Patient Phenotyping

5 papers with code • 1 benchmarks • 1 datasets

Classifying patients after 24h regarding their admission diagnosis, using the APACHE group II and IV labels.

Datasets


Most implemented papers

Benchmarking machine learning models on multi-centre eICU critical care dataset

mostafaalishahi/eICU_Benchmark 2 Oct 2019

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

nch08a/EDVizPhenotyping 5 Jul 2019

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

chl8856/AC_TPC ICML 2020

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 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

antoniolopezmc/A-methodology-based-on-Trace-based-clustering-for-patient-phenotyping Knowledge-Based Systems 2021

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.