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."
Anomaly Detection in Time Series Data using Autoencoders approach.
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
Effect of socioeconomic status in mortality rates after brain injury: cohort study
NASA Turbofan Jet Engine Propagation modeling
Conceptual models of oceanic diurnal warm layer dynamics
Unify engineering principles and processes with a flexible framework that promotes clarity, consistency, and long-term alignment across any organization.
Consultorias em Estatistica Medica e Epidemiologia Clinica. CNPJ:42.154.074/0001-22
Predicting Remaining Useful Life (RUL) of jet engines using NASA's CMAPSS FD001 multivariate time-series sensor data.
Multi-method ML diagnostic & prognostic classifier using ensemble learning (RF, SVM, XGBoost, KNN, MLP) with permutation testing, feature selection, and profile ranking
Survival analysis of events attributed to PJI in patients that undergone TJA surgeries
Benchmarking ML models for predicting DT Cancer recurrence with nested CV and explaining predictions with SHAP (XAI).
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
Prognostico de sobrevida em cativeiro de Tityus bahiensis capturados em Americana/SP
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