PredictMod: Difference between revisions
Jump to navigation
Jump to search
Lorikrammer (talk | contribs) No edit summary |
Lorikrammer (talk | contribs) No edit summary |
||
| Line 15: | Line 15: | ||
<div id="ggw_row3" style="display: flex; flex-flow: row wrap; justify-content: space-between; padding: 0; margin: 0 -5px 0 -5px;"> | <div id="ggw_row3" style="display: flex; flex-flow: row wrap; justify-content: space-between; padding: 0; margin: 0 -5px 0 -5px;"> | ||
<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;"> | <div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;"> | ||
<h3>[[About]]</h3> | <h3>[[PredictMod About | About]]</h3> | ||
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;"> | <div style="border-top: 1px solid #CCC; padding-top: 0.5em;"> | ||
PredictMod (https://hivelab.biochemistry.gwu.edu/predictmod) is an application designed to provide clinicians with a powerful decision making tool that enhances clinical understanding of patient-level data. 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. The PredictMod platform utilizes ML tools and complex datasets based on electronic medical records (EMR), gut microbiome, and other -omics data to forecast patient outcomes, often in response to treatment for a particular condition. | PredictMod (https://hivelab.biochemistry.gwu.edu/predictmod) is an application designed to provide clinicians with a powerful decision making tool that enhances clinical understanding of patient-level data. 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. The PredictMod platform utilizes ML tools and complex datasets based on electronic medical records (EMR), gut microbiome, and other -omics data to forecast patient outcomes, often in response to treatment for a particular condition. | ||
| Line 27: | Line 27: | ||
</div><div id="ggw_row3" style="display: flex; flex-flow: row wrap; justify-content: space-between; padding: 0; margin: 0 -5px 0 -5px;"> | </div><div id="ggw_row3" style="display: flex; flex-flow: row wrap; justify-content: space-between; padding: 0; margin: 0 -5px 0 -5px;"> | ||
<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;"> | <div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;"> | ||
<h3>[[User Guide]]</h3> | <h3>[[PredictMod User Guide | User Guide]]</h3> | ||
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;"> | <div style="border-top: 1px solid #CCC; padding-top: 0.5em;"> | ||
This document contains tutorials, help pages, and frequently asked questions for users. | This document contains tutorials, help pages, and frequently asked questions for users. | ||
| Line 35: | Line 35: | ||
</div><div id="ggw_row3" style="display: flex; flex-flow: row wrap; justify-content: space-between; padding: 0; margin: 0 -5px 0 -5px;"> | </div><div id="ggw_row3" style="display: flex; flex-flow: row wrap; justify-content: space-between; padding: 0; margin: 0 -5px 0 -5px;"> | ||
<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;"> | <div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;"> | ||
<h3>[[Publications]]</h3> | <h3>[[PredictMod Publications | Publications]]</h3> | ||
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;"> | <div style="border-top: 1px solid #CCC; padding-top: 0.5em;"> | ||
* 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. | * 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. | ||
| Line 48: | Line 48: | ||
</div><div id="ggw_row3" style="display: flex; flex-flow: row wrap; justify-content: space-between; padding: 0; margin: 0 -5px 0 -5px;"> | </div><div id="ggw_row3" style="display: flex; flex-flow: row wrap; justify-content: space-between; padding: 0; margin: 0 -5px 0 -5px;"> | ||
<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;"> | <div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;"> | ||
<h3>[[MultiMedia]]</h3> | <h3>[[PredictMod MultiMedia | MultiMedia]]</h3> | ||
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;"> | <div style="border-top: 1px solid #CCC; padding-top: 0.5em;"> | ||
* '''Microbiome: VA AI Tech Sprint 2021 | Phase 2 Demo''' Featuring Stephanie Singleton, Edited by James Ziegler Published December 8th, 2020 <br />[https://www.youtube.com/embed/K2S7YrIBN_0 Phase 2 Demo]. View our [https://youtu.be/RRm6-kCGegE MATLAB Prototype Demo]. View our [https://tinyurl.com/phase-2-demo-slides Phase 2 Demo Slides]. This video is a part of the [https://hivelab.biochemistry.gwu.edu/gfkb Microbiome Project]. | * '''Microbiome: VA AI Tech Sprint 2021 | Phase 2 Demo''' Featuring Stephanie Singleton, Edited by James Ziegler Published December 8th, 2020 <br />[https://www.youtube.com/embed/K2S7YrIBN_0 Phase 2 Demo]. View our [https://youtu.be/RRm6-kCGegE MATLAB Prototype Demo]. View our [https://tinyurl.com/phase-2-demo-slides Phase 2 Demo Slides]. This video is a part of the [https://hivelab.biochemistry.gwu.edu/gfkb Microbiome Project]. | ||
Revision as of 19:52, 5 March 2025
About
PredictMod (https://hivelab.biochemistry.gwu.edu/predictmod) is an application designed to provide clinicians with a powerful decision making tool that enhances clinical understanding of patient-level data. 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. The PredictMod platform utilizes ML tools and complex datasets based on electronic medical records (EMR), gut microbiome, and other -omics data to forecast patient outcomes, often in response to treatment for a particular condition.
While our primary conditions of interest are prediabetes and cancer, 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.
User Guide
This document contains tutorials, help pages, and frequently asked questions for users.
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.
- Bhuiyan, U. in Biochemistry and Molecular Medicine, Vol. Masters 72 (George Washington University, Washington, DC; 2023).
- 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.
MultiMedia
- Microbiome: VA AI Tech Sprint 2021 | Phase 2 Demo Featuring Stephanie Singleton, Edited by James Ziegler Published December 8th, 2020
Phase 2 Demo. View our MATLAB Prototype Demo. View our Phase 2 Demo Slides. This video is a part of the Microbiome Project.
- VA AI Tech Sprint Phase 3 Final Demo | GWU HIVE Presented by Stephanie Singleton, James Ziegler, Edited by James Ziegler Published April 20th, 2021
Phase 3 Demo. View our materials. This video is a part of the Microbiome Project.