Medical decision support systems will only be accepted by the medical community if properly evaluated. However, little attention has been given in the scientific literature to the topic of how to incorporate evaluation issues into the design of a decision-support system. In this paper, we describe work in developing a decision-support system that is intended to support the management (diagnosis and treatment selection) of ventilator-associated pneumonia in patients. From the beginning of the development of this system, we have taken care to incorporate evaluation issues into the design of the system. In the paper, we analyse the problems that need be taken into account when evaluating a system. Next, we describe the consequences for the functionality of the system.
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JMIR Res Protoc
January 2025
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Background: Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the potential to improve the quality of care and thereby positively impact patient-relevant outcomes, the majority of AI-based CDSS have not been adopted in standard care. Possible reasons for this include barriers in the implementation and a nonuser-oriented development approach, resulting in reduced user acceptance.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany.
Background: Unobtrusively collected objective sensor data from everyday devices like smartphones provide a novel paradigm to infer mental health symptoms. This process, called smart sensing, allows a fine-grained assessment of various features (eg, time spent at home based on the GPS sensor). Based on its prevalence and impact, depression is a promising target for smart sensing.
View Article and Find Full Text PDFBull World Health Organ
February 2025
Department of Rheumatology and Immunology, Singapore General Hospital, Singapore.
Objective: To evaluate the adoption, effectiveness and cost-effectiveness of digital health interventions for rheumatic disease management.
Methods: Between 25 May 2024 and 2 June 2024, we systematically searched PubMed®, Scopus, ClinicalTrials.gov, the Global Observatory for eHealth and the World Bank Open Knowledge Repository for randomized controlled trials (RCTs) evaluating digital health interventions for rheumatic disease management.
Water Res
January 2025
Department of Chemical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK; SWING - Department of Built Environment, Oslo Metropolitan University, St Olavs plass 0130, Oslo, Norway. Electronic address:
Climate resilience in water resource recovery facilities (WRRFs) necessitates improved adaptation to shock-loading conditions and mitigating greenhouse gas emission. Data-driven learning methods are widely utilised in soft-sensors for decision support and process optimization due to their simplicity and high predictive accuracy. However, unlike for mechanistic models, transferring machine-learning-based insights across systems is largely infeasible, which limits communication and knowledge sharing.
View Article and Find Full Text PDFParasit Vectors
January 2025
Faculty of Information Technology, Mutah University, Mutah, Jordan.
Background: Amebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples.
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