Eliciting knowledge for an ontology for personalizing medical decision-making

Adi Fux; Mor Peleg; Pnina Soffer; Silvana Quaglini; Matteo Gabetta

Eliciting knowledge for an ontology for personalizing medical decision-making

Adi Fux, Mor Peleg, Pnina Soffer, Silvana Quaglini, Matteo Gabetta

Department of Information Systems

Research output: Contribution to conference › Paper › peer-review

Abstract

Patients' adherence to a particular treatment depends on its suitability to the patient’s personal needs and social and demographic situation. In order to develop a personalized clinical decision-support system (DSS), we interviewed physicians, nurses, care providers and patients to elicit the main personal categories and their effects on treatment recommendations. Main conclusion: Based on the questionnaire’s analysis, we defined 9 main personal categories influencing 13 different clinical effects.

Original language	English
State	Published - 15 Mar 2012
Event	Up Close and Personalized, International Congress on Personalized Medicine 2012 - Duration: 1 Mar 2012 → …

Conference

Conference	Up Close and Personalized, International Congress on Personalized Medicine 2012
Period	1/03/12 → …

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https://www.researchgate.net/publication/266159437_Eliciting_knowledge_for_an_ontology_for_personalizing_medical_decision-making

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Fux, A., Peleg, M., Soffer, P., Quaglini, S., & Gabetta, M. (2012). Eliciting knowledge for an ontology for personalizing medical decision-making. Paper presented at Up Close and Personalized, International Congress on Personalized Medicine 2012. https://www.researchgate.net/publication/266159437_Eliciting_knowledge_for_an_ontology_for_personalizing_medical_decision-making

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abstract = "Patients' adherence to a particular treatment depends on its suitability to the patient{\textquoteright}s personal needs and social and demographic situation. In order to develop a personalized clinical decision-support system (DSS), we interviewed physicians, nurses, care providers and patients to elicit the main personal categories and their effects on treatment recommendations. Main conclusion: Based on the questionnaire{\textquoteright}s analysis, we defined 9 main personal categories influencing 13 different clinical effects.",

author = "Adi Fux and Mor Peleg and Pnina Soffer and Silvana Quaglini and Matteo Gabetta",

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Fux, A, Peleg, M , Soffer, P, Quaglini, S & Gabetta, M 2012, 'Eliciting knowledge for an ontology for personalizing medical decision-making', Paper presented at Up Close and Personalized, International Congress on Personalized Medicine 2012, 1/03/12. <https://www.researchgate.net/publication/266159437_Eliciting_knowledge_for_an_ontology_for_personalizing_medical_decision-making>

TY - CONF

T1 - Eliciting knowledge for an ontology for personalizing medical decision-making

AU - Fux, Adi

AU - Peleg, Mor

AU - Soffer, Pnina

AU - Quaglini, Silvana

AU - Gabetta, Matteo

PY - 2012/3/15

Y1 - 2012/3/15

N2 - Patients' adherence to a particular treatment depends on its suitability to the patient’s personal needs and social and demographic situation. In order to develop a personalized clinical decision-support system (DSS), we interviewed physicians, nurses, care providers and patients to elicit the main personal categories and their effects on treatment recommendations. Main conclusion: Based on the questionnaire’s analysis, we defined 9 main personal categories influencing 13 different clinical effects.

AB - Patients' adherence to a particular treatment depends on its suitability to the patient’s personal needs and social and demographic situation. In order to develop a personalized clinical decision-support system (DSS), we interviewed physicians, nurses, care providers and patients to elicit the main personal categories and their effects on treatment recommendations. Main conclusion: Based on the questionnaire’s analysis, we defined 9 main personal categories influencing 13 different clinical effects.

M3 - הרצאה

T2 - Up Close and Personalized, International Congress on Personalized Medicine 2012

Y2 - 1 March 2012

ER -

Eliciting knowledge for an ontology for personalizing medical decision-making

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