Patient Phenotyping
6 papers with code • 1 benchmarks • 1 datasets
Classifying patients after 24h regarding their admission diagnosis, using the APACHE group II and IV labels.
Benchmarks
These leaderboards are used to track progress in Patient Phenotyping
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HiRID
Most implemented papers
Benchmarking machine learning models on multi-centre eICU critical care dataset
This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model.
Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping
In the five chief complaints, we find between 2 and 6 clusters, with the peak mean pairwise ARI between subsequent training iterations to range from 0. 35 to 0. 74.
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e. g., adverse events, the onset of comorbidities).
HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data
ratschlab/HIRID-ICU-Benchmark
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NeurIPS Datasets and Benchmarks 2021
The recent success of machine learning methods applied to time series collected from Intensive Care Units (ICU) exposes the lack of standardized machine learning benchmarks for developing and comparing such methods.
A methodology based on Trace-based clustering for patient phenotyping
Methods: We propose a new unsupervised machine learning technique, denominated as Trace-based clustering, and a 5-step methodology in order to support clinicians when identifying patient phenotypes.
POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study
We describe a method for phenotype recognition that imputes phenotype codes for all UK Biobank participants.
