Deep learning model for sepsis prediction using high-frequency data
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Updated
May 5, 2019 - Python
Deep learning model for sepsis prediction using high-frequency data
The early prediction of sepsis is potentially life-saving, and we challenge participants to predict sepsis 6 hours before the clinical prediction of sepsis.
IEEE BIBM 2021: Bayesian optimization-guided topic modeling for automatic detection of sepsis-related events from free text
Code and Datasets for the paper "An Interpretable Risk Prediction Model for Healthcare with Pattern Attention", published on BMC Medical Informatics and Decision Making.
Code and Datasets for the paper "Estimating Individual Treatment Effects with Time-Varying Confounders", published on ICDM 2020.
Data processing scripts for analysis of targeted metagenomics sequencing data. Intended for use with multiplexed Illumina short-read data generated after enrichment.
A Python program for automated calculation and selection of fluids to manage conditions, e.g. hypernatremia, severe sepsis, severe dehydration, some dehydration, along with deducing MAP and drop rates easily.
Early Prediction of Sepsis using Time Seiries Forecasting (Published at 2023 IEEE AI4Health)
One of the top solutions for The 2019 DII National Data Science Challenge: https://sbmi.uth.edu/dii-challenge/. More details in the paper "An interpretable deep-learning model for early prediction of sepsis in the emergency department", published on Patterns 2021.
Code and Datasets for the paper "Identifying Sepsis Subphenotypes via Time-Aware Multi-ModalAuto-Encoder", published on KDD 2020.
An interactive CLI tool to select and calculate components to manage medical emergencies
Deep learning model for sepsis prediction using high-frequency data
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