Skip to main content

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 2024 Symposium sessions attended for ACHIPsTM Recertification.

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.

FAQs

All content was recorded live at AMIA’s Annual Symposium event November 9-13, 2024, in San Francisco, CA. Plan now to join us for the next Annual Symposium!

Yes! Purchase the AMIA 2024 Annual Symposium On Demand Bundle to enjoy all recorded sessions available at the best value. Get the bundle.

Purchase the AMIA 2024 Annual Symposium On Demand Bundlefor the best value on all top 20 sessions. Additional individual sessions are also available for purchase in the catalog.

Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.

Yes! AMIA 2024 Annual Symposium On Demand is available for anyone to purchase. Become an AMIA member before you purchase to receive exclusive member discounts. Join AMIA today.

We’re glad you asked! AMIA offers a variety of membership options, all with exclusive benefits and abundant networking opportunities. Choose the membership that’s right for you.

The Audio-only format of all 20 sessions is available free of charge exclusively to AMIA members. Access the AMIA 2024 Annual Symposium On Demand Audio Library. Log in required.

Join us at the next Annual Symposium and engage with leaders from across the health informatics field. Learn more.

Yes! You can claim Self-Study credit when you complete AMIA 2024 Annual Symposium On Demand sessions, in addition to claiming Live credit for attending the live event. View the full details on self-study accreditation for this product.

Yes, The AMIA 2024 Annual Symposium On Demand Bundle (Presenter, Slides, and Audio) may be purchased for 8 educational credits using your health system’s code at checkout. Individual sessions (Presenter, Slides, and Audio) may be purchased for 1 educational credit per session using your health system’s code at checkout.

Available Until:
Dates and Times:
Type: AMIA On Demand
Course Format(s): On Demand
Credits:
1.00
CME
Price: Member: $60, Nonmember: $85
Purchase now
Share