Unraveling Complex Temporal Patterns in EHRs via Robust Irregular Tensor Factorization
Electronic health records (EHRs) contain diverse patient data with varying visit frequencies, resulting in unaligned tensors in the time mode. While PARAFAC2 has been used for extracting meaningful medical concepts from EHRs, existing methods fail to capture non-linear and complex temporal patterns and struggle with missing entries. In this paper, we propose REPAR, an RNN Regularized Robust PARAFAC2 method to model complex temporal dependencies and enhance robustness in the presence of missing data. Our approach employs RNNs for temporal regularization and a low-rank constraint for robustness. We design a hybrid optimization framework that handles multiple regularizations and supports various data types. REPAR is evaluated on 3 real-world EHR datasets, demonstrating improved reconstruction and robustness under missing data. Two case studies further showcase REPAR's ability to extract meaningful dynamic phenotypes and enhance phenotype predictability from noisy temporal EHRs.
Learning Objectives
- Explain how REPAR extends the PARAFAC2 framework by incorporating recurrent neural networks (RNNs) to model temporal dependencies in electronic health record (EHR) data.
- Analyze how REPAR effectively handles missing and irregular EHR data to improve data quality and model performance.
- Evaluate the ability of REPAR to extract clinically meaningful phenotypes for patient subgrouping and disease progression analysis.
- Apply the REPAR framework to identify patient subgroups and track disease progression based on complex, time-dependent clinical data.
Speaker
- Linghui Zeng, MS (Emory University)
Systematic Exploration of Hospital Cost Variability: A Conformal Prediction-Based Outlier Detection Method for Electronic Health Records
Marked variability in inpatient hospitalization costs poses significant challenges to healthcare quality, resource allocation, and patient outcomes. Traditional methods like Diagnosis-Related Groups (DRGs) aid in cost management but lack practical solutions for enhancing hospital care value. We introduce a novel methodology for outlier detection in Electronic Health Records (EHRs) using Conformal Prediction. This approach identifies and prioritizes areas for optimizing high-value care processes. Unlike conventional predictive models that neglect uncertainty, our method employs Conformal Quantile Regression (CQR) to generate robust prediction intervals, offering a comprehensive view of cost variability. By integrating Conformal Prediction with machine learning models, healthcare professionals can more accurately pinpoint opportunities for quality and efficiency improvements. Our framework systematically evaluates unexplained hospital cost variations and generates interpretable hypotheses for refining clinical practices associated with atypical costs. This data-driven approach offers a systematic method to generate clinically sound hypotheses that may inform processes to enhance care quality and optimize resource utilization.
Learning Objectives
- Describe the challenges posed by variability in inpatient hospitalization costs and the limitations of traditional cost management methods like Diagnosis-Related Groups (DRGs).
- Analyze the use of Conformal Quantile Regression (CQR) to generate robust prediction intervals that account for uncertainty in cost variability.
Speaker
- François Grolleau, MD, PhD (Stanford Center for Biomedical Informatics Research)
powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities
Rigorous external validation is crucial for assessing the generalizability of prediction models, particularly by evaluating their discrimination (AUROC) on new data. This often involves comparing a new model's AUROC to that of an established reference model. However, many studies rely on arbitrary rules of thumb for sample size calculations, often resulting in underpowered analyses and unreliable conclusions. This paper reviews crucial concepts for accurate sample size determination in AUROC-based external validation studies, making the theory and practice more accessible to researchers and clinicians. We introduce powerROC, an open-source web tool designed to simplify these calculations, enabling both the evaluation of a single model and the comparison of two models. The tool offers guidance on selecting target precision levels and employs flexible approaches, leveraging either pilot data or user-defined probability distributions. We illustrate powerROC’s utility through a case study on hospital mortality prediction using the MIMIC database.
Learning Objectives
- Understand that in healthcare, robust external validation of prediction models often has greater impact than developing new ones
Speaker
- François Grolleau, MD, PhD (Stanford Center for Biomedical Informatics Research)
Continuing Education Credit
Physicians
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this online enduring material for 1.0 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Claim credit no later than March 10, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after March 10, 2028.
ACHIPsTM
AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.
ACHIPsTM may use CME/CNE certificates or the ACHIPsTM Recertification Log to report 2025 Summit sessions attended for ACHIPsTM Recertification.
Claim credit no later than within three years of the release date or within two years of your purchase date, whichever is sooner.