Journal of Medical Systems最新文献

This editorial discusses the recent study conducted by Macias et al., revealing that anesthesiologists' case volume history has only a marginal impact on improving operating room efficiency, resulting in minimal clinical significance. The idea that a specific anesthesia team or type of anesthesia could enhance productivity has been previously investigated, yielding similar conclusions. Although the study primarily focuses on the time from patient arrival to the completion of anesthesia induction, excluding the latter part of anesthesia-controlled time, Macias et al. have made a valuable contribution by challenging the prevalent notion that less experienced anesthesiologists adversely affect operating room efficiency.

Gene expression datasets offer a wide range of information about various biological processes. However, it is difficult to find the important genes among the high-dimensional biological data due to the existence of redundant and unimportant ones. Numerous Feature Selection (FS) techniques have been created to get beyond this obstacle. Improving the efficacy and precision of FS methodologies is crucial in order to identify significant genes amongst complicated complex biological data. In this work, we present a novel approach to gene selection called the Sine Cosine and Cuckoo Search Algorithm (SCACSA). This hybrid method is designed to work with well-known machine learning classifiers Support Vector Machine (SVM). Using a dataset on breast cancer, the hybrid gene selection algorithm's performance is carefully assessed and compared to other feature selection methods. To improve the quality of the feature set, we use minimum Redundancy Maximum Relevance (mRMR) as a filtering strategy in the first step. The hybrid SCACSA method is then used to enhance and optimize the gene selection procedure. Lastly, we classify the dataset according to the chosen genes by using the SVM classifier. Given the pivotal role gene selection plays in unraveling complex biological datasets, SCACSA stands out as an invaluable tool for the classification of cancer datasets. The findings help medical practitioners make well-informed decisions about cancer diagnosis and provide them with a valuable tool for navigating the complex world of gene expression data.

Despite the high potential of mHealth-related educational interventions to reach large segments of the population, implementation and adoption of such interventions may be challenging. The objective of this study was to gather knowledge on the feasibility of a future cancer prevention education intervention based on the European Code Against Cancer (ECAC), using a population-based mHealth implementation strategy. A type-2 hybrid effectiveness-implementation study was conducted in a sample of the Spanish general population to assess adoption, fidelity, appropriateness, and acceptability of an intervention to disseminate cancer prevention messages, and willingness to consult further digital information. Participation rates, sociodemographic data, mHealth use patterns and implementation outcomes were calculated. Receiving cancer prevention messages through mHealth is acceptable, appropriate (frequency, timing, understandability and perceived usefulness) and feasible. mHealth users reported high access to the Internet through different devices, high ability and confidence to browse a website, and high willingness to receive cancer prevention messages in the phone, despite low participation rates in comparison to the initial positive response rates. Although adoption of the intervention was high, post-intervention fidelity was seriously hampered by the disruptions caused by the Covid-19 pandemic, which may have affected recall bias. In the context of the Europe's Beating Cancer Plan to increase knowledge about cancer prevention across the European Union, this study contributes to inform the design of future interventions using mHealth at large scale, to ensure a broad coverage and adoption of cancer prevention messages as those promoted by the ECAC.Trial Registration: ClinicalTrials.gov from the U.S. National Library of Medicine, NCT05992792. Registered 15 August 2023 - Retrospectively registered https://clinicaltrials.gov/study/NCT05992792?cond=Cancer&term=NCT05992792&rank=1 .

Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures.

Virtual assistants (VAs) are conversational agents that are able to provide cognitive aid. We developed a VA device for donning and doffing personal protective equipment (PPE) procedures and compared it to live human coaching to explore the feasibility of using VAs in the anesthesiology setting. An automated, scalable, voice-enabled VA was built using the Amazon Alexa device and Alexa Skills application. The device utilized voice-recognition technology to allow a touch-free interactive user experience. Audio and video step-by-step instructions for proper donning and doffing of PPE were programmed and displayed on an Echo Show device. The effectiveness of VA in aiding adherence to PPE protocols was compared to traditional human coaching in a randomized, controlled, single-blinded crossover design. 70 anesthesiologists, anesthesia assistants, respiratory therapists, and operating room nurses performed both donning and doffing procedures, once under step-by-step VA instructional guidance and once with human coaching. Performance was assessed using objective performance evaluation donning and doffing checklists. More participants in the VA group correctly performed the step of “Wash hands for 20 seconds” during both donning and doffing tests. Fewer participants in the VA group correctly performed the steps of “Put cap on and ensure covers hair and ears” and “Tie gown on back and around neck”. The mean doffing total score was higher in the VA group; however, the donning score was similar in both groups. Our study demonstrates that it is feasible to use commercially available technology to create a voice-enabled VA that provides effective step-by-step instructions to healthcare professionals.

Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation. PubMed, Embase, Web of science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database were searched from inception to December 2021. Studies involving the utilization of computer technology and AI to promote the implementation of CPGs were eligible for review. A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems. We conclude that the application of computer technology and AI to CPG implementation mainly concentrated on the guideline content classification and extraction, guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration. The AI methods used for guideline content classification and extraction were pattern-based algorithm and machine learning. In guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration, computer techniques of knowledge representation were the most used.

Physician reviews influence how patients seek care, but dishonest reviews can be detrimental to a physician practice. It is unclear if reviews can be challenged, and processes differ and are not readily apparent. The objective of this observational study was to determine the ability to challenge dishonest negative reviews online. Commonly used websites for physician reviews as of August 2021 were utilized: Healthgrades, Vitals, RateMDs, Zocdoc, Yelp, and Google Business. Each review platform’s website was tested for leaving a physician review and process of appeal and possible removal of a negative review. The process for appeal and the steps involved in posting and appealing a review were determined, whether individuals are verified patients and criteria for verification, how physicians can respond, and the process of appealing false or defamatory reviews.Any individual can leave reviews by searching for a physician’s name or practice and visiting their profile page and can then provide a rating and written review of their experience with the physician. Many require verification to prevent suspicious activity but not proof of a medical visit, allowing significant potential for inaccurate review postings. Posting a review can be done by anyone without verification of a visit. It is challenging for physicians to remove negative online reviews, as most review platforms have strict policies against. This review concludes that physicians should be aware of their online presence and the steps that can be taken to address issues to mitigate adverse effects on their practices.

Adhesion is a critical quality attribute and performance characteristic for transdermal and topical delivery systems (TDS). Regulatory agencies recommend in vivo skin adhesion studies to support the approval of TDS in both new drug applications and abbreviated new drug applications. The current assessment approach in such studies is based on the visual observation of the percent adhesion, defined as the ratio of the area of TDS attached to the skin to the total area of the TDS. Visually estimated percent adhesion by trained clinicians or trial participants creates variability and bias. In addition, trial participants are typically confined to clinical centers during the entire product wear period, which may lead to challenges when translating adhesion performance to the real world setting. In this work we propose to use artificial intelligence and mobile technologies to aid and automate the collection of photographic evidence and estimation of percent adhesion. We trained state-of-art deep learning models with advanced techniques and in-house curated data. Results indicate good performance from the trained models and the potential use of such models in clinical practice is further explored.