Therapy Decision Support Based on Recommender System Methods
We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to...
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Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden
2017
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ndltd-DRESDEN-oai-qucosa.de-bsz-14-qucosa-2268692017-07-22T03:30:59Z Therapy Decision Support Based on Recommender System Methods Gräßer, Felix Beckert, Stefanie Küster, Denise Schmitt, Jochen Abraham, Susanne Malberg, Hagen Zaunseder, Sebastian Datengesteuerte Therapieentscheidungsunterstützung Kollaborative Recommender Demographische Recommender Autoimmunhautkrankheit Psoriasis Technische Universität Dresden Publikationsfonds data-driven therapy decision support Collaborative Recommender Demographic-based Recommender autoimmune skin disease psoriasis Technische Universität Dresden Publishing Fund ddc:610 rvk:XA 10000 We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system. Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden Hindawi, 2017-07-21 doc-type:article application/pdf http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-226869 urn:nbn:de:bsz:14-qucosa-226869 http://www.qucosa.de/fileadmin/data/qucosa/documents/22686/8659460%20%281%29.pdf Journal of Healthcare Engineering (2017) , 2017. ISSN: 2040-2309. DOI: 10.1155/2017/8659460. Artikel-Nr.: 8659460 eng |
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Datengesteuerte Therapieentscheidungsunterstützung Kollaborative Recommender Demographische Recommender Autoimmunhautkrankheit Psoriasis Technische Universität Dresden Publikationsfonds data-driven therapy decision support Collaborative Recommender Demographic-based Recommender autoimmune skin disease psoriasis Technische Universität Dresden Publishing Fund ddc:610 rvk:XA 10000 |
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Datengesteuerte Therapieentscheidungsunterstützung Kollaborative Recommender Demographische Recommender Autoimmunhautkrankheit Psoriasis Technische Universität Dresden Publikationsfonds data-driven therapy decision support Collaborative Recommender Demographic-based Recommender autoimmune skin disease psoriasis Technische Universität Dresden Publishing Fund ddc:610 rvk:XA 10000 Gräßer, Felix Beckert, Stefanie Küster, Denise Schmitt, Jochen Abraham, Susanne Malberg, Hagen Zaunseder, Sebastian Therapy Decision Support Based on Recommender System Methods |
description |
We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system. |
author2 |
Hindawi, |
author_facet |
Hindawi, Gräßer, Felix Beckert, Stefanie Küster, Denise Schmitt, Jochen Abraham, Susanne Malberg, Hagen Zaunseder, Sebastian |
author |
Gräßer, Felix Beckert, Stefanie Küster, Denise Schmitt, Jochen Abraham, Susanne Malberg, Hagen Zaunseder, Sebastian |
author_sort |
Gräßer, Felix |
title |
Therapy Decision Support Based on Recommender System Methods |
title_short |
Therapy Decision Support Based on Recommender System Methods |
title_full |
Therapy Decision Support Based on Recommender System Methods |
title_fullStr |
Therapy Decision Support Based on Recommender System Methods |
title_full_unstemmed |
Therapy Decision Support Based on Recommender System Methods |
title_sort |
therapy decision support based on recommender system methods |
publisher |
Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden |
publishDate |
2017 |
url |
http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-226869 http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-226869 http://www.qucosa.de/fileadmin/data/qucosa/documents/22686/8659460%20%281%29.pdf |
work_keys_str_mv |
AT graßerfelix therapydecisionsupportbasedonrecommendersystemmethods AT beckertstefanie therapydecisionsupportbasedonrecommendersystemmethods AT kusterdenise therapydecisionsupportbasedonrecommendersystemmethods AT schmittjochen therapydecisionsupportbasedonrecommendersystemmethods AT abrahamsusanne therapydecisionsupportbasedonrecommendersystemmethods AT malberghagen therapydecisionsupportbasedonrecommendersystemmethods AT zaunsedersebastian therapydecisionsupportbasedonrecommendersystemmethods |
_version_ |
1718503077623889920 |