In the course of our review, we examined 83 different studies. Over half (63%) of the retrieved studies had publication dates falling within 12 months of the search. SGC-CBP30 manufacturer Time series data was the most frequent application of transfer learning, accounting for 61% of cases, followed by tabular data (18%), audio (12%), and text data (8%). Following the conversion of non-image data to images, 33 studies (40% of the total) utilized an image-based modeling approach. Spectrograms, essentially sound-wave images, show the evolution of sound frequencies. 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%).
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. Over the past several years, transfer learning has experienced substantial growth in application. Our identification of studies and subsequent analysis have revealed the applicability of transfer learning across a spectrum of clinical research specialties. Increased interdisciplinary partnerships and a wider acceptance of reproducible research practices are critical for boosting the effectiveness of transfer learning in clinical studies.
A scoping review of the clinical literature highlights current trends in the application of transfer learning to non-image datasets. Transfer learning has become increasingly prevalent and widely adopted over the last several years. Our investigations into transfer learning's potential have shown its applicability in numerous medical specialties within clinical research. Boosting the influence of transfer learning in clinical research demands increased interdisciplinary collaboration and a broader application of reproducible research methodologies.
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. Worldwide, there's growing consideration of telehealth interventions as potentially effective solutions for the management of 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. 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. Research from low- and middle-income countries (LMICs), which outlined telehealth models, revealed psychoactive substance use among participants, employed methods that evaluated outcomes either by comparing pre- and post-intervention data, or contrasted treatment versus control groups, or employed post-intervention data only, or examined behavioral or health outcomes, or measured the acceptability, feasibility, and effectiveness of the interventions. These studies were incorporated into the review. Data is narratively summarized via charts, graphs, and tables. From a ten-year study (2010-2020), spanning 14 countries, our search yielded 39 articles, each satisfying our predetermined eligibility standards. A substantial rise in research pertaining to this topic was observed during the latter five years, with 2019 exhibiting the maximum number of investigations. The reviewed studies displayed substantial methodological differences, and a spectrum of telecommunication methods were utilized for the assessment of substance use disorders, with cigarette smoking emerging as the most frequently studied behavior. Quantitative methods were the standard in the majority of these 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. spine oncology A substantial body of research has emerged, assessing telehealth interventions for substance use disorders (SUDs) in low- and middle-income countries (LMICs). Telehealth-based approaches to substance use disorders exhibited promising levels of acceptability, practicality, and effectiveness. The present article showcases research strengths while also pointing out areas needing further investigation, subsequently proposing potential research avenues for the future.
Multiple sclerosis (MS) sufferers frequently experience falls, which are often accompanied by negative health consequences. Fluctuations in MS symptoms are frequent, making standard, twice-yearly check-ups insufficient to properly track them. Wearable sensor technology has lately revolutionized remote monitoring, offering an approach that acknowledges the variability of diseases. Laboratory-based studies on walking patterns have revealed the potential for identifying fall risk using wearable sensor data, but the extent to which these findings translate to the varied and unpredictable home environments is unknown. To ascertain the correlation between remote data and fall risk, and daily activity performance, we present a new, open-source dataset, derived from 38 PwMS. Twenty-one of these participants are categorized as fallers, based on their six-month fall history, while seventeen are classified as non-fallers. This dataset includes inertial measurement unit readings from eleven body locations, obtained in a laboratory, along with patient self-reported surveys and neurological assessments, plus two days of free-living chest and right thigh sensor data. Repeat assessments of some patients are available for both six months (n = 28) and one year (n = 15). plant-food bioactive compounds Using these data, we investigate the use of free-living walking episodes for evaluating fall risk in people with multiple sclerosis (PwMS), comparing the data with findings from controlled settings and assessing how walking duration impacts gait characteristics and fall risk assessments. Variations in both gait parameters and fall risk classification performance were observed in correlation with the duration of the bout. When evaluating home data, deep learning models surpassed feature-based models. Detailed assessment of individual bouts revealed deep learning's superior performance across all bouts, and feature-based models exhibited stronger results with shorter bouts. Short, independent walks exhibited the smallest resemblance to laboratory-controlled walks; more extended periods of free-living walking offered more distinct characteristics between individuals susceptible to falls and those who were not; and a summation of all free-living walks yielded the most proficient method for predicting fall risk.
The healthcare system is undergoing a transformation, with mobile health (mHealth) technologies playing a progressively crucial role. This research evaluated the viability (considering adherence, usability, and patient satisfaction) of a mobile health application for delivering Enhanced Recovery Protocol information to cardiac surgery patients peri-operatively. This prospective cohort study, focused on a single medical center, included patients who had undergone a cesarean section. 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. Prior to and following surgery, patients participated in surveys evaluating system usability, patient satisfaction, and quality of life. 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%). Educating peri-operative cesarean section (CS) patients, including older adults, using mHealth technology is demonstrably a viable option. A substantial portion of patients found the application satisfactory and would choose it over conventional printed resources.
For clinical decision-making purposes, risk scores are commonly created via logistic regression models. While machine learning methods excel at pinpointing crucial predictive factors for constructing concise scores, their inherent opacity in variable selection hinders interpretability, and the importance assigned to variables based solely on a single model can be skewed. 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. From variable contributions across various models, we derive an ensemble variable ranking, readily integrated into the automated and modularized risk score generator, AutoScore, making implementation simple. A study on early death or unintended re-admission after hospital discharge by ShapleyVIC identified six crucial variables out of forty-one candidates, resulting in a risk score exhibiting comparable performance to a sixteen-variable machine-learning-based ranking model. The recent focus on interpretable prediction models in high-stakes decision-making is furthered by our work, which provides a rigorous framework for detailed variable importance analysis and the development of transparent, parsimonious clinical risk prediction models.
COVID-19 cases can present with impairing symptoms that mandate intensive surveillance procedures. 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. Data from the Predi-COVID prospective cohort, comprising 272 participants enrolled between May 2020 and May 2021, were used in this study.
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