Master's thesis work aims to develop a tool for the analysis and prediction of data from the MIMIC-III database, using sepsis as a case study. Two specific prediction tasks have been selected: sepsis mortality and mortality within 30 days of sepsis diagnosis.
This repository houses a machine learning project focused on the early detection and classification of sepsis, and integrating the model into a web application using FAST API.
This project aims to predict sepsis in patients using advanced machine learning models. The workflow encompasses data preprocessing, feature engineering, class imbalance handling, hyperparameter optimization, model training, evaluation, model card generation, and model registry management for reproducibility and scalability.
This repository contains the implementation and evaluation of multiple machine learning models using Jupyter Notebook. A total of 13 models have been tested, with Model 13 achieving the highest accuracy using ensemble methods like RandomForestClassifier and StackingClassifier.
Winter 2024 - DSC180B A05-01 GitHub Repository (Click the link to go to the Dashboard)
It analyze clinical data and detect early signs of sepsis, enabling timely treatment and improving patient outcomes.
A Machine learning approach to predict whether a patient is likely to develop sepsis within the next six hours.
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