A total of 83 studies were factored into the review's analysis. From the research gathered, a considerable proportion (63%) of the studies have been published within the past 12 months. Clinical toxicology In transfer learning applications, time series data was employed most frequently (61%), followed by tabular data (18%), audio (12%), and textual data (8%). Image-based models were employed in 33 (40%) studies that initially converted non-image data to images (e.g.). The time-frequency representation of acoustic signals, commonly seen in audio analysis, is known as a spectrogram. Among the 29 (35%) studies reviewed, none of the authors possessed health-related affiliations. Numerous research projects used freely available datasets (66%) and pre-existing models (49%), but only a minority (27%) shared their accompanying code.
This scoping review describes current practices in the clinical literature regarding the use of transfer learning for non-image information. Over the past several years, transfer learning has experienced substantial growth in application. We have demonstrated through various medical specialty studies the potential applications of transfer learning in clinical research. Transfer learning in clinical research can achieve a stronger impact through a surge in collaborative projects across disciplines and a wider embrace of the principles of reproducible research.
This review of clinical literature scopes the recent trends in utilizing transfer learning for analysis of non-image data. Transfer learning has become increasingly prevalent and widely adopted over the last several years. Across various medical specialties, we have observed and validated the potential of transfer learning within clinical research studies. Increased interdisciplinary cooperation and the expanded usage of reproducible research methods are necessary to augment the impact of transfer learning within clinical research.

The increasing incidence and severity of substance use disorders (SUDs) in low- and middle-income countries (LMICs) necessitates the implementation of interventions that are socially viable, operationally feasible, and clinically effective in diminishing this significant health concern. Across the globe, there's a growing interest in telehealth's capacity to effectively manage substance use disorders. This article leverages a scoping review of the literature to provide a concise summary and evaluation of the evidence regarding the acceptability, applicability, and efficacy of telehealth interventions for substance use disorders (SUDs) in low- and middle-income contexts. Searches were executed across PubMed, PsycINFO, Web of Science, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library, five major bibliographic databases. Studies originating from low- and middle-income countries (LMICs) that detailed a telehealth approach, and in which at least one participant exhibited psychoactive substance use, and whose methodologies either compared results using pre- and post-intervention data, or compared treatment and comparison groups, or utilized post-intervention data for assessment, or analyzed behavioral or health outcomes, or evaluated the acceptability, feasibility, and/or effectiveness of the intervention were included in the analysis. Charts, graphs, and tables are used to create a narrative summary of the data. From a ten-year study (2010-2020), spanning 14 countries, our search yielded 39 articles, each satisfying our predetermined eligibility standards. A notable surge in research on this subject occurred over the past five years, peaking with the largest volume of studies in 2019. Methodological variability was evident in the reviewed studies, which used diverse telecommunication modalities to assess substance use disorder, with cigarette smoking being the most assessed substance. Across the range of studies, quantitative methods predominated. In terms of included studies, China and Brazil had the highest counts, with a notable disparity, as only two studies from Africa examined telehealth for substance use disorders. Vorinostat Evaluating telehealth interventions for substance use disorders (SUDs) in low- and middle-income countries (LMICs) has become a substantial area of research. Evaluations of telehealth interventions for substance use disorders highlighted encouraging findings regarding acceptability, feasibility, and effectiveness. Future research directions are suggested in this article, which also identifies knowledge gaps and existing research strengths.

The incidence of falls is high amongst individuals with multiple sclerosis, a condition often associated with significant health problems. Biannual clinical visits, while standard, prove insufficient for adequately monitoring the variable symptoms of MS. Recent advancements in remote monitoring, utilizing wearable sensors, have demonstrated a capacity for discerning disease variability. Studies conducted in controlled laboratory settings have shown that fall risk can be identified through analysis of walking data collected using wearable sensors, although the external validity of these findings for real-world domestic situations remains unclear. 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 includes eleven body-site inertial measurement unit data, along with patient survey responses and neurological assessments, and two days of chest and right thigh free-living sensor recordings. Repeat assessments for some individuals, covering a period of six months (n = 28) and one year (n = 15), are likewise available in their records. Viral genetics To showcase the practical utility of these data, we investigate free-living walking episodes for assessing fall risk in people with multiple sclerosis, comparing the gathered data with controlled environment data, and examining the effect of bout duration on gait parameters and fall risk estimation. 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. Free-living walking, particularly in short durations, demonstrated the lowest correlation with laboratory-based walking; longer free-living walking periods exhibited more pronounced variations between individuals prone to falls and those who did not; and aggregating data from all free-living walking bouts generated the most potent classification system for fall risk assessment.

The crucial role of mobile health (mHealth) technologies in shaping our healthcare system is undeniable. This research investigated the implementability (in terms of compliance, user-friendliness, and patient satisfaction) of a mobile health application for dissemination of Enhanced Recovery Protocols to cardiac surgery patients peri-operatively. The prospective cohort study on patients undergoing cesarean sections was conducted at a single, central location. A mobile health application, developed for the research, was given to patients upon their consent and remained in their use for six to eight weeks after their surgical procedure. Surveys regarding system usability, patient satisfaction, and quality of life were completed by patients both before and after their surgical procedure. Participating in the study were 65 patients, whose average age was 64 years. A post-operative survey gauged the app's overall utilization at 75%, demonstrating a contrast in usage between the 65 and under cohort (68%) and the 65 and over group (81%). Older adult patients undergoing cesarean section (CS) procedures can benefit from mHealth technology for pre and post-operative education, making it a practical solution. The overwhelming number of patients expressed contentment with the application and would favor its use over printed materials.

Logistic regression models are frequently utilized to compute risk scores, which are broadly employed in clinical decision-making. Methods employing machine learning might be effective in finding essential predictors for the creation of parsimonious scores, however, the lack of interpretability associated with the 'black box' nature of variable selection, and potential bias in variable importance derived from a single model, remains a concern. 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. To achieve thorough inference and transparent variable selection, our approach evaluates and visually represents the aggregate contributions of variables, and eliminates non-significant contributions to streamline model development. Model-specific variable contributions are combined to generate an ensemble variable ranking, which seamlessly integrates with the automated and modularized risk scoring system AutoScore for convenient implementation. In investigating early death or unplanned hospital readmission after discharge, ShapleyVIC selected six significant variables from a pool of forty-one candidates, achieving a risk score exhibiting performance similar to a sixteen-variable model developed using machine learning-based rankings. Our research endeavors to provide a structured solution to the interpretation of prediction models within high-stakes decision-making, specifically focusing on variable importance analysis and the construction of parsimonious clinical risk scoring models that are transparent.

Symptoms arising from COVID-19 infection in some individuals can be debilitating, demanding heightened monitoring and supervision. Our strategy involved training an artificial intelligence-based model to predict COVID-19 symptoms and to develop a digital vocal biomarker for straightforward and quantifiable symptom resolution tracking. The prospective Predi-COVID cohort study, which enrolled 272 participants between May 2020 and May 2021, provided the data we used.