95% CI 94.81-100%) than the ChatGPT (89.3%; 95% CI82.75-93.64%). However, the recall of ChatGPT (100%. 95% CI 96.18-100%) was considerably higher than that of RegexT (72.6%, 95% CI 63.92-79.94%). Limitations: Subtle prompt engineering is needed to improve ChatGPT output. Conclusions: Large language models create diverse opportunities and, whilst complex, present an intuitive interface to information but require careful implementation to avoid unpredictable errors.

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