Popular GitHub Repositories for Hospitals

Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

(TMI-2024) Source-Free Active Domain Adaptation (SFADA) for GTV Segmentation across Multiple Hospitals

Multi-agent LangGraph Chatbot for hospitals - Django based web application. Leveraging LLM, Knowledge Graph, Multi-agent architecture, RAG.

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage

MedHeaven is an innovative solution designed to revolutionize healthcare by empowering hospitals and medical institutions to collaborate on machine learning models without compromising patient privacy. Using federated learning and advanced homomorphic encryption techniques, MedHeaven ensures that sensitive medical data remains secure.

1st AI Prize: DeDoctor is a revolutionary solution that employs federative learning learning within hospitalswithin hospitals, aggregates scardiovascular disease data to then provide accurate diagnoses and recommendations, and has the potential to do chain abstraction.

The primary goal is to develop automated brain tumor detection, a working proposed system. That will be used by Hospitals or laboratories to access the patients to get their MRI reports in time without any problem or dependency. The main objective is to understand the working of image processing, machine learning technologies and working these algorithms on applied technology. This project will help the doctors in finding of tumors precisely according to the length, breadth and the exact position of the infected area.

The project is focused on developing a cutting edge technology to assist hospitals in diagnosis of Pheneumonia patients, particularly children.

An intelligent voice assistant for hospitals that interacts with patients in real-time over phone calls that listens, understands intent, books appointments, and responds instantly - all via a real-time WebSocket pipeline.

A digital bureaucratic advocate that helps people navigate complex institutions like the DMV, hospitals, and courthouses with clarity, guidance, and empathy.

Sentiment analysis of hospital reviews on Google Maps using web scraping and NLP

This repository features an app that uses LightGBM machine learning to instantly evaluate real estate properties in Florianópolis, Brazil based on location, size, and other features, as well as proximity to risk areas, transportation, hospitals, markets, and green spaces. It also includes code and datasets for reproducing the analysis.