Our review procedure entailed the inclusion of 83 studies. Within 12 months of the search, 63% of the reviewed studies were published. https://www.selleckchem.com/products/gi254023x.html Transfer learning techniques were preponderantly applied to time series data (61%) compared to tabular data (18%), audio (12%), and text (8%). An image-based modeling technique was applied in 33 (40%) studies examining non-image data after translating it to image format (e.g.). A spectrogram displays how sound frequencies change over time, offering a visual representation of the acoustic data. Of the studies analyzed, 29 (35%) did not feature authors affiliated with any health-related institutions. Numerous research projects used freely available datasets (66%) and pre-existing models (49%), but only a minority (27%) shared their accompanying code.
This review examines how transfer learning is currently applied to non-visual data within the clinical literature. Over the past several years, transfer learning has experienced substantial growth in application. Across numerous medical specialities, transfer learning's potential in clinical research has been recognized and demonstrated through our review of pertinent studies. Crucial for improving the impact of transfer learning in clinical research are a rise in interdisciplinary partnerships and the broader adoption of reproducible research procedures.
We explore the current trends in the clinical literature on transfer learning methods specifically for non-image data in this scoping review. The number of transfer learning applications has been noticeably higher in the recent few years. Our investigations into transfer learning's potential have shown its applicability in numerous medical specialties within clinical research. To amplify the impact of transfer learning in clinical research, a greater emphasis on interdisciplinary collaborations and wider implementation of reproducible research principles are essential.
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. Globally, a rising interest is evident in exploring the effectiveness of telehealth in the management of 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. A search encompassing five bibliographic databases—PubMed, PsycINFO, Web of Science, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Database of Systematic Reviews—was performed. LMIC-based studies that detailed telehealth approaches and at least one participant's psychoactive substance use were included if their methodologies involved comparisons of outcomes using pre- and post-intervention data, or comparisons between treatment and control groups, or analysis using only post-intervention data, or evaluation of behavioral or health outcomes, or assessments of the intervention's acceptability, feasibility, or effectiveness. Data is presented in a narrative summary format, utilizing charts, graphs, and tables. The search, encompassing a period of 10 years (2010 to 2020) and 14 countries, produced 39 articles that satisfied our inclusion requirements. A substantial rise in research pertaining to this topic was observed during the latter five years, with 2019 exhibiting the maximum number of investigations. Across the reviewed studies, a diversity of methods were employed, combined with a variety of telecommunication modalities utilized for substance use disorder evaluation, with cigarette smoking being the most studied. Quantitative methods were the standard in the majority of these studies. China and Brazil exhibited the greatest representation in the included studies; conversely, only two African studies evaluated telehealth interventions for substance use disorders. Polymer bioregeneration 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. The strengths and shortcomings of current research are analyzed in this article, along with recommendations for future investigation.
A substantial portion of people with multiple sclerosis (MS) experience frequent falls, a factor correlated with adverse health outcomes. Fluctuations in MS symptoms are frequent, making standard, twice-yearly check-ups insufficient to properly track them. A new paradigm in remote disease monitoring, leveraging wearable sensors, has recently surfaced, offering a nuanced perspective on 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. An open-source dataset, derived from remote data of 38 PwMS, is presented to investigate the connection between fall risk and daily activity. The dataset separates participants into 21 fallers and 17 non-fallers, identified through their six-month fall history. Laboratory-collected inertial measurement unit data from eleven body sites, patient-reported surveys and neurological assessments, along with two days' worth of free-living chest and right thigh sensor data, are included in this dataset. Data on some individuals shows repeat assessments at both six months (n = 28) and one year (n = 15) after initial evaluation. Blood-based biomarkers To evaluate the efficacy of these data, we investigate the use of free-living walking episodes for identifying fall risk in people with multiple sclerosis (PwMS), comparing these outcomes to those gathered in controlled conditions, and assessing the effect of bout duration on gait features and fall risk estimations. Gait parameters and fall risk classification performance exhibited a dependency on the length of the bout duration. Deep learning models using home data achieved better results than feature-based models. Evaluating individual bouts highlighted deep learning's consistency over full bouts, while feature-based models proved more effective with shorter bouts. Brief, free-living walking episodes demonstrated the least similarity to laboratory-based walking; longer bouts of free-living walking revealed more substantial differentiations between fallers and non-fallers; and analyzing the totality of free-living walking patterns achieved the most optimal results in fall risk categorization.
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. Involving patients who underwent cesarean sections, this prospective, cohort study concentrated on a single institution. Upon giving their consent, patients were given access to a mobile health application designed for the study, which they used for a period of six to eight weeks after their surgery. Patients completed pre- and post-operative surveys encompassing system usability, patient satisfaction, and quality of life evaluations. Sixty-five patients, with an average age of 64 years, were involved in the study. The post-surgical survey indicated a 75% overall utilization rate for the app, specifically showing 68% usage among those 65 and younger and 81% among those 65 and older. Peri-operative cesarean section (CS) patient education, specifically for older adults, is achievable with the practical application of mHealth technology. Most patients expressed contentment with the app and would prefer it to using printed documents.
Risk scores, frequently produced through logistic regression modeling, play a significant role in clinical decision-making procedures. Machine learning algorithms can successfully identify pertinent predictors for creating compact scores, but their opaque variable selection process compromises interpretability. Further, variable significance calculated from a solitary model may be skewed. A robust and interpretable variable selection method, incorporating the recently developed Shapley variable importance cloud (ShapleyVIC), is presented, addressing the variability in variable importance across diverse modeling scenarios. Our methodology, by evaluating and graphically presenting variable contributions, enables thorough inference and transparent variable selection. It then eliminates irrelevant contributors, thereby simplifying the process of model building. We construct an ensemble variable ranking based on variable contributions from multiple models, easily integrating with AutoScore, an automated and modularized risk score generator, facilitating practical implementation. A study of early death or unplanned re-admission following hospital discharge employed ShapleyVIC's technique to select six variables from forty-one candidates, creating a risk score that exhibited performance comparable to a sixteen-variable model based on 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.
Impairing symptoms, a common consequence of COVID-19 infection, warrant elevated surveillance. To achieve our objective, we sought to train an AI model to anticipate COVID-19 symptoms and extract a digital vocal biomarker to quantify and expedite symptom recovery. Within the Predi-COVID prospective cohort study, data from 272 participants enrolled between May 2020 and May 2021 were incorporated into our study.