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."

Conceptual models of oceanic diurnal warm layer dynamics

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

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

NASA Turbofan Jet Engine Propagation modeling

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

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

Anomaly Detection in Time Series Data using Autoencoders approach.

Pronostic biomarkers for SKCM

🚀 Unify engineering principles and processes with a flexible framework that promotes clarity, consistency, and long-term alignment across any organization.

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

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

Implementation of UD_ARULE CLI version within Python to process feature data (FD) nodes of a system/system-of-systems to predict key prognostic parameters such as RUL, PH, and SoH

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This repo contains the code used for the CARSS external validation project.

Prognóstico de sobrevida em cativeiro de Tityus bahiensis capturados em Americana/SP

Prognóstico de componentes hematológicos após ATQ bilateral simultânea em centro cirúrgico de referência

No description provided.

Implant failure rates in a knee prosthesis sub-population of the Helios Klinikum Berlin-Buch hospitals

Cardiovascular Disease Risk Prediction from Cardiac CT and Risk Factors Using Deep Survival Networks

A toolkit for Development, Validation, and Updating of Clinical Prediction Models.