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...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden
2017
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| Subjects: | |
| Online Access: | 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 |
| Summary: | 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. |
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