The review process involved the inclusion of 83 studies. A significant portion, 63%, of the studies, exceeded 12 months since their publication. upper genital infections In transfer learning applications, time series data was employed most frequently (61%), followed by tabular data (18%), audio (12%), and textual data (8%). Following the conversion of non-image data to images, 33 studies (40% of the total) utilized an image-based modeling approach. These visual representations of sound data are known as spectrograms. A significant portion (35%) of the 29 reviewed studies lacked authors with a health-related affiliation. Publicly accessible datasets (66%) and models (49%) were frequently utilized in many studies, yet the sharing of code remained comparatively less prevalent (27%).
The present scoping review explores the prevailing trends in the utilization of transfer learning for non-image data, as presented in the clinical literature. The deployment of transfer learning has increased substantially over the previous years. Transfer learning's promise in clinical research, demonstrated through our study findings across multiple medical disciplines, has been established. The application of transfer learning in clinical research can be enhanced by expanding interdisciplinary collaborations and widespread adoption of reproducible research standards.
This review of clinical literature scopes the recent trends in utilizing transfer learning for analysis of non-image data. The number of transfer learning applications has been noticeably higher in the recent few years. Clinical research, encompassing a multitude of medical specialties, has seen us identify and showcase the efficacy of transfer learning. The impact of transfer learning in clinical research can be magnified by fostering more interdisciplinary collaborations and by widely adopting reproducible research practices.
The growing trend of substance use disorders (SUDs) and the severity of their impacts in low- and middle-income countries (LMICs) makes imperative the adoption of interventions that are acceptable, practical, and effective in addressing this major concern. The world is increasingly examining the potential of telehealth interventions to provide effective management of substance use disorders. This paper employs a scoping review approach to compile and assess the empirical data for the acceptability, practicality, and effectiveness of telehealth interventions for managing substance use disorders (SUDs) in low- and middle-income countries (LMICs). A comprehensive search strategy was employed across five bibliographic databases: PubMed, PsycINFO, Web of Science, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library of Systematic Reviews. Among the studies included were those from low- and middle-income countries (LMICs) which characterized telehealth approaches, identified psychoactive substance use amongst study participants, and utilized methodologies that either compared outcomes using pre- and post-intervention data, or used treatment versus control groups, or utilized data collected post-intervention, or assessed behavioral or health outcomes, or measured the intervention’s acceptability, feasibility, and/or effectiveness. Data is narratively summarized via charts, graphs, and tables. During the period between 2010 and 2020, a search conducted in 14 countries found 39 articles that perfectly aligned with our eligibility requirements. A notable surge in research on this subject occurred over the past five years, peaking with the largest volume of studies in 2019. The identified studies demonstrated a degree of methodological variance, using diverse telecommunication means to evaluate substance use disorders, where cigarette smoking represented the most frequent target of assessment. Quantitative methodologies were prevalent across most studies. Included studies were most prevalent from China and Brazil, and only two from Africa examined telehealth interventions for substance use disorders. Hepatic alveolar echinococcosis A substantial body of research has emerged, assessing telehealth interventions for substance use disorders (SUDs) in low- and middle-income countries (LMICs). Evaluations of telehealth interventions for substance use disorders highlighted encouraging findings regarding acceptability, feasibility, and effectiveness. In this article, the identification of both research gaps and areas of strength informs suggestions for future research directions.
Frequent falls are a common occurrence and are linked to health problems in individuals with multiple sclerosis. MS symptoms exhibit significant fluctuation, which makes standard, every-other-year clinical assessments inadequate for capturing these changes. The application of wearable sensors within remote monitoring systems has emerged as a strategy sensitive to the dynamic range of disease. Previous research in controlled laboratory settings has highlighted the potential of walking data from wearable sensors for fall risk identification; however, the transferability of these results to the complex and often uncontrolled home environments is not guaranteed. We introduce a novel open-source dataset, compiled from 38 PwMS, to evaluate fall risk and daily activity performance using remote data. Data from 21 fallers and 17 non-fallers, identified over six months, are included in this dataset. This dataset comprises inertial measurement unit data gathered from eleven body sites in a laboratory setting, patient-reported surveys and neurological evaluations, and two days of free-living sensor data from the chest and right thigh. Additional data on some patients' progress encompasses six-month (n = 28) and one-year (n = 15) repeat evaluations. Ceralasertib These data's practical utility is explored by examining free-living walking episodes to characterize fall risk in individuals with multiple sclerosis, comparing these findings to those from controlled settings and analyzing the relationship between bout duration, gait characteristics, and fall risk predictions. Changes in both gait parameters and fall risk classification performance were noted, dependent upon the duration of the bout. Feature-based models were outperformed by deep learning models in analyzing home data. Performance testing on individual bouts revealed deep learning's effectiveness with comprehensive bouts and feature-based models' strengths with concise bouts. While short, free-living strolls displayed minimal similarity to controlled laboratory walks, longer, free-living walking sessions underscored more substantial distinctions between individuals who experience falls and those who do not; furthermore, a composite analysis of all free-living walking routines yielded the most effective methodology in classifying fall risk.
Our healthcare system is being augmented and strengthened by the expanding influence of mobile health (mHealth) technologies. A mobile health application's capacity (in terms of user compliance, ease of use, and patient satisfaction) for conveying Enhanced Recovery Protocol information to cardiac surgical patients around the time of surgery was assessed in this study. Patients undergoing cesarean sections were subjects in this prospective cohort study, conducted at a single center. The mobile health application, developed specifically for this study, was provided to patients at the time of their informed consent and used by them for six to eight weeks post-operative. Following the surgical procedure, patients completed surveys for system usability, patient satisfaction, and quality of life, as well as prior to the procedure Sixty-five patients, having an average age of 64 years, participated in the study's procedures. The app's utilization rate, as measured in post-surgery surveys, stood at a substantial 75%, showing a divergence in use patterns between those younger than 65 (68%) and those 65 and older (81%). Peri-operative cesarean section (CS) patient education, specifically for older adults, is achievable with the practical application of mHealth technology. The application's positive reception among patients was substantial, with most recommending its use over printed materials.
Logistic regression models are commonly used to calculate risk scores, which are pivotal for clinical decision-making. While machine learning techniques demonstrate the capability to identify crucial predictors for concise scoring systems, the 'black box' nature of variable selection procedures hinders interpretability, and the calculated importance of variables from a singular model may exhibit bias. Using the novel Shapley variable importance cloud (ShapleyVIC), we present a robust and interpretable approach to variable selection, taking into account the variance in variable importance measures across different models. Our approach scrutinizes and displays the comprehensive influence of variables for thorough inference and transparent variable selection, while eliminating insignificant contributors to streamline the model-building process. Variable contributions across multiple models are used to create an ensemble ranking of variables, seamlessly integrating with the automated and modularized risk scoring tool, AutoScore, for straightforward implementation. To predict early death or unplanned re-admission after hospital discharge, ShapleyVIC's methodology narrowed down forty-one candidate variables to six, resulting in a risk score that matched the performance of a sixteen-variable model built through machine learning ranking. The current focus on interpretable prediction models in high-stakes decision-making is advanced by our work, which establishes a rigorous process for evaluating variable importance and developing transparent, parsimonious clinical risk prediction scores.
Individuals diagnosed with COVID-19 may exhibit debilitating symptoms necessitating rigorous monitoring. We sought to develop an AI-based model that would predict COVID-19 symptoms and create a digital vocal biomarker that would allow for the easy and numerical monitoring of symptom remission. The Predi-COVID prospective cohort study, with 272 participants recruited during the period from May 2020 to May 2021, provided the data for our investigation.