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This app uses multi-modality machine learning to assist with predicting Parkinson’s Disease.

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This app offers an insightful journey into understanding the predictive model for Parkinson's disease (PD) based on genetic features. With a user-friendly interface, healthcare professionals and researchers can explore the intricate workings of the model, delving into the contributions of individual genetic markers to PD classification. Through summary plots, dependence analyses, and decision pathways, users gain valuable insights into how the model arrives at its predictions, discerning key features and their impact. Additionally, interactive features such as force plots enable a closer examination of individual patient predictions, enhancing interpretability and facilitating deeper investigations into the underlying mechanisms of PD.

See owner's GitHub repository for more information: https://github.com/anant-dadu/shapleyPDPredictionGenetics/blob/main/streamlit_app.py

Makarious, M.B., Leonard, H.L., Vitale, D. et al. Multi-modality machine learning predicting Parkinson’s disease. npj Parkinsons Dis. 8, 35 (2022). https://doi.org/10.1038/s41531-022-00288-w

This application was not uploaded by the author, but through their publicly available Github repository, https://github.com/anant-dadu/shapleyPDPredictionGenetics/blob/main/streamlit_app.py.

Prototype

Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.


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  • Clinical Informatics

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C Community Discovery

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