Using lifelines to replicate published articles
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Jan 17, 2022 - Jupyter Notebook
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lifelines
Here are 27 public repositories matching this topic...
This project focuces on analysis of survival patients with Aids, with Python library Lifelines
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Dec 22, 2022 - Jupyter Notebook
DecenniumClinic is a reproducible Python stack for epidemiology-style cohorts: validation → imputation-in-pipeline random forests → Harrell’s C tuning, plus /health, /ready, and curl-friendly APIs. Built for methods research, not patient care.
python flask machine-learning scikit-learn survival-analysis epidemiology medical-informatics lifelines prognosis risk-prediction
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Apr 5, 2026 - Python
Applying KaplanMeierFitter model on Time and Events
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Feb 7, 2021 - Jupyter Notebook
A biostatistical survival analysis pipeline using Python to evaluate patient prognosis in the Mayo Clinic PBC dataset. Implements Kaplan-Meier estimators and Cox Proportional Hazards models to mathematically process right-censored clinical data and identify mortality risk factors.
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Apr 8, 2026 - Jupyter Notebook
Predictive survival modeling of pancreatic cancer progression. Investigating lifestyle risk factors through non-parametric analysis (KM/Log-Rank), semi-parametric Cox Proportional Hazards, and Machine Learning via Random Survival Forests (RSF).
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Jun 13, 2026 - Jupyter Notebook
A repository containing various projects and microprojects.
python data-science machine-learning deep-learning clustering numpy scikit-learn regression pandas data-visualization classification dimensionality-reduction data-analysis survival-analysis matplotlib keras-tensorflow time-series-analysis lifelines scikit-surprise
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Jun 15, 2023 - Jupyter Notebook
end-to-end survival analysis on 15,054 breast cancer patients using Kaplan–Meier, Cox PH, and Weibull AFT models to identify prognostic factors and evaluate survival outcomes
python machine-learning biostatistics survival-analysis predictive-modeling kaplan-meier oncology lifelines cox-ph healthcare-analytics clinical-analysis weibull-aft breat-cancer
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Jun 8, 2026 - Python
Survival analysis of breast cancer clinical data using Kaplan–Meier curves and Cox proportional hazards models in Python
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Mar 9, 2026 - Jupyter Notebook
Notebooks for "A topic model analysis of TCGA transcriptomic data of breast and lung cancer"
topic-modeling stochastic-block-model tcga-data lifelines cancer-machine-learning topic-model-analysis
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Mar 23, 2022 - Jupyter Notebook
Project and tutorial for analyzing datasets with Python, pandas, lifelines, matplotlib, statsmodels, and seaborn
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Nov 6, 2017 - Jupyter Notebook
Survival prediction model on TCGA-BRCA data · Python · Lifelines · Streamlit
python portfolio data-science machine-learning cancer scikit-learn survival-analysis breast-cancer lifelines streamlit
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Apr 11, 2026 - Python
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Jan 27, 2024 - Jupyter Notebook
Python survival-analysis engine for retention strategy testing: K-Means personas, CoxPH runway modeling, high-risk scenario simulation, Kaplan-Meier curves, and executive PPTX/PDF outputs.
python customer-retention scikit-learn survival-analysis matplotlib k-means kaplan-meier predictive-analytics reportlab python-pptx lifelines scenario-analysis churn-analysis segmentation-models coxph risk-modeling cox-proportional-hazards coxph-model customer-retention-analysis executive-reporting
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May 30, 2026 - Python
Analyzed user events from a leading scheduling SaaS platform to uncover what drives activation, engagement, and subscription [Part 2]
python postgresql pandas survival-analysis lifelines matplotlib-pyplot cox-proportional-hazard kaplan-meier-curve
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Feb 13, 2026 - Jupyter Notebook
Interactive web application to analyize the survival of Endometrial Cancer
plotly tcga pandas django-framework survival-analysis kaplan-meier science-research pandas-library lifelines pandas-python plotly-python kaplan-meier-plot kaplanmeierfitter endometriosis scipy-stats endometrial-cancer plolty-express kaplan-meier-curve tcga-ucec ucec
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Mar 24, 2025 - Python
analyzing 200+ Falcon 9 missions via survival analysis, Kaplan-Meier curves, and more
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Jul 24, 2025 - HTML
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