Simple library to help calculate and graph survival curves.
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Updated
Mar 12, 2018 - Ruby
Simple library to help calculate and graph survival curves.
ML models to predict the probability of patient survival based on various KPI's.
Applying KaplanMeierFitter model on Time and Events
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
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.
TNO MPC Lab - Protocols - Kaplan-Meier
UX Analytics & A/B Testing
Kaplan-Meier for Congestive Heart Failure Analysis
Generating Kaplan Meier plots using gene expression data
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).
Minimal implementation of Kaplan-Meier and Cox proportional hazards models
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.
tracking survival rate of new employees with a best fitted Cox Proportional Hazards model using 4 most significant personality traits
Analyzed employee turnover (Jan 2022 - Mar 2023) at my former organization, considering trends, departmental attrition, and tenure insights. Used predictive analytics from the 2022 Employee Engagement Survey to identify groups with flight risk. Incorporated Survival Analysis for temporal patterns, guiding decisions to improve retention.
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