Utilising a publicly-available and small dataset of ~5K patients from Kaggle, to practice health data analysis.
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Updated
Oct 17, 2024
Utilising a publicly-available and small dataset of ~5K patients from Kaggle, to practice health data analysis.
Resources for Survival Analysis
Graphical Presentation of Clinical Data in Oncology Studies Using R
Use of clinical EHR data and codelists to investigate all-cause mortality between proton pump inhibitor (PPI) and histamine H2-receptor antagonist (H2RA) exposure groups.
Survival Analysis of cancer patient data
HeartPredict is a Python library designed to analyze and predict heart failure outcomes using patient data.
R package for Kaplan-Meier Plot: Modified ggkm
This project aims to analyze customer churn data to identify key drivers and suggest actionable strategies to improve retention.
🍊 ➕ Survival Analysis add-on for Orange3 data mining suite.
Survival Analysis for Glioblastoma Multiforme
Kaplan-Meier-Estimator also known as the product limit estimator.
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.
Survival analysis in R for Public Health (Imperial College London through Coursera)
Survival Analysis of Lung Cancer Patients
survival curves in ggplot2
I proved the probabilities of freedom from biochemical recurrence (BCR) among prostate cancer patients are significantly different using stratified Logrank test. I also built a Cox's PH model to identify which genes and demographic factors have effect on survival.
Survival Analysis
Generating Kaplan Meier plots using gene expression data
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