Therapy Decision Support Based on Recommender System Methods

Abstract

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.

A minimum data set of user profile or electronic health record for chemical warfare victims' recommender system

“…pointed to data elements such as date of birth, gender, and weight, which were similar with the results of the present study, but Graber et al .’ recommender system was for the treatment of psoriasis. [ 40 ] Agapito et al . in a study referred to data elements such as age, sex, race, and weight that were similar with the results of the current study.…”

First-step validation of a text message-based application for newborn clinical management among pediatricians

“…Mobile applications have been used to improve skilled attendance at delivery [25], and follow up infants for other outcomes such as breastfeeding and perinatal mortality [24,38]. Existing interventions have targeted the patients, but very few have targeted the health worker [24][25][26][27]. Health worker targeted electronic interventions have mainly been for management of childhood illnesses with limited focus on newborn care [39][40][41].…”

“…Applications have also been extended to include training of FLHW in retention of knowledge and skills for managing newborns [21], patient follow-up, and communication of critical laboratory results [22,23], creating a vibrant and innovative landscape in mHealth. Most of these interventions target the patient with few directed towards capacity development of practicing health workers [24][25][26][27].…”

Contributions from the 2017 Literature on Clinical Decision Support

“…The work relied on Semantic Web technology to solve complex clinical decision support queries and it was evaluated using a real-world medical dataset of patients with hepatitis, from which different percentages of data were randomly removed to reflect scenarios with increasing amounts of incomplete medical knowledge. Gräßer et al [14] presented a system for data-driven therapy decision support based on techniques from the field of recommender systems, while Danaley et al [15] introduced the Genomic Prescribing System, an online, secure, electronic custom interface aiming to simplify the use of pharmacogenomics in clinical practice.…”