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.

The authors have declared no competing interest.

This study did not receive any funding

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Internal Review Board (IRB) of Mayo Clinic gave ethical approval for this work

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All data produced in the present study are available upon reasonable request to the authors