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Predictive Healthcare: Leveraging GPT-Driven Language Models for Anticipating Patient Needs

EasyChair Preprint no. 12963

11 pagesDate: April 9, 2024


Predictive healthcare, empowered by GPT-driven language models, has emerged as a transformative approach for anticipating patient needs and optimizing healthcare delivery. This article explores the utilization of Generative Pre-trained Transformers (GPT) in predictive healthcare, elucidating their capabilities, applications, and implications in modern healthcare settings.  
The paper begins by defining predictive healthcare and introducing the concept of GPT- driven language models. It explores how GPT models, trained on vast amounts of healthcare data, enable healthcare systems to analyze patient information, anticipate future health outcomes, and proactively address patient needs.  
Furthermore, the article investigates the multifaceted applications of GPT-driven language models in predictive healthcare, spanning diverse domains such as risk prediction, disease prevention, treatment planning, and resource allocation. By leveraging the predictive capabilities of GPT models, healthcare providers can identify high-risk patients, tailor interventions, and optimize healthcare resources to improve patient outcomes.

Keyphrases: Healthcare, Leveraging GPT, Technology

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Shophia Lorriane},
  title = {Predictive Healthcare: Leveraging GPT-Driven Language Models for Anticipating Patient Needs},
  howpublished = {EasyChair Preprint no. 12963},

  year = {EasyChair, 2024}}
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