Frequently Asked Questions - Revision history

Lorikrammer: Blanked the page

2025-03-12T19:34:42Z

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	~~<small> Go Back to [[PredictMod\|PredictMod Project]]. </small>~~
	~~== Login & Registration ==~~

	~~==== Who can register for a PredictMod account? ====~~
	~~Any clinicians, and researchers, or other individuals interested in using the tool are invited to register.~~

	~~==== How long does registration last? ====~~
	~~Registered accounts do not expire. You will be logged out after 24 hours of each login.~~

	~~==== Will the system store patient data? ====~~
	~~The data will not be saved in the system. PredictMod will use uploaded patient data to make a one-time prediction.~~

	~~==== Can I view a history of my predictions? ====~~
	~~Prediction results are not saved, so a prediction history is not available.~~

	~~== Query Builder ==~~

	~~==== <strong>How can I run a prediction using a single-patient data file?</strong> ====~~
	Use the query builder to select the desired condition, intervention, and data type associated with your patient data. File upload templates vary by model. Reference our example files to ensure your data meets our formatting requirements. Select ‘Run Prediction’ to view the results.

	~~==== What file types are accepted? ====~~
	~~Comma separated values (csv) or excel workbook (xlsx) files are accepted.~~

	~~==== How do I know if my file is not formatted correctly? ====~~
	An incorrectly formatted file will return an error message when the 'Run a Prediction' option is selected. If you receive a formatting error message and you are unsure why, please contact our team and we can work with you to resolve the issue.

	~~''For additional assistance with uploading and formatting data, please contact our team.''~~

	~~==== <strong>What data types are included in the PredictMod query selection?</strong> ====~~
	~~The current data types are metagenomic (MG), glycomic, glycoproteomic, and electronic medical record (EMR).~~

	* <em>MG data</em> consist of the microbial composition of the gut microbiome typically displayed as a percent of abundance. This data is typically used within a research context to profile healthy gut microbiomes and to identify differential abundances and their associations to disease. Our MG Model is trained on publicly available data. The reference genomes used for this model are recorded in the Gut Feeling Knowledge Base (GFKB). Please use this list as a reference. Organisms not referenced in the GFKB can still be included in the input and receive an accurate model prediction. If you have any questions about this, please don’t hesitate to contact our team.
	* <em>EMR</em> consists of real-time patient-centered records that are utilized by physicians to streamline their workflow. It provides physicians the ability to view important medical details about their patients in order to provide improved patient care that is both efficient and safe. Our EMR models are trained on data from the MDClone and Epic Cosmos.

	~~==== <strong>What conditions are included in the PredictMod query selection?</strong> ====~~
	~~The current conditions of interest are prediabetes, epilepsy, and cancer. The platform is condition-agnostic and is designed to be used for a variety of conditions, interventions, and data types.~~

	* <em>Prediabetes</em> is a precursor to Type 2 Diabetes Mellitus (T2DM), where blood sugars are higher than normal, but not high enough to be considered T2DM. Prediction outcomes are based on a 5% reduction in weight, HOMA-IR, or a diagnosis of T2DM.

	~~==== <strong>What interventions are included in the PredictMod query selection?</strong> ====~~
	~~The current interventions are exercise, ketogenic diet, and dietary counseling. We also have a Semaglutide intervention coming soon.~~

	~~==== <strong>What are the possible prediction outcomes?</strong> ====~~
	PredictMod will provide a prediction categorized as either <em>Responder</em> or <em>Non-Responder</em>. The outcomes associated with the response status vary for each model, though a <em>Responder</em> result is generally associated with a positive health outcome, and the <em>Non-Responder</em> result is generally associated with a negative health outcome.

	~~''For additional information, please review the [https://hivelab.biochemistry.gwu.edu/predictmod/models models] page on the portal.''~~

	~~== Data & Security ==~~

	~~==== <strong>Will the system store patient data?</strong> ====~~
	~~The data will not be saved in the system. PredictMod will use uploaded patient data to make a one-time prediction.~~

Lorikrammer at 21:11, 5 March 2025

2025-03-05T21:11:31Z

← Older revision		Revision as of 21:11, 5 March 2025
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	==== <strong>What interventions are included in the PredictMod query selection?</strong> ====		==== <strong>What interventions are included in the PredictMod query selection?</strong> ====
	The current interventions are exercise, ketogenic diet, and dietary counseling. We also have a Semaglutide intervention coming soon.		The current interventions are exercise, ketogenic diet, and dietary counseling. We also have a Semaglutide intervention coming soon.
	~~== Prediction Results ==~~

	==== <strong>What are the possible prediction outcomes?</strong> ====		==== <strong>What are the possible prediction outcomes?</strong> ====

Lorikrammer at 21:11, 5 March 2025

2025-03-05T21:11:02Z

← Older revision		Revision as of 21:11, 5 March 2025
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	== Query Builder ==		== Query Builder ==
	<strong>How can I run a prediction using a single-patient data file?</strong>~~<br />~~
			==== <strong>How can I run a prediction using a single-patient data file?</strong> ====
	Use the query builder to select the desired condition, intervention, and data type associated with your patient data. File upload templates vary by model. Reference our example files to ensure your data meets our formatting requirements. Select ‘Run Prediction’ to view the results.		Use the query builder to select the desired condition, intervention, and data type associated with your patient data. File upload templates vary by model. Reference our example files to ensure your data meets our formatting requirements. Select ‘Run Prediction’ to view the results.

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	==== How do I know if my file is not formatted correctly? ====		==== How do I know if my file is not formatted correctly? ====
	An incorrectly formatted file will return an error message when the 'Run a Prediction' option is selected.		An incorrectly formatted file will return an error message when the 'Run a Prediction' option is selected. If you receive a formatting error message and you are unsure why, please contact our team and we can work with you to resolve the issue.

	If you receive a formatting error message and you are unsure why, please contact our team and we can work with you to resolve the issue.


	~~For additional assistance with uploading and formatting data, please contact our team~~.

			''For additional assistance with uploading and formatting data, please contact our team.''

	<strong>What data types are included in the PredictMod query selection?</strong>~~<br />~~		==== <strong>What data types are included in the PredictMod query selection?</strong> ====
	The current data types are metagenomic (MG), glycomic, glycoproteomic, and electronic medical record (EMR). ~~<br />~~		The current data types are metagenomic (MG), glycomic, glycoproteomic, and electronic medical record (EMR).

	<em>MG data</em> consist of the microbial composition of the gut microbiome typically displayed as a percent of abundance. This data is typically used within a research context to profile healthy gut microbiomes and to identify differential abundances and their associations to disease. Our MG Model is trained on publicly available data. The reference genomes used for this model are recorded in the Gut Feeling Knowledge Base (GFKB). Please use this list as a reference. Organisms not referenced in the GFKB can still be included in the input and receive an accurate model prediction. If you have any questions about this, please don’t hesitate to contact our team.<br />		* <em>MG data</em> consist of the microbial composition of the gut microbiome typically displayed as a percent of abundance. This data is typically used within a research context to profile healthy gut microbiomes and to identify differential abundances and their associations to disease. Our MG Model is trained on publicly available data. The reference genomes used for this model are recorded in the Gut Feeling Knowledge Base (GFKB). Please use this list as a reference. Organisms not referenced in the GFKB can still be included in the input and receive an accurate model prediction. If you have any questions about this, please don’t hesitate to contact our team.
			* <em>EMR</em> consists of real-time patient-centered records that are utilized by physicians to streamline their workflow. It provides physicians the ability to view important medical details about their patients in order to provide improved patient care that is both efficient and safe. Our EMR models are trained on data from the MDClone and Epic Cosmos.

	<em>EMR</em> consists of real-time patient-centered records that are utilized by physicians to streamline their workflow. It provides physicians the ability to view important medical details about their patients in order to provide improved patient care that is both efficient and safe. Our EMR models are trained on data from the MDClone and Epic Cosmos.		==== <strong>What conditions are included in the PredictMod query selection?</strong> ====


	<strong>What conditions are included in the PredictMod query selection?</strong>~~<br />~~
	The current conditions of interest are prediabetes, epilepsy, and cancer. The platform is condition-agnostic and is designed to be used for a variety of conditions, interventions, and data types.		The current conditions of interest are prediabetes, epilepsy, and cancer. The platform is condition-agnostic and is designed to be used for a variety of conditions, interventions, and data types.

	<em>Prediabetes</em> is a precursor to Type 2 Diabetes Mellitus (T2DM), where blood sugars are higher than normal, but not high enough to be considered T2DM. Prediction outcomes are based on a 5% reduction in weight, HOMA-IR, or a diagnosis of T2DM.		* <em>Prediabetes</em> is a precursor to Type 2 Diabetes Mellitus (T2DM), where blood sugars are higher than normal, but not high enough to be considered T2DM. Prediction outcomes are based on a 5% reduction in weight, HOMA-IR, or a diagnosis of T2DM.

			==== <strong>What interventions are included in the PredictMod query selection?</strong> ====
			The current interventions are exercise, ketogenic diet, and dietary counseling. We also have a Semaglutide intervention coming soon.
			== Prediction Results ==

	<strong>What ~~interventions~~ are ~~included in~~ the ~~PredictMod query selection~~?</strong><br />		==== <strong>What are the possible prediction outcomes?</strong> ====
	The ~~current interventions are exercise~~, ~~ketogenic diet~~, and ~~dietary counseling. We also have~~ a ~~Semaglutide intervention coming soon~~.		PredictMod will provide a prediction categorized as either <em>Responder</em> or <em>Non-Responder</em>. The outcomes associated with the response status vary for each model, though a <em>Responder</em> result is generally associated with a positive health outcome, and the <em>Non-Responder</em> result is generally associated with a negative health outcome.

	~~== Prediction Results ==~~		''For additional information, please review the [https://hivelab.biochemistry.gwu.edu/predictmod/models models] page on the portal.''
	~~<strong>What are the possible prediction outcomes?</strong><br />~~
	PredictMod will provide a prediction categorized as either <em>Responder</em> or <em>Non-Responder</em>. The outcomes associated with the response status vary for each model, though a <em>Responder</em> result is generally associated with a positive health outcome, and the <em>Non-Responder</em> result is generally associated with a negative health outcome.<br /><br />
	For additional information, please review the ''[https://hivelab.biochemistry.gwu.edu/predictmod/models models]'' page on the portal.

	== Data & Security ==		== Data & Security ==
	<strong>Will the system store patient data?</strong>~~<br />~~
			==== <strong>Will the system store patient data?</strong> ====
	The data will not be saved in the system. PredictMod will use uploaded patient data to make a one-time prediction.		The data will not be saved in the system. PredictMod will use uploaded patient data to make a one-time prediction.

Lorikrammer at 21:07, 5 March 2025

2025-03-05T21:07:17Z

← Older revision		Revision as of 21:07, 5 March 2025
Line 1:		Line 1:
	<small> Go Back to [[PredictMod\|PredictMod Project]]. </small>		<small> Go Back to [[PredictMod\|PredictMod Project]]. </small>
			== Login & Registration ==

			==== Who can register for a PredictMod account? ====
			Any clinicians, and researchers, or other individuals interested in using the tool are invited to register.

			==== How long does registration last? ====
			Registered accounts do not expire. You will be logged out after 24 hours of each login.

			==== Will the system store patient data? ====
			The data will not be saved in the system. PredictMod will use uploaded patient data to make a one-time prediction.

			==== Can I view a history of my predictions? ====
			Prediction results are not saved, so a prediction history is not available.

			== Query Builder ==
	<strong>How can I run a prediction using a single-patient data file?</strong><br />		<strong>How can I run a prediction using a single-patient data file?</strong><br />
	Use the query builder to select the desired condition, intervention, and data type associated with your patient data. File upload templates vary by model. Reference our example files to ensure your data meets our formatting requirements. Select ‘Run Prediction’ to view the results.~~<br /><br />~~		Use the query builder to select the desired condition, intervention, and data type associated with your patient data. File upload templates vary by model. Reference our example files to ensure your data meets our formatting requirements. Select ‘Run Prediction’ to view the results.
	~~For additional assistance with uploading and formatting data, please contact our team.<br /><br />~~

	~~<strong>Will the system store patient data~~?~~</strong><br />~~		==== What file types are accepted? ====
	~~The data~~ will ~~not be saved in~~ the ~~system~~. ~~PredictMod will use uploaded patient~~ data ~~to make a one-time prediction~~.~~<br /><br />~~		Comma separated values (csv) or excel workbook (xlsx) files are accepted.

			==== How do I know if my file is not formatted correctly? ====
			An incorrectly formatted file will return an error message when the 'Run a Prediction' option is selected.

			If you receive a formatting error message and you are unsure why, please contact our team and we can work with you to resolve the issue.


			For additional assistance with uploading and formatting data, please contact our team.

	~~<strong>What are the possible prediction outcomes?</strong><br />~~
	PredictMod will provide a prediction categorized as either <em>Responder</em> or <em>Non-Responder</em>. The outcomes associated with the response status vary for each model, though a <em>Responder</em> result is generally associated with a positive health outcome, and the <em>Non-Responder</em> result is generally associated with a negative health outcome.<br /><br />
	~~For additional information, please review the ''[https://hivelab.biochemistry.gwu.edu/predictmod/models models]'' page on the portal.<br /><br />~~

	<strong>What data types are included in the PredictMod query selection?</strong><br />		<strong>What data types are included in the PredictMod query selection?</strong><br />
	The current data types are metagenomic (MG), glycomic, glycoproteomic, and electronic medical record (EMR). ~~<br />~~<br />		The current data types are metagenomic (MG), glycomic, glycoproteomic, and electronic medical record (EMR). <br />

	<em>MG data</em> consist of the microbial composition of the gut microbiome typically displayed as a percent of abundance. This data is typically used within a research context to profile healthy gut microbiomes and to identify differential abundances and their associations to disease. Our MG Model is trained on publicly available data. The reference genomes used for this model are recorded in the Gut Feeling Knowledge Base (GFKB). Please use this list as a reference. Organisms not referenced in the GFKB can still be included in the input and receive an accurate model prediction. If you have any questions about this, please don’t hesitate to contact our team.<br /><br />		<em>MG data</em> consist of the microbial composition of the gut microbiome typically displayed as a percent of abundance. This data is typically used within a research context to profile healthy gut microbiomes and to identify differential abundances and their associations to disease. Our MG Model is trained on publicly available data. The reference genomes used for this model are recorded in the Gut Feeling Knowledge Base (GFKB). Please use this list as a reference. Organisms not referenced in the GFKB can still be included in the input and receive an accurate model prediction. If you have any questions about this, please don’t hesitate to contact our team.<br />

			<em>EMR</em> consists of real-time patient-centered records that are utilized by physicians to streamline their workflow. It provides physicians the ability to view important medical details about their patients in order to provide improved patient care that is both efficient and safe. Our EMR models are trained on data from the MDClone and Epic Cosmos.

	<em>EMR</em> consists of real-time patient-centered records that are utilized by physicians to streamline their workflow. It provides physicians the ability to view important medical details about their patients in order to provide improved patient care that is both efficient and safe. Our EMR models are trained on data from the MDClone and Epic Cosmos.<br /><br />

	<strong>What conditions are included in the PredictMod query selection?</strong><br />		<strong>What conditions are included in the PredictMod query selection?</strong><br />
	The current conditions of interest are prediabetes, epilepsy, and cancer. The platform is condition-agnostic and is designed to be used for a variety of conditions, interventions, and data types.<~~br /~~><br />		The current conditions of interest are prediabetes, epilepsy, and cancer. The platform is condition-agnostic and is designed to be used for a variety of conditions, interventions, and data types.

			<em>Prediabetes</em> is a precursor to Type 2 Diabetes Mellitus (T2DM), where blood sugars are higher than normal, but not high enough to be considered T2DM. Prediction outcomes are based on a 5% reduction in weight, HOMA-IR, or a diagnosis of T2DM.

	<em>Prediabetes</em> is a precursor to Type 2 Diabetes Mellitus (T2DM), where blood sugars are higher than normal, but not high enough to be considered T2DM. Prediction outcomes are based on a 5% reduction in weight, HOMA-IR, or a diagnosis of T2DM.<br /><br />

	<strong>What interventions are included in the PredictMod query selection?</strong><br />		<strong>What interventions are included in the PredictMod query selection?</strong><br />
	The current interventions are exercise, ketogenic diet, and dietary counseling. We also have a Semaglutide intervention coming soon.		The current interventions are exercise, ketogenic diet, and dietary counseling. We also have a Semaglutide intervention coming soon.

	== ~~Login & Registration~~ ==		== Prediction Results ==
			<strong>What are the possible prediction outcomes?</strong><br />
			PredictMod will provide a prediction categorized as either <em>Responder</em> or <em>Non-Responder</em>. The outcomes associated with the response status vary for each model, though a <em>Responder</em> result is generally associated with a positive health outcome, and the <em>Non-Responder</em> result is generally associated with a negative health outcome.<br /><br />
			For additional information, please review the ''[https://hivelab.biochemistry.gwu.edu/predictmod/models models]'' page on the portal.

	==~~== Who can register for a PredictMod account? ====~~		== Data & Security ==
	~~Any clinicians, and researchers, or other individuals interested in using the tool are invited to register.~~		<strong>Will the system store patient data?</strong><br />

	~~==== How long does registration last? ==~~==
	~~Registered accounts do not expire. You will be logged out after 24 hours of each login.~~

	~~====~~ Will the system store patient data? ~~====~~
	The data will not be saved in the system. PredictMod will use uploaded patient data to make a one-time prediction.		The data will not be saved in the system. PredictMod will use uploaded patient data to make a one-time prediction.

	~~==== Can I view a history of my predictions? ====~~
	~~Prediction results are not saved, so a prediction history is not available.~~

Lorikrammer at 21:01, 5 March 2025

2025-03-05T21:01:27Z

← Older revision		Revision as of 21:01, 5 March 2025
Line 26:		Line 26:
	<strong>What interventions are included in the PredictMod query selection?</strong><br />		<strong>What interventions are included in the PredictMod query selection?</strong><br />
	The current interventions are exercise, ketogenic diet, and dietary counseling. We also have a Semaglutide intervention coming soon.		The current interventions are exercise, ketogenic diet, and dietary counseling. We also have a Semaglutide intervention coming soon.

			== Login & Registration ==

			==== Who can register for a PredictMod account? ====
			Any clinicians, and researchers, or other individuals interested in using the tool are invited to register.

			==== How long does registration last? ====
			Registered accounts do not expire. You will be logged out after 24 hours of each login.

			==== Will the system store patient data? ====
			The data will not be saved in the system. PredictMod will use uploaded patient data to make a one-time prediction.

			==== Can I view a history of my predictions? ====
			Prediction results are not saved, so a prediction history is not available.

Lorikrammer at 20:45, 5 March 2025

2025-03-05T20:45:24Z

← Older revision		Revision as of 20:45, 5 March 2025
Line 1:		Line 1:
	Go Back to [[PredictMod\|PredictMod Project]].		<small> Go Back to [[PredictMod\|PredictMod Project]]. </small>

	<strong>How can I run a prediction using a single-patient data file?</strong><br />		<strong>How can I run a prediction using a single-patient data file?</strong><br />

Lorikrammer at 20:38, 5 March 2025

2025-03-05T20:38:12Z

← Older revision		Revision as of 20:38, 5 March 2025
Line 1:		Line 1:
	Go Back to [[~~Main Page~~\|PredictMod Project]].		Go Back to [[PredictMod\|PredictMod Project]].

	<strong>How can I run a prediction using a single-patient data file?</strong><br />		<strong>How can I run a prediction using a single-patient data file?</strong><br />

Lorikrammer at 20:37, 5 March 2025

2025-03-05T20:37:16Z

← Older revision		Revision as of 20:37, 5 March 2025
Line 1:		Line 1:
			Go Back to [[Main Page\|PredictMod Project]].

	<strong>How can I run a prediction using a single-patient data file?</strong><br />		<strong>How can I run a prediction using a single-patient data file?</strong><br />

Lorikrammer at 20:35, 5 March 2025

2025-03-05T20:35:45Z

← Older revision		Revision as of 20:35, 5 March 2025
Line 1:		Line 1:
	~~<p>~~
	~~<strong>How can I run a prediction using a single-patient data file?</strong><br/>~~
	Use the query builder to select the desired condition, intervention, and data type associated with your patient data. File upload templates vary by model. Reference our example files to ensure your data meets our formatting requirements. Select ‘Run Prediction’ to view the results.<br/><br/>
	~~For additional assistance with uploading and formatting data, please contact our team.<br/><br/>~~

	<strong>~~Will the system store~~ patient data?</strong><br/>		<strong>How can I run a prediction using a single-patient data file?</strong><br />
	~~The~~ data ~~will not be saved in~~ the ~~system~~. ~~PredictMod will use uploaded patient~~ data ~~to make a one-time prediction~~.<br/><br/>		Use the query builder to select the desired condition, intervention, and data type associated with your patient data. File upload templates vary by model. Reference our example files to ensure your data meets our formatting requirements. Select ‘Run Prediction’ to view the results.<br /><br />
			For additional assistance with uploading and formatting data, please contact our team.<br /><br />

	<strong>~~What are~~ the ~~possible prediction outcomes~~?</strong><br/>		<strong>Will the system store patient data?</strong><br />
	PredictMod will ~~provide~~ a prediction categorized as either <em>Responder</em> or <em>Non-Responder</em>. The outcomes associated with the response status vary for each model, though a <em>Responder</em> result is generally associated with a positive health outcome, and the <em>Non-Responder</em> result is generally associated with a negative health outcome.<br/><br/>		The data will not be saved in the system. PredictMod will use uploaded patient data to make a one-time prediction.<br /><br />
	~~For additional information, please review the ''[https://hivelab.biochemistry.gwu.edu/predictmod/models models]'' page on the portal~~.<br/><br/>

	<strong>What ~~data types~~ are ~~included in~~ the ~~PredictMod query selection~~?</strong><br/>		<strong>What are the possible prediction outcomes?</strong><br />
	The ~~current data types are metagenomic (MG)~~ and ~~electronic~~ health ~~record (EHR)~~. ~~We also have a glycomics data type coming soon~~.<br/><br/>		PredictMod will provide a prediction categorized as either <em>Responder</em> or <em>Non-Responder</em>. The outcomes associated with the response status vary for each model, though a <em>Responder</em> result is generally associated with a positive health outcome, and the <em>Non-Responder</em> result is generally associated with a negative health outcome.<br /><br />
			For additional information, please review the ''[https://hivelab.biochemistry.gwu.edu/predictmod/models models]'' page on the portal.<br /><br />

	<em>MG data</em> consist of the microbial composition of the gut microbiome typically displayed as a percent of abundance. This data is typically used within a research context to profile healthy gut microbiomes and to identify differential abundances and their associations to disease. Our MG Model is trained on publicly available data~~. The reference genomes used for this model~~ are ~~recorded in the Gut Feeling Knowledge Base~~ (~~GFKB~~)~~. Please use this list as a reference. Organisms not referenced in the GFKB can still be included in the input~~ and ~~receive an accurate model prediction. If you have any questions about this, please don’t hesitate to contact our team~~.<br/><br/>		<strong>What data types are included in the PredictMod query selection?</strong><br />
			The current data types are metagenomic (MG), glycomic, glycoproteomic, and electronic medical record (EMR). <br /><br />

	<em>~~EHR~~</em> ~~consists~~ of ~~real-time patient-centered records that are utilized by physicians~~ to ~~streamline their workflow. It provides physicians the ability~~ to ~~view important medical details about~~ their ~~patients in order~~ to ~~provide improved patient care that is both efficient and safe~~. Our ~~EHR models are~~ trained on data ~~from~~ the ~~MDClone~~ and ~~Epic Cosmos~~.<br/><br/>		<em>MG data</em> consist of the microbial composition of the gut microbiome typically displayed as a percent of abundance. This data is typically used within a research context to profile healthy gut microbiomes and to identify differential abundances and their associations to disease. Our MG Model is trained on publicly available data. The reference genomes used for this model are recorded in the Gut Feeling Knowledge Base (GFKB). Please use this list as a reference. Organisms not referenced in the GFKB can still be included in the input and receive an accurate model prediction. If you have any questions about this, please don’t hesitate to contact our team.<br /><br />

	<~~strong>What conditions are included in the PredictMod query selection?</strong~~><br/>		<em>EMR</em> consists of real-time patient-centered records that are utilized by physicians to streamline their workflow. It provides physicians the ability to view important medical details about their patients in order to provide improved patient care that is both efficient and safe. Our EMR models are trained on data from the MDClone and Epic Cosmos.<br /><br />
	~~The current condition~~ of ~~interest~~ is ~~Prediabetes~~. ~~We also have an Epilepsy model coming soon~~.<br/><br/>

	<em>~~Prediabetes~~</em> ~~is a precursor to Type 2 Diabetes Mellitus (T2DM)~~, ~~where blood sugars are higher than normal~~, ~~but not high enough~~ to be ~~considered T2DM. Prediction outcomes are based on~~ a ~~5% reduction in weight~~, ~~HOMA-IR~~, ~~or a diagnosis of T2DM~~.<br/><br/>		<strong>What conditions are included in the PredictMod query selection?</strong><br />
			The current conditions of interest are prediabetes, epilepsy, and cancer. The platform is condition-agnostic and is designed to be used for a variety of conditions, interventions, and data types.<br /><br />

	<strong>What interventions are included in the PredictMod query selection?</strong><br/>		<em>Prediabetes</em> is a precursor to Type 2 Diabetes Mellitus (T2DM), where blood sugars are higher than normal, but not high enough to be considered T2DM. Prediction outcomes are based on a 5% reduction in weight, HOMA-IR, or a diagnosis of T2DM.<br /><br />

			<strong>What interventions are included in the PredictMod query selection?</strong><br />
	The current interventions are exercise, ketogenic diet, and dietary counseling. We also have a Semaglutide intervention coming soon.		The current interventions are exercise, ketogenic diet, and dietary counseling. We also have a Semaglutide intervention coming soon.
	~~</p>~~

Lorikrammer at 20:32, 5 March 2025

2025-03-05T20:32:44Z

← Older revision		Revision as of 20:32, 5 March 2025
Line 9:		Line 9:
	<strong>What are the possible prediction outcomes?</strong><br/>		<strong>What are the possible prediction outcomes?</strong><br/>
	PredictMod will provide a prediction categorized as either <em>Responder</em> or <em>Non-Responder</em>. The outcomes associated with the response status vary for each model, though a <em>Responder</em> result is generally associated with a positive health outcome, and the <em>Non-Responder</em> result is generally associated with a negative health outcome.<br/><br/>		PredictMod will provide a prediction categorized as either <em>Responder</em> or <em>Non-Responder</em>. The outcomes associated with the response status vary for each model, though a <em>Responder</em> result is generally associated with a positive health outcome, and the <em>Non-Responder</em> result is generally associated with a negative health outcome.<br/><br/>
	For additional information, please review the ~~model information below~~.<br/><br/>		For additional information, please review the ''[https://hivelab.biochemistry.gwu.edu/predictmod/models models]'' page on the portal.<br/><br/>

	<strong>What data types are included in the PredictMod query selection?</strong><br/>		<strong>What data types are included in the PredictMod query selection?</strong><br/>