The NASA Prognostic Model Package is a Python framework focused on defining and building models for prognostics (computation of remaining useful life) of engineering systems, and provides a set of prognostics models for select components developed within this framework, suitable for use in prognostics applications for these components.

The NASA Prognostic Python Packages is a Python framework focused on defining and building models and algorit for prognostics (computation of remaining useful life) of engineering systems, and provides a set of models and algorithms for select components developed within this framework, suitable for use in prognostic applications.

This is the official repository of the R package metamisc

Reproduce results from the paper "Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis. K. Chalkou et. al. Diagn Progn Res . 2021 Oct 27;5(1):17. doi: 10.1186/s41512-021-00106-6."

🩺 Multi-method ML diagnostic & prognostic classifier using ensemble learning (RF, SVM, XGBoost, KNN, MLP) with permutation testing, feature selection, and profile ranking

Time-adjusted effect of socioeconomic status in mortality rates after brain injury: cohort study

Sensitivity of mortality rates to the imputation of missing socioeconomic data: cohort study

Anomaly Detection in Time Series Data using Autoencoders approach.

Conceptual models of oceanic diurnal warm layer dynamics

Effect of socioeconomic status in mortality rates after brain injury: cohort study

NASA Turbofan Jet Engine Propagation modeling

A missing-aware multimodal framework that fuses CT, Whole-Slide Histopathology, and clinical tabular data for survival prediction in unresectable stage II–III Non-Small Cell Lung Cancer (NSCLC) — without dropping patients or imputing absent modalities.

Pronostic biomarkers for SKCM

Consultorias em Estatística Médica e Epidemiologia Clínica. CNPJ:42.154.074/0001-22

[MICCAI 2024] novel Multi-Dataset Multi-Task (MDMT) learning framework that predicts COVID-19 prognostic outcomes from chest X-rays by jointly training on two publicly available datasets with distinct but correlated labelling schemes, AIforCOVID and BRIXIA, without requiring universal cross-dataset annotations.

Predicting Remaining Useful Life (RUL) of jet engines using NASA’s CMAPSS FD001 multivariate time-series sensor data.

Benchmarking ML models for predicting DT Cancer recurrence with nested CV and explaining predictions with SHAP (XAI).

Survival analysis of events attributed to PJI in patients that undergone TJA surgeries