Acceptance of female urologists among patients with suspected prostate disease

Steinkohl, Fabian; Luger, Anna; Grüber, L.; Hochleitner, Margarethe; Pichler, Renate; Heidegger-Pircher, Isabel; Bektic, Jasmin

doi:10.21037/tau-21-131

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…However, the diagnosis of prostate cancer is often followed by immediate decline in mental and physical health 3,4 . Limited knowledge regarding the disease, embarrassment around physical examination such as rectal exam and discussion of sexual symptoms with healthcare professionals, potentially exacerbated depending on the provider gender 5,6 , are known causes of poor outcome among older patients and/or those belonging ethnic minorities [7][8][9] . These uncertainties and anxiety related to sexual symptoms could lead patients to search for information through online search engines in which the patient may end-up with either incomplete, contradictory, misleading, and/or inaccurate information 10 .…”

Section: Introductionmentioning

confidence: 99%

Domain-specific LLM Development and Evaluation – A Case-study for Prostate Cancer

Tariq,

Luo,

Urooj

et al. 2024

Preprint

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In this work, we present our strategy for developing domain-specific large language models which cover the vocabulary of the target domain and train on reliable sources of clinical information. Prostate cancer was chosen as a use-case for this study. We collected more than 1.8 million clinical notes and radiology and pathology reports for 15341 patients treated for prostate cancer in Mayo Clinic across three sites and outpatient clinics. In addition to domain-specific training data, we built domain-specific tokenizers and devised knowledge-guided training strategies for LLM development. During the self-supervised training, LLM was forced to predict domain-specific information by marking clinical terms using UMLS parser. We evaluated the model for downstream tasks of clinical information prediction and question answering using quantitative and user evaluation study to measure the accuracy, reliability and information completeness. We compared the domain-specific model against similarly sized general purpose model GPT-2 and a three-times larger domain specialized model. i.e., BioGPT. Our model outperformed GPT-2 on both tasks by a wide margin. Our model was also able to outperform BioGPT on clinical information prediction tasks and showed some advantages over BioGPT in question-answering tasks.

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