Eighty-three studies were incorporated into our review. Within 12 months of the search, 63% of the reviewed studies were published. cross-level moderated mediation The dominant application area for transfer learning involved time series data (61%), with tabular data following closely behind at 18%, and audio and text data each representing 12% and 8% respectively. After converting non-image data into images, 40% (thirty-three) of the studies utilized an image-based model. These visual representations of sound data are known as spectrograms. No health-related affiliations were listed for 29 (35%) of the studies' authors. Studies using publicly available datasets (66%) and models (49%) were common, but the practice of sharing their code was less prevalent (27%).
In this scoping review, we present an overview of the current state of transfer learning applications for non-image data, gleaned from the clinical literature. In recent years, transfer learning has shown a considerable surge in use. Across numerous medical specialities, transfer learning's potential in clinical research has been recognized and demonstrated through our review of pertinent studies. More interdisciplinary collaboration and broader adoption of principles for reproducible research are required to generate a more substantial effect from transfer learning in clinical research.
Transfer learning's current trends for non-image data applications, as demonstrated in clinical literature, are documented in this scoping review. Transfer learning has become increasingly prevalent and widely adopted over the last several years. Studies conducted in clinical research across various medical specialties have demonstrated the potential 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.
Substance use disorders (SUDs) are increasingly prevalent and impactful in low- and middle-income countries (LMICs), thus mandating the adoption of interventions that are acceptable to the community, practical to execute, and proven to produce positive results in addressing this widespread issue. Telehealth interventions are experiencing a global surge in exploration as potential solutions for managing substance use disorders. A scoping review informs this article's analysis of the available evidence concerning the acceptability, practicality, and effectiveness of telehealth interventions designed to address substance use disorders (SUDs) in low- and middle-income countries. Five bibliographic databases—PubMed, PsycINFO, Web of Science, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library—were the focus of the database searches. Telehealth interventions from low- and middle-income countries (LMICs) which reported on psychoactive substance use amongst participants, and which included methodology comparing outcomes using pre- and post-intervention data, or treatment versus comparison groups, or post-intervention data, or behavioral or health outcome measures, or which measured intervention acceptability, feasibility, and/or effectiveness, were selected for inclusion. A narrative summary of the data is presented using charts, graphs, and tables. Eighteen eligible articles were discovered in fourteen nations over a 10-year period between 2010 and 2020 through the search. The volume of research dedicated to this subject dramatically increased over the previous five years, reaching its zenith in the year 2019. Varied methodologies were observed in the identified studies, coupled with multiple telecommunication approaches used to evaluate substance use disorder, with cigarette smoking being the most scrutinized aspect. Quantitative methods were employed in the majority of studies. The preponderance of included studies originated from China and Brazil, with just two studies from Africa focusing on telehealth interventions for substance use disorders. physiological stress biomarkers There is a considerable and increasing body of work dedicated to evaluating telehealth strategies for substance use disorders in low- and middle-income countries. Telehealth-based approaches to substance use disorders exhibited promising levels of acceptability, practicality, and effectiveness. The strengths and shortcomings of current research are analyzed in this article, along with recommendations for future investigation.
In persons with multiple sclerosis, falls happen frequently and are associated with various health issues. Fluctuations in MS symptoms are frequent, making standard, twice-yearly check-ups insufficient to properly track them. Wearable sensor-based remote monitoring methods have recently gained prominence as a means of detecting disease variations. Prior research has confirmed that fall risk can be identified from gait data collected using wearable sensors in a controlled laboratory environment. However, applying these findings to the complexities of home environments is a significant challenge. From a dataset of 38 PwMS monitored remotely, we introduce an open-source resource to study fall risk and daily activity. This dataset differentiates 21 participants classified as fallers and 17 identified as non-fallers based on their six-month fall history. 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. For some patients, repeat assessment data is available, collected at six months (n = 28) and one year (n = 15) after their initial visit. PRT062070 solubility dmso 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. Gait parameters and fall risk classification performance exhibited a dependency on the length of the bout duration. 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. In independent, free-living walks, brief durations exhibited the least similarity to controlled laboratory settings; longer duration free-living walks revealed more notable discrepancies between those prone to falls and those who were not; and a holistic assessment encompassing all free-living walking bouts provided the most effective prediction for fall risk.
Mobile health (mHealth) technologies are rapidly becoming indispensable to the functioning of our healthcare system. This study investigated the practicality (adherence, user-friendliness, and patient contentment) of a mobile health application for disseminating Enhanced Recovery Protocol information to cardiac surgery patients during the perioperative period. This prospective, single-center cohort study included patients who had undergone cesarean section procedures. Patients received the study-specific mHealth application at the moment of consent, and continued using it for six to eight weeks after their operation. Patients' system usability, satisfaction, and quality of life were assessed via surveys both before and after surgical intervention. A cohort of 65 patients, averaging 64 years of age, took part in the research. According to post-operative surveys, the app's overall utilization was 75%, demonstrating a variation in usage between users under 65 (utilizing it 68% of the time) and users above 65 (utilizing it 81% of the time). The feasibility of mHealth technology in providing peri-operative patient education for cesarean section (CS) procedures extends to older adult populations. The application garnered high levels of satisfaction from a majority of patients, who would recommend its use to printed materials.
Risk scores, frequently produced through logistic regression modeling, play a significant role in clinical decision-making procedures. Though machine learning techniques may effectively determine significant predictors for streamlined scoring, their opacity in variable selection diminishes interpretability, and single-model-based variable importance estimates can be unreliable. We advocate for a robust and interpretable variable selection method, leveraging the newly introduced Shapley variable importance cloud (ShapleyVIC), which precisely captures the variability in variable significance across various models. Our method for in-depth inference and transparent variable selection involves evaluating and visualizing the total impact of variables, while removing non-significant contributions to simplify the model construction 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. ShapleyVIC, in their study on premature death or unplanned re-admission following hospital discharge, curated a six-variable risk score from a larger pool of forty-one candidates, showing performance on par with a sixteen-variable machine learning-based ranking model. Our research contributes to the current emphasis on interpretable prediction models for high-stakes decision-making by offering a meticulously designed approach for evaluating variable influence and developing concise and understandable clinical risk scores.
Impairing symptoms, a common consequence of COVID-19 infection, warrant elevated surveillance. Our mission was to construct an artificial intelligence-based model that could predict COVID-19 symptoms, and in turn, develop a digital vocal biomarker for the easy and measurable 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.