Patient Phenotyping
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Benchmarks
HiRID
Most implemented
Discovering multiple antibiotic resistance phenotypes using diverse top-k subgroup list discovery
POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study
A methodology based on Trace-based clustering for patient phenotyping
HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
Papers
A variational Bayes latent class approach for EHR-based patient phenotyping in R
The VBphenoR package for R provides a closed-form variational Bayes approach to patient phenotyping using Electronic Health Records (EHR) data. We implement a variational Bayes Gaussian Mixture Model (GMM) algorithm usin…
Patient PhenotypingDANIEL: A Distributed and Scalable Approach for Global Representation Learning with EHR Applications
Classical probabilistic graphical models face fundamental challenges in modern data environments, which are characterized by high dimensionality, source heterogeneity, and stringent data-sharing constraints. In this work…
Representation LearningPatient PhenotypingDiscovering multiple antibiotic resistance phenotypes using diverse top-k subgroup list discovery
Antibiotic resistance is one of the major global threats to human health and occurs when antibiotics lose their ability to combat bacterial infections. In this problem, a clinical decision support system could use phenot…
Data MiningDecision MakingDiverse Top-k Subgroup List DiscoveryPatient Phenotyping+1Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all treatment with limited success in persistent AF. This may be due to our inability to map the dynamics of AF with the limited resolution and co…
Patient PhenotypingM3H: Multimodal Multitask Machine Learning for Healthcare
Developing an integrated many-to-many framework leveraging multimodal data for multiple tasks is crucial to unifying healthcare applications ranging from diagnoses to operations. In resource-constrained hospital environm…
Binary ClassificationPatient PhenotypingTime SeriesCollaborative learning of common latent representations in routinely collected multivariate ICU physiological signals
In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping. In contrast to previous research focused on electronic health rec…
Collaborative FilteringPatient PhenotypingTime Series