Introducing the Large Medical Model: State of the art healthcare cost and risk prediction with transformers trained on patient event sequences
<|reference_start|>Introducing the Large Medical Model: State of the art healthcare cost and risk prediction with transformers trained on patient event sequences: With U.S. healthcare spending approaching $5T (NHE Fact Sheet 2024), and 25% of it estimated to be wasteful (Waste in the US the health care system: estimated costs and potential for savings, n.d.), the need to better predict risk and optimal patient care is evermore important. This paper introduces the Large Medical Model (LMM), a generative pre-trained transformer (GPT) designed to guide and predict the broad facets of patient care and healthcare administration. The model is trained on medical event sequences from over 140M longitudinal patient claims records with a specialized vocabulary built from medical terminology systems and demonstrates a superior capability to forecast healthcare costs and identify potential risk factors. Through experimentation and validation, we showcase the LMM's proficiency in not only in cost and risk predictions, but also in discerning intricate patterns within complex medical conditions and an ability to identify novel relationships in patient care. The LMM is able to improve both cost prediction by 14.1% over the best commercial models and chronic conditions prediction by 1.9% over the best transformer models in research predicting a broad set of conditions. The LMM is a substantial advancement in healthcare analytics, offering the potential to significantly enhance risk assessment, cost management, and personalized medicine.<|reference_end|>
@article{sahu2024introducing,
    title={Introducing the Large Medical Model: State of the art healthcare cost
  and risk prediction with transformers trained on patient event sequences},
    author={Ricky Sahu, Eric Marriott, Ethan Siegel, David Wagner, Flore Uzan,
  Troy Yang, Asim Javed},
    journal={arXiv preprint arXiv:2409.13000},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13000},
    primaryClass={cs.LG cs.AI stat.AP stat.ML}
}