In the course of our review, we examined 83 different studies. A considerable 63% of the examined studies were published in the year preceding and encompassing the search. read more Transfer learning saw its greatest usage with time series data (61%), followed considerably by tabular data (18%), and more narrowly by audio (12%) and text (8%) data. Thirty-three studies (representing 40% of the total) employed an image-based model following the transformation of non-image data into images. A spectrogram displays how sound frequencies change over time, offering a visual representation of the acoustic data. No health-related affiliations were listed for 29 (35%) of the studies' authors. Many research projects employed publicly accessible datasets (66%) and pre-built models (49%), although a smaller number (27%) also made their code accessible.
We outline current clinical literature trends in applying transfer learning techniques to non-image datasets in this scoping review. 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. 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.
The current usage of transfer learning for non-image data in clinical research is surveyed in this scoping review. A rapid rise in the adoption of transfer learning has been observed in recent years. Our work in clinical research has not only identified but also demonstrated the potential of transfer learning across diverse medical specialties. Transfer learning's impact in clinical research can be strengthened through more interdisciplinary collaborations and the wider use of reproducible research practices.
The alarming escalation of substance use disorders (SUDs) and their devastating effects in low- and middle-income countries (LMICs) makes it essential to implement interventions which are compatible with local norms, viable in practice, and demonstrably effective in reducing this considerable burden. Worldwide, there's growing consideration of telehealth interventions as potentially effective solutions for the management of substance use disorders. In this article, a scoping review is used to collate and appraise the evidence for the acceptance, practicality, and success of telehealth in treating substance use disorders (SUDs) within limited-resource nations. Five bibliographic databases, including PubMed, PsycINFO, Web of Science, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library, were utilized for the search process. Telehealth modalities explored in low- and middle-income countries (LMICs) were investigated, and for which participants exhibited at least one type of psychoactive substance use. Studies using methodologies involving comparisons of pre- and post-intervention data, or comparisons between treatment and control groups, or data from the post-intervention period, or analysis of behavioral or health outcomes, or assessments of acceptability, feasibility, and effectiveness were included. Charts, graphs, and tables are employed to present the data in a narrative summary. The search, encompassing a period of 10 years (2010 to 2020) and 14 countries, produced 39 articles that satisfied our inclusion requirements. The volume of research dedicated to this subject dramatically increased over the previous five years, reaching its zenith in the year 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. Quantitative approaches were frequently used in the conducted studies. 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. Cancer biomarker Evaluating telehealth interventions for substance use disorders (SUDs) in low- and middle-income countries (LMICs) has become a substantial area of research. Telehealth interventions demonstrated encouraging levels of acceptance, practicality, and efficacy in the treatment of substance use disorders. Research gaps, areas of strength, and potential future research avenues are highlighted in this article.
The incidence of falls is high amongst individuals with multiple sclerosis, a condition often associated with significant health problems. MS symptom fluctuations are a challenge, as standard twice-yearly clinical appointments often fail to capture these changes. Wearable sensor-based remote monitoring methods have recently gained prominence as a means of detecting disease variations. Prior studies have indicated that the risk of falling can be determined from gait data acquired by wearable sensors in controlled laboratory settings, though the applicability of this data to the fluctuating conditions of domestic environments remains uncertain. We present a novel open-source dataset of remote data from 38 PwMS to examine fall risk and daily activity. Within this dataset, 21 individuals are categorized as fallers and 17 as non-fallers, based on their fall occurrences over six months. 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. Some patients' records contain data from six-month (n = 28) and one-year (n = 15) follow-up assessments. Comparative biology 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. The duration of the bout was found to be a determinant of changes in both gait parameters and the determination of fall risk. Home data analysis revealed deep learning models outperforming feature-based models. Evaluation of individual bouts showed deep learning's success with comprehensive bouts and feature-based models' improved performance with condensed bouts. Short duration free-living walking bouts displayed the least correlation to laboratory walking; longer duration free-living walking bouts provided more substantial differences between fallers and non-fallers; and the accumulation of all free-living walking bouts yielded the most effective performance for fall risk prediction.
Our healthcare system is being augmented and strengthened by the expanding influence of mobile health (mHealth) technologies. The study assessed the potential success (regarding patient adherence, user experience, and satisfaction) of a mobile health app for providing Enhanced Recovery Protocols to cardiac surgery patients during the perioperative period. At a single medical center, a prospective cohort study included patients who had undergone cesarean sections. Patients were furnished with the mHealth application designed for this study at the time of consent, maintaining its use for a period of six to eight weeks after undergoing the surgical procedure. Following the surgical procedure, patients completed surveys for system usability, patient satisfaction, and quality of life, as well as prior to the procedure In total, 65 patients, whose mean age was 64 years, were subjects of the investigation. 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%). mHealth technology proves practical for peri-operative patient education, specifically targeting older adult patients undergoing cesarean section (CS). The application garnered high levels of satisfaction from a majority of patients, who would recommend its use to printed materials.
Clinical decision-making often relies on risk scores, which are frequently a product of calculations using logistic regression models. Although machine-learning approaches might prove effective in pinpointing significant predictors to formulate streamlined scores, the lack of transparency in their variable selection procedures reduces interpretability, and the assessment of variable importance from a single model may introduce bias. Our proposed robust and interpretable variable selection approach, implemented through the newly introduced Shapley variable importance cloud (ShapleyVIC), acknowledges the variability in variable importance across different models. Our approach examines and visually depicts the overall contribution of variables, allowing for thorough inference and a transparent variable selection process, and removes non-essential contributors to simplify the steps in model creation. 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. 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. 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.
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. Data gathered from the prospective Predi-COVID cohort study, which included 272 participants enrolled between May 2020 and May 2021, served as the foundation for our research.