PredictMod: Difference between revisions
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==== Introduction ==== | ==== Introduction ==== | ||
PredictMod (https://hivelab.biochemistry.gwu.edu/predictmod) is an application designed to predict the outcome of an intervention prior to a patient initiating treatment. Through the use of the open-source PredictMod platform, clinicians, patients, and researchers will access predictive ML models based on real-world data. The platform empowers users with limited experience in bioinformatics to leverage the power of predictive modeling, providing a collaborative solution for improving patient outcomes. This resource aims to provide clinicians with a powerful decision-making tool that enhances clinical understanding of patient-level data. The PredictMod platform utilizes ML tools and complex datasets based on EHR, gut microbiome, and other -omics data to forecast patient outcomes, often in response to treatment for a particular condition. While our primary condition of interest is Prediabetes, the tool is designed to be used for a variety of conditions, interventions, and data types. The agnostic nature of the platform allows for widespread use and relevance to all fields within the scope of medicine. | PredictMod (https://hivelab.biochemistry.gwu.edu/predictmod) is an application designed to predict the outcome of an intervention prior to a patient initiating treatment. Through the use of the open-source PredictMod platform, clinicians, patients, and researchers will access predictive ML models based on real-world data. The platform empowers users with limited experience in bioinformatics to leverage the power of predictive modeling, providing a collaborative solution for improving patient outcomes. This resource aims to provide clinicians with a powerful decision-making tool that enhances clinical understanding of patient-level data. The PredictMod platform utilizes ML tools and complex datasets based on EHR, gut microbiome, and other -omics data to forecast patient outcomes, often in response to treatment for a particular condition. While our primary condition of interest is Prediabetes, the tool is designed to be used for a variety of conditions, interventions, and data types. The agnostic nature of the platform allows for widespread use and relevance to all fields within the scope of medicine. | ||
'''Publications''' | '''Publications''' | ||
Revision as of 19:34, 17 December 2024
Introduction
PredictMod (https://hivelab.biochemistry.gwu.edu/predictmod) is an application designed to predict the outcome of an intervention prior to a patient initiating treatment. Through the use of the open-source PredictMod platform, clinicians, patients, and researchers will access predictive ML models based on real-world data. The platform empowers users with limited experience in bioinformatics to leverage the power of predictive modeling, providing a collaborative solution for improving patient outcomes. This resource aims to provide clinicians with a powerful decision-making tool that enhances clinical understanding of patient-level data. The PredictMod platform utilizes ML tools and complex datasets based on EHR, gut microbiome, and other -omics data to forecast patient outcomes, often in response to treatment for a particular condition. While our primary condition of interest is Prediabetes, the tool is designed to be used for a variety of conditions, interventions, and data types. The agnostic nature of the platform allows for widespread use and relevance to all fields within the scope of medicine.
Publications
- Krammer L, Aggarwal V, Bhuiyan U, McNeely P, Mazumder R. PredictMod: a machine learning-based platform for predicting and sharing intervention outcomes in patients. Poster presented at: 22nd International Conference on Artificial Intelligence in Medicine; July 9-12, 2024; Salt Lake City, Utah, USA.
- Wu J, Singleton SS, Bhuiyan, Krammer L, Mazumder R. Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning. Front. Mol. Biosci.. 19 January 2024; Sec. Molecular Diagnostics and Therapeutics. Volume 10 – 2023. PMID: 38313584.
- Dahlin M, Singleton S, David J, Basuchoudhary A, Wickstrom, Mazumder R, Prast-Nielsen S. Higher levels of Bifidobacteria and tumor necrosis factor in children with drug-resistant epilepsy are associated with anti-seizure response to the ketogenic diet. eBioMedicine. June 2022; vol: 80. https://doi.org/10.1016/j.ebiom.2022.104061.
- Hopson L, Singleton S, David J, Basuchoudhary A, Prast-Nielsen S, Klein P, Sen S, Mazumder R. Bioinformatics and machine learning in gastrointestinal microbiome research and clinical application. Prog Mol Biol Transl Sci. 2020 Sep 30; 176:141-178. PMID: 33814114.
- King CH, Desai H, Sylvetsky AC, LoTempio J, Ayanyan S, Carrie J, Crandall K, Fochtman B, Gasparyan L, Gulzar N, Howell P, Issa N, Krampis K, Mishra L, Morizono H, Pisegna JR, Rao S, Ren Y, Simonyan V, Smith K, VedBrat S, Yao M, Mazumder R. Baseline human gut microbiota profile in healthy people and standard reporting template. PLOS ONE. 2019. PMID: 31509535.