Utilizing Electronic Health Records for Predictive Modeling of Cancer Risk

EasyChair Preprint 13646

Abstract

Predictive modeling of cancer risk plays a crucial role in improving prevention, early detection, and personalized treatment strategies. With the increasing availability of electronic health records (EHRs), there is an opportunity to leverage these comprehensive and longitudinal datasets to develop accurate predictive models. This abstract provides an overview of the utilization of EHRs for the predictive modeling of cancer risk. It highlights the significance of EHRs in healthcare, the importance of predictive modeling in cancer risk assessment, and the potential benefits of utilizing EHRs for this purpose. The abstract also explores the challenges and considerations involved in utilizing EHRs, such as data quality, privacy concerns, and interpretability of predictive models. Furthermore, it discusses case studies and examples of research studies that have successfully employed EHRs for cancer risk prediction. Finally, this abstract presents future directions and potential impacts, including advancements in technology, integration of artificial intelligence, and the potential for improved precision and personalized cancer risk assessment. Overall, the utilization of EHRs for predictive modeling of cancer risk holds immense promise in enhancing cancer care and patient outcomes.

Keyphrases: Cancer Risk Prediction, Electronic Health Records, Targeted interventions, personalized, risk assessment

@booklet{EasyChair:13646,
  author    = {Elizabeth Henry},
  title     = {Utilizing Electronic Health Records for Predictive Modeling of Cancer Risk},
  howpublished = {EasyChair Preprint 13646},
  year      = {EasyChair, 2024}}