Introduction: Remote digital assessments (RDAs) such as voice recording, video and motor sensors, olfactory, hearing, and vision screenings are now starting to be employed to complement classical biomarker and clinical evidence to identify patients in the early AD stages. Choosing which RDA can be proposed to individual patients is not trivial and often time-consuming. This position paper presents a decision-making algorithm for using RDA during teleconsultations in memory clinic settings.
Method: The algorithm was developed by an expert panel following the Delphi methodology.
Results: The decision-making algorithm is structured as a series of yes-no questions. The resulting questionnaire is freely available online.
Discussion: We suggest that the use of screening questionnaires in the context of memory clinics may help accelerating the adoption of RDA in everyday clinical practice.
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http://dx.doi.org/10.1159/000539129 | DOI Listing |
Heliyon
July 2024
School of Business Information Technology, University of Economics, Ho Chi Minh City, Viet Nam.
In this manuscript, we first initiate several types of effective arcs of intuitionistic fuzzy directed graphs, followed by discussions on different types of dominations in intuitionistic fuzzy directed graphs and their application in decision-making. The notion of dominations in fuzzy graphs, fuzzy directed graphs, intuitionistic fuzzy graphs and picture fuzzy graphs have been extensively discussed in the literature. Thus, the work presented in our study is two-fold: on one side, it extends the notion of domination in fuzzy directed graphs, while on the other side, it fills the gap existing in the literature.
View Article and Find Full Text PDFHeliyon
July 2024
School of Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom.
Road traffic accidents pose a significant global health concern, with an alarming 1.19 million fatalities reported in 2021. Traditionally, strategies to address this challenge have relied on expert input and subjective evaluations.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Kenya Medical Research Institute- Center for Global Health Research (KEMRI-CGHR), P.O Box 1578-40100, Kisumu, Kenya.
Background: Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machine learning (ML) to derive and validate a predictive model for LDD among children presenting with diarrhea to health facilities.
Methods: LDD was defined as a diarrhea episode lasting ≥ 7 days.
BMC Med Inform Decis Mak
January 2025
Department of Orthopedics, the First Hospital of Jilin University, Changchun, Jilin Province, 130021, China.
Purpose: Identifying patients who may benefit from multiple drilling are crucial. Hence, the purpose of the study is to utilize radiomics and deep learning for predicting no-collapse survival in patients with femoral head osteonecrosis.
Methods: Patients who underwent multiple drilling were enrolled.
Abdom Radiol (NY)
January 2025
Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Purpose: Mesenteric artery embolism (MAE) is a relatively uncommon abdominal surgical emergency, but it can lead to catastrophic clinical outcomes if the diagnosis is delayed. This study aims to build a prediction model of clinical-radiomics nomogram for early diagnosis of MAE based on non-contrast computed tomography (CT) and biomarkers.
Method: In this retrospective study, a total of 364 patients confirmed as MAE (n = 131) or non-MAE (n = 233) who were randomly divided into a training cohort (70%) and a validation cohort (30%).
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