Improving Clinical Records with Generative Model Techniques

EasyChair Preprint 14161

Abstract

In the realm of healthcare, accurate and comprehensive clinical records are crucial for effective patient management and treatment. However, traditional methods of maintaining and updating these records often face challenges related to data completeness, consistency, and integration. This paper explores the application of generative model techniques to enhance the quality and usability of clinical records. By leveraging advanced algorithms such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), we propose a novel approach to synthesizing and augmenting clinical data. These generative models are employed to address data gaps, generate realistic patient scenarios, and improve the robustness of clinical record systems. The proposed techniques not only facilitate better data representation but also enable more accurate predictive analytics and decision support. Through a series of case studies and evaluations, we demonstrate the effectiveness of these methods in improving the fidelity of clinical records and their subsequent impact on patient care and outcomes. This research underscores the potential of generative models to transform clinical data management and pave the way for more advanced, data-driven healthcare solutions.

Keyphrases: Data Imputation, Synthetic Data Generation, anomaly detection, clinical records, data augmentation, generative models

@booklet{EasyChair:14161,
  author    = {John Owen},
  title     = {Improving Clinical Records with Generative Model Techniques},
  howpublished = {EasyChair Preprint 14161},
  year      = {EasyChair, 2024}}