Dataset Information

Evaluating ChatGPT text mining of clinical records for companion animal obesity monitoring.

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 pertaining to companion animals.

Methods

BCS values were extracted from 4415 anonymised clinical narratives using either RegexT or by appending the narrative to a prompt sent to ChatGPT, prompting the model to return the BCS information. Data were manually reviewed for comparison.

Results

The precision of RegexT was higher (100%, 95% confidence interval [CI] 94.81%-100%) than that of ChatGPT (89.3%, 95% CI 82.75%-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

Prior anonymisation and subtle prompt engineering are needed to improve ChatGPT output.

Conclusions

Large language models create diverse opportunities and, while complex, present an intuitive interface to information. However, they require careful implementation to avoid unpredictable errors.

SUBMITTER: Fins IS

PROVIDER: S-EPMC10952314 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

Evaluating ChatGPT text mining of clinical records for companion animal obesity monitoring.

Fins Ivo S IS Davies Heather H Farrell Sean S Torres Jose R JR Pinchbeck Gina G Radford Alan D AD Noble Peter-John PJ

The Veterinary record 20231206 3

<h4>Background</h4>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 pertaining to companion animals.<h4>Methods</h4>BCS values were extracted from 4415 anonymised clinical narratives using either RegexT or by appending the narrative to a prompt sent to Ch ...[more]

PMID: 38058223

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Dataset Information

Evaluating ChatGPT text mining of clinical records for companion animal obesity monitoring.

Background

Methods

Results

Limitations

Conclusions

Publications

Evaluating ChatGPT text mining of clinical records for companion animal obesity monitoring.

Similar Datasets

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