PredictMod: Difference between revisions
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==== Recent Publications: ==== | ==== Recent Publications: ==== | ||
* | * Talk Data Podcast | MDClone Featuring Lori Krammer | Published March 4th, 2026 <br/>[https://www.linkedin.com/posts/lori-krammer_syntheticdata-machinelearning-healthcareinnovation-activity-7442231985365770242-xzV0?utm_source=share&utm_medium=member_desktop&rcm=ACoAACerBVYBiJq4wwQ4cu1WPEc-RZ1z7ZHiMhQ Linkedin Post]. Listen on [https://open.spotify.com/show/68biApf6cwsE50bAnAdj1R Spotify] or [https://podcasts.apple.com/us/podcast/talk-data/id1653305563 Apple Podcasts]. | ||
* Arethiya NJ, Krammer L, David J, Bakshi V, BasuChoudhary A, Bhuiyan U, Sen S, Mazumder R, McNeely P. Enhancing prediabetes diagnosis from continuous glucose monitoring data via iterative label cleaning and deep learning of Bridge2AI AI-READI data. medRxiv. 2026 Mar 4. Preprint. [https://www.medrxiv.org/content/10.64898/2026.03.04.26347604v1 DOI: 10.64898/2026.03.04.26347604]. | * Arethiya NJ, Krammer L, David J, Bakshi V, BasuChoudhary A, Bhuiyan U, Sen S, Mazumder R, McNeely P. Enhancing prediabetes diagnosis from continuous glucose monitoring data via iterative label cleaning and deep learning of Bridge2AI AI-READI data. medRxiv. 2026 Mar 4. Preprint. [https://www.medrxiv.org/content/10.64898/2026.03.04.26347604v1 DOI: 10.64898/2026.03.04.26347604]. | ||
* Krammer L, McNeely P, and Bhuiyan U et al. PredictMod: A Platform for Predicting Medical Intervention Outcomes and Sharing Custom ML/AI Models. ''NSM.'' 2025. Vol. 1(1):57-66. [https://drugrepocentral.scienceopen.com/hosted-document?doi=10.14293/NSM.25.1.0007 DOI:10.14293/NSM.25.1.0007]</div> | * Krammer L, McNeely P, and Bhuiyan U et al. PredictMod: A Platform for Predicting Medical Intervention Outcomes and Sharing Custom ML/AI Models. ''NSM.'' 2025. Vol. 1(1):57-66. [https://drugrepocentral.scienceopen.com/hosted-document?doi=10.14293/NSM.25.1.0007 DOI:10.14293/NSM.25.1.0007]</div> | ||
Latest revision as of 17:20, 27 March 2026
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 resources for users of the PredictMod Platform.
Quick links for model users:
Quick links for model submitters:
Publications & MultiMedia
Recent Publications:
- Talk Data Podcast | MDClone Featuring Lori Krammer | Published March 4th, 2026
Linkedin Post. Listen on Spotify or Apple Podcasts. - Arethiya NJ, Krammer L, David J, Bakshi V, BasuChoudhary A, Bhuiyan U, Sen S, Mazumder R, McNeely P. Enhancing prediabetes diagnosis from continuous glucose monitoring data via iterative label cleaning and deep learning of Bridge2AI AI-READI data. medRxiv. 2026 Mar 4. Preprint. DOI: 10.64898/2026.03.04.26347604.
- Krammer L, McNeely P, and Bhuiyan U et al. PredictMod: A Platform for Predicting Medical Intervention Outcomes and Sharing Custom ML/AI Models. NSM. 2025. Vol. 1(1):57-66. DOI:10.14293/NSM.25.1.0007
Current and Former Contributors
The George Washington University
Raja Mazumder
Pat McNeely
Urnisha Bhuiyan
Lori Krammer
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
Miguel Mazumder
Abel Argaw
Stephanie Singleton
Sangeeta Agarwal
Zacharie Savarie
Janet Chrosniak
Josh Hakakian
Nicole Richmond
Wilma Jogunoori
Arad Jain
Hadley King
Special thanks to our interns and volunteers.