DR-GPT : A large language model for medical report analysis of diabetic retinopathy patients

Joel Jaskari, Jaakko Sahlsten, Paula Summanen, Jukka Moilanen, Erika Lehtola, Marjo Aho, Elina Säpyskä, Kustaa Hietala, Kimmo Kaski*

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

15 Lataukset (Pure)

Abstrakti

Diabetic retinopathy (DR) is a sight-threatening condition caused by diabetes. Screening programmes for DR include eye examinations, where the patient’s fundi are photographed, and the findings, including DR severity, are recorded in the medical report. However, statistical analyses based on DR severity require structured labels that calls for laborious manual annotation process if the report format is unstructured. In this work, we propose a large language model DR-GPT for classification of the DR severity from unstructured medical reports. On a clinical set of medical reports, DR-GPT reaches 0.975 quadratic weighted Cohen’s kappa using truncated Early Treatment Diabetic Retinopathy Study scale. When DR-GPT annotations for unlabeled data are paired with corresponding fundus images, the additional data improves image classifier performance with statistical significance. Our analysis shows that large language models can be applied for unstructured medical report databases to classify diabetic retinopathy with a variety of applications.

AlkuperäiskieliEnglanti
Artikkelie0297706
Sivut1-14
Sivumäärä14
JulkaisuPloS one
Vuosikerta19
Numero10 October
DOI - pysyväislinkit
TilaJulkaistu - lokak. 2024
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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