Applying KaplanMeierFitter model on Time and Events

Simple library to help calculate and graph survival curves.

ML models to predict the probability of patient survival based on various KPI's.

Survival Analysis

Reconstructing individual-level data from published Kaplan-Meier survival curves using the method proposed by Guyot, Patricia et al (2012)

survival analysis on cirrhosis data from mayo clinic study: kaplan-meier estimator/curve, log rank test, cox proportional hazards model

A survival analysis study of ovarian carcinoma patients involved in clinical trials using R

business analytics course homework assignments

TNO MPC Lab - Protocols - Kaplan-Meier

A preprocessor to construct medical history table from data source

Survival Analysis on the patients from a trial of laser coagulation for the treatment of diabetic retinopathy. Survival times in this dataset are actual time to blindness in months, minus the minimum possible time to event (6.5 months).

Determined how long a patient is likely to survive advanced inoperable lung cancer when treated with chemotherapy (standard treatment) vs chemotherapy combined with a new drug (test treatment).

This repository contains the notes, codes, assignments, quizzes and other additional materials about the course "AI for Medical Prognosis" from DeepLearning.AI Coursera.

UX Analytics & A/B Testing

Minimal implementation of Kaplan-Meier and Cox proportional hazards models

tracking survival rate of new employees with a best fitted model using 4 most significant personality trait parameters

This Project is a study of the patient’s survival rate due to heart failure condition caused by cardiovascular diseases. Various factors causing the disease were analyzed with the use of reliability analysis and software to model and predict patients’ survival.

This project focuces on analysis of survival patients with Aids, with Python library Lifelines

Examining how caffeine levels impact fly survival

Kaplan-Meier for Congestive Heart Failure Analysis