Transthoracic echocardiography and mortality in sepsis: analysis of the MIMIC-III database

Reinforcement Learning based 'Learning' of Dynamic Sepsis Treatment Strategies

Deep learning model for sepsis prediction using high-frequency data

Early prediction of sepsis with gradient boosting (XGBoost) and deep learning (LSTM and GRU) using MIMIC-III data.

Mobile and Web application for real time early prediction of sepsis(6 hours before the onset). I did pre processing, data visualisation , feature engineering and all the data science and machine learning work, I also handled Google Cloud and real time analysis. A flask app is built and deployed on heroku platform

React-based web application for sepsis detection

Baseline to compare the performance of different models with sepsis data from MIMIC-III database

IEEE BIBM 2021: Bayesian optimization-guided topic modeling for automatic detection of sepsis-related events from free text

Repository for the journal article, 'FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices', Mahbub Ul Alam, Rahim Rahmani. Sensors 23, no. 2: 970, https://doi.org/10.3390/s23020970.

The early prediction of sepsis is potentially life-saving, and we challenge participants to predict sepsis 6 hours before the clinical prediction of sepsis.

Web application for sepsis clinical assessment scores using batch ICU patient data

Laboratory Diagnostics from Septic and Non-septic Patients Used in the AMPEL Project

Predict sepsis using structured data of patients who were admitted to the ICU along with their radiology reports using Deep Learning Models (CNN and LSTM)

Integrating genomics and physiologic data (high-frequency) for sepsis detection

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 for "AI Gone Astray: Technical Supplement", which investigates the effect of time drift on clinically deployed machine learning models

To Detect Sepsis Disease using six Classifiers on clinical data

R, Python, and SQL implementations of the Phoenix Sepsis Criteria

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.