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         <div style="font-size:160%; padding:.1em;">Welcome to PredictMod Wiki!</div>
         <div style="font-size:160%; padding:.1em;">Welcome to PredictMod Wiki!</div>
         <div style="font-size:100%;">This is the [https://www.mediawiki.org/wiki/MediaWiki MediaWiki] for the PredictMod project. This wiki system provides complementary information to the [https://hivelab.biochemistry.gwu.edu/predictmod/ PredictMod Portal].
         <div style="font-size:100%;">This is the [https://www.mediawiki.org/wiki/MediaWiki MediaWiki] for the PredictMod project. This wiki system provides complementary information to the [https://hivelab.biochemistry.gwu.edu/predictmod/ PredictMod Portal].
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         <h2>Introduction</h2>
         <h2>[[About PredictMod|About]]</h2>
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.  


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.
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.
[https://hivelab.biochemistry.gwu.edu/predictmod/about Learn More]
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         <h2>User Guide</h2>
         <h2>[[PredictMod User Guide|User Guide]]</h2>
This document contains tutorials, help pages, and frequently asked questions for users.   
This document contains resources for users of the PredictMod Platform.   


Read more:  
Quick links:  
*[[PredictMod User Guide|ML Tutorial]]
*[[PredictMod ML Pipeline Tutorial|PredictMod Machine Learning Pipeline Tutorial]]
*[[How to Find and Extract Machine-Usable Data from Scientific Literature]]
*[[How to Find and Extract Machine-Usable Data from Scientific Literature]]
*[[PredictMod User Guide|Frequently Asked Questions]]
*[[PredictMod Frequently Asked Questions|Frequently Asked Questions]]
*[[PredictMod Contact Us|Contact Us]]
*[[PredictMod Model Submission]]
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         <h2>Publications</h2>
         <h2>[[PredictMod Publications & Multimedia|Publications & MultiMedia]]</h2>Recent 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. [https://pubmed.ncbi.nlm.nih.gov/38313584/ 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://linkinghub.elsevier.com/retrieve/pii/S2352396422002420 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. [https://pubmed.ncbi.nlm.nih.gov/33814114/ 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. [https://pubmed.ncbi.nlm.nih.gov/31509535/ PMID: 31509535].
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'''PredictMod: a machine learning-based platform for predicting and sharing intervention outcomes in patients.'''  
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        <h2>MultiMedia</h2>
* '''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].


* '''VA AI Tech Sprint Phase 3 Final Demo | GWU HIVE'''  Presented by Stephanie Singleton, James Ziegler, Edited by James Ziegler  Published April 20th, 2021  <br />[https://www.youtube.com/embed/CgIwy_zfn9g Phase 3 Demo]. View our [https://tinyurl.com/Final-Demo-Materials materials].  This video is a part of the [https://hivelab.biochemistry.gwu.edu/gfkb Microbiome Project].
[[File:AIME Poster.png|frameless|751x751px]]


<small>''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''.</small>
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         <h2>Current and Former Contributors</h2>
         <h2>Current and Former Contributors</h2>
<h3>The George Washington University </h3>
<h3>The George Washington University </h3>
<p> Raja Mazumder <br/>  
<p> Raja Mazumder <br />  
Pat McNeely <br/>   
Pat McNeely <br />   
Urnisha Bhuiyan <br/>
Urnisha Bhuiyan <br />
Lori Krammer <br/>  
Lori Krammer <br />  
Miguel Mazumder <br/>
Miguel Mazumder <br />


<h3>External Collaborators</h3>
<h3>External Collaborators</h3>
<p>
<p>
Sabyasachi Sen, <em>Veterans Administration</em><br/>
Sabyasachi Sen, <em>Veterans Administration</em><br />
Jorge Sepulveda, <em>Medical Faculty Associates</em><br/>
Jorge Sepulveda, <em>Medical Faculty Associates</em><br />
Atin Basu Choudhary, <em>Virginia Military Institute</em><br/>
Atin Basu Choudhary, <em>Virginia Military Institute</em><br />
John David, <em>Virginia Military Institute</em><br/>
John David, <em>Virginia Military Institute</em><br />
Vinod Aggarwal, <em>Veterans Administration</em><br/>
Vinod Aggarwal, <em>Veterans Administration</em><br />


<h3>Former Contributors</h3>
<h3>Former Contributors</h3>
<p>
<p>
Abel Argaw<br/>
Abel Argaw<br />
Stephanie Singleton<br/>
Stephanie Singleton<br />
Sangeeta Agarwal<br/>
Sangeeta Agarwal<br />
Zacharie Savarie<br/>
Zacharie Savarie<br />
Janet Chrosniak<br/>
Janet Chrosniak<br />
Josh Hakakian<br/>
Josh Hakakian<br />
Nicole Richmond<br/>
Nicole Richmond<br />
Wilma Jogunoori<br/>
Wilma Jogunoori<br />
Arad Jain<br/>
Arad Jain<br />
Hadley King<br/>
Hadley King<br />
Robel Kahsay<p>
Robel Kahsay<p>


Latest revision as of 20:42, 12 March 2025


Welcome to PredictMod Wiki!
This is the MediaWiki for the PredictMod project. This wiki system provides complementary information to the PredictMod Portal.

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.

Publications & MultiMedia

Recent Publications:

PredictMod: a machine learning-based platform for predicting and sharing intervention outcomes in patients.

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.

Current and Former Contributors

The George Washington University

Raja Mazumder
Pat McNeely
Urnisha Bhuiyan
Lori Krammer
Miguel Mazumder

External Collaborators

Sabyasachi Sen, Veterans Administration
Jorge Sepulveda, Medical Faculty Associates
Atin Basu Choudhary, Virginia Military Institute
John David, Virginia Military Institute
Vinod Aggarwal, Veterans Administration

Former Contributors

Abel Argaw
Stephanie Singleton
Sangeeta Agarwal
Zacharie Savarie
Janet Chrosniak
Josh Hakakian
Nicole Richmond
Wilma Jogunoori
Arad Jain
Hadley King
Robel Kahsay


Special thanks to our interns and volunteers.

Retrieved from "https://hivelab.biochemistry.gwu.edu/wiki/index.php?title=PredictMod&oldid=505"