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Found 11 papers, 7 papers with code
Bias patterns in the application of LLMs for clinical decision support: A comprehensive study
Large Language Models (LLMs) have emerged as powerful candidates to inform clinical decision-making processes.
HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks
While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format.
Multimodal Sleep Apnea Detection with Missing or Noisy Modalities
Our experiments show that the proposed model outperforms other state-of-the-art approaches in sleep apnea detection using various subsets of available data and different levels of noise, and maintains its high performance (AUROC>0. 9) even in the presence of high levels of noise or missingness.
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods
Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes applications such as those in healthcare.
An Extensive Data Processing Pipeline for MIMIC-IV
An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical purposes.
Who will Leave a Pediatric Weight Management Program and When? -- A machine learning approach for predicting attrition patterns
Childhood obesity is a major public health concern.
Subtyping patients with chronic disease using longitudinal BMI patterns
Obesity is a major health problem, increasing the risk of various major chronic diseases, such as diabetes, cancer, and stroke.
Identifying the Leading Factors of Significant Weight Gains Using a New Rule Discovery Method
Overweight and obesity remain a major global public health concern and identifying the individualized patterns that increase the risk of future weight gains has a crucial role in preventing obesity and numerous sub-sequent diseases associated with obesity.
Time-series Imputation and Prediction with Bi-Directional Generative Adversarial Networks
Multivariate time-series data are used in many classification and regression predictive tasks, and recurrent models have been widely used for such tasks.
Obesity Prediction with EHR Data: A deep learning approach with interpretable elements
Childhood obesity is a major public health challenge.
Multi-modal Predictive Models of Diabetes Progression
In this study, we have used a dataset related to 63 patients with T2D that includes the data from two different types of wearable devices worn by the patients: continuous glucose monitoring (CGM) devices and activity trackers (ActiGraphs).
