A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

BACKGROUND: Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering. Specifically, the role of feature selection and extraction in enhancing model accuracy has been underexplored. OBJECTIVES: This scoping review aims to fulfill two primary objectives: To identify pivotal features for predicting sepsis across a variety of ML models, providing valuable insights for future model development, and To assess model efficacy through performance metrics including AUROC, sensitivity, and specificity. RESULTS: The analysis included 29 studies across diverse clinical settings such as Intensive Care Units (ICU), Emergency Departments, and others, encompassing 1,147,202 patients. The review highlighted the diversity in prediction strategies and timeframes. It was found that feature extraction techniques notably outperformed others in terms of sensitivity and AUROC values, thus indicating their critical role in improving sepsis prediction models. CONCLUSION: Key dynamic indicators, including vital signs and critical laboratory values, are instrumental in the early detection of sepsis. Applying feature selection methods significantly boosts model precision, with models like Random Forest and XG Boost showing promising results. Furthermore, Deep Learning models (DL) reveal unique insights, spotlighting the pivotal role of feature engineering in sepsis prediction, which could greatly benefit clinical practice.

The Accuracy of Wearable Photoplethysmography Sensors for Telehealth Monitoring: A Scoping Review.

Background and Objectives: Photoplethysmography (PPG) sensors have been increasingly used for remote patient monitoring, especially during the COVID-19 pandemic, for the management of chronic diseases and neurological disorders. There is an urgent need to evaluate the accuracy of these devices. This scoping review considers the latest applications of wearable PPG sensors with a focus on studies that used wearable PPG sensors to monitor various health parameters. The primary objective is to report the accuracy of the PPG sensors in both real-world and clinical settings. Methods: This scoping review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Studies were identified by querying the Medline, Embase, IEEE, and CINAHL databases. The goal was to capture eligible studies that used PPG sensors to monitor various health parameters for populations with a minimum of 30 participants, with at least some of the population having relevant health issues. A total of 2,996 articles were screened and 28 are included in this review. Results: The health parameters and disorders identified and investigated in this study include heart rate and heart rate variability, atrial fibrillation, blood pressure (BP), obstructive sleep apnea, blood glucose, heart failure, and respiratory rate. An overview of the algorithms used, and their limitations is provided. Conclusion: Some of the barriers identified in evaluating the accuracy of multiple types of wearable devices include the absence of reporting standard accuracy metrics and a general paucity of studies with large subject size in real-world settings, especially for parameters such as BP.

Telemedicine in Your Pocket: An Alternative Teleconsultation Tool in a Pandemic and in Resource-Poor Settings.

Introduction: Owing to the coronavirus disease 2019 (COVID-19) pandemic, digitalization is booming in all sectors, including health care. This study provides evidence of using a social networking mobile app as a telemedicine tool during the pandemic in India. Methods: The data include telemedicine conversations of 318 COVID-19 patients who were consulted over WhatsApp during the period March 31 to April 16, 2021, and monitored during a quarantine period of 17 days. Results: In total, 2,089 consultations were made by these patients during the study period. Diagnostic reports and recommendations were suggested through WhatsApp 74 times, and 30 patients were advised to admit themselves to the hospital due to increasing symptom severity. Conclusions: WhatsApp efficiently helped remotely manage hundreds of patients, thereby avoiding unnecessary travel to hospitals. Future study implies the need to acquire information on patient satisfaction during the teleconsultations and further encourage applications of commonly used social networking apps as alternative tools for consultations during a pandemic situation.

Exploring the Potential Use of Wearable Devices as a Prognostic Tool among Patients in Hospice Care.

Background: Smartphones and wearable devices have become a part and parcel of the healthcare industry. The use of wearable technology has already proved its potentials in improving healthcare research, clinical work, and patient care. The real time data allows the care providers to monitor the patients' symptoms remotely, prioritize the patients' visits, assist in decision-making, and carry out advanced care planning. Objectives: The primary objective of our study was to investigate the potential use of wearable devices as a prognosis tool among patients in hospice care and palliative care, and the secondary objective was to examine the association between wearable devices and clinical data in the context of patient outcomes, such as discharge and deceased at various time intervals. Methods: We employed a prospective observational research approach to continuously monitor the hand movements of the selected 68 patients between December 2019 and June 2022 via an actigraphy device at hospice or palliative care ward of Taipei Medical University Hospital (TMUH) in Taiwan. Results: The results revealed that the patients with higher scores in the Karnofsky Performance Status (KPS), and Palliative Performance Scale (PPS) tended to live at discharge, while Palliative Prognostic Score (PaP) and Palliative prognostic Index (PPI) also shared the similar trend. In addition, the results also confirmed that all these evaluating tools only suggested rough rather than accurate and definite prediction. The outcomes (May be Discharge (MBD) or expired) were positively correlated with accumulated angle and spin values, i.e., the patients who survived had higher angle and spin values as compared to those who died/expired. Conclusion: The outcomes had higher correlation with angle value compared to spin and ACT. The correlation value increased within the first 48 h and then began to decline. We recommend rigorous prospective observational studies/randomized control trials with many participants for the investigations in the future.

A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients.

Background and Objectives: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention. This creates a burden on hospitals and affects the overall management and administration of the hospitals. Hence, this study aimed to develop a machine learning (ML) model to predict the Length of Stay (LoS) and mortality among T2DM and HTN inpatients. Materials and Methods: Using Taiwan's National Health Insurance Research Database (NHIRD), this cohort study consisted of 58,618 patients, where 25,868 had T2DM, 32,750 had HTN, and 6419 had both T2DM and HTN. We analyzed the data with different machine learning models for the prediction of LoS and mortality. The evaluation was done by plotting descriptive statistical graphs, feature importance, precision-recall curve, accuracy plots, and AUC. The training and testing data were set at a ratio of 8:2 before applying ML algorithms. Results: XGBoost showed the best performance in predicting LoS (R2 0.633; RMSE 0.386; MAE 0.123), and RF resulted in a slightly lower performance (R2 0.591; RMSE 0.401; MAE 0.027). Logistic Regression (LoR) performed the best in predicting mortality (CV Score 0.9779; Test Score 0.9728; Precision 0.9432; Recall 0.9786; AUC 0.97 and AUPR 0.93), closely followed by Ridge Classifier (CV Score 0.9736; Test Score 0.9692; Precision 0.9312; Recall 0.9463; AUC 0.94 and AUPR 0.89). Conclusions: We developed a robust prediction model for LoS and mortality of T2DM and HTN inpatients. Linear Regression showed the best performance for LoS, and Logistic Regression performed the best in predicting mortality. The results showed that ML algorithms can not only help healthcare professionals in data-driven decision-making but can also facilitate early intervention and resource planning.

Predicting severe outcomes in Covid-19 related illness using only patient demographics, comorbidities and symptoms.

OBJECTIVE: Development of a risk-stratification model to predict severe Covid-19 related illness, using only presenting symptoms, comorbidities and demographic data. MATERIALS AND METHODS: We performed a case-control study with cases being those with severe disease, defined as ICU admission, mechanical ventilation, death or discharge to hospice, and controls being those with non-severe disease. Predictor variables included patient demographics, symptoms and past medical history. Participants were 556 patients with laboratory confirmed Covid-19 and were included consecutively after presenting to the emergency department at a tertiary care center from March 1, 2020 to April 21, 2020 RESULTS: Most common symptoms included cough (82%), dyspnea (75%), and fever/chills (77%), with 96% reporting at least one of these. Multivariable logistic regression analysis found that increasing age (adjusted odds ratio [OR], 1.05; 95% confidence interval [CI], 1.03-1.06), dyspnea (OR, 2.56; 95% CI: 1.51-4.33), male sex (OR, 1.70; 95% CI: 1.10-2.64), immunocompromised status (OR, 2.22; 95% CI: 1.17-4.16) and CKD (OR, 1.76; 95% CI: 1.01-3.06) were significant predictors of severe Covid-19 infection. Hyperlipidemia was found to be negatively associated with severe disease (OR, 0.54; 95% CI: 0.33-0.90). A predictive equation based on these variables demonstrated fair ability to discriminate severe vs non-severe outcomes using only this historical information (AUC: 0.76). CONCLUSIONS: Severe Covid-19 illness can be predicted using data that could be obtained from a remote screening. With validation, this model could possibly be used for remote triage to prioritize evaluation based on susceptibility to severe disease while avoiding unnecessary waiting room exposure.

Data Analytics and Applications of the Wearable Sensors in Healthcare: An Overview.

Improving health and lives of people is undoubtedly one of the prime goals of healthcare organizations, policy-makers, and leaders around the world. [...].

Evaluation of Free, Open-source, Web-based DICOM Viewers for the Indian National Telemedicine Service (eSanjeevani).

The Digital Image and Communications in Medicine (DICOM) viewer is a very useful component in telemedicine applications. Owing to increased demand, adoption, and prospects of browser-based software in the recent past, web-based DICOM viewers have gained significant ground. There are myriad web-based DICOM viewers which are open source and are available free of cost as stand-alone applications. These freely available tools have rich functionality like the commercial ones. To find an optimal DICOM viewer for integration with a web-based telemedicine solution is quite a challenge, and no research has gone into assessing these freely available DICOM viewers. This research assessed a range of web-based, open-source, and freely available DICOM viewers from the perspective of their integration with the Indian National Telemedicine Solution (eSanjeevani). To introduce teleradiology module in eSanjeevani, a study is carried out to enable viewing of radiological images through DICOM viewer. eSanjeevani is being prepared for a national roll-out at 155,000 health and wellness centers across rural India by the Ministry of Health and Family Welfare (Government of India) under the Ayushman Bharat Scheme (the world's largest health insurance scheme). In total, 13 free, open-source, and web-based DICOM viewers were identified for evaluation; however, only six were shortlisted as assessed. This study can serve as a one-stop source for researchers looking for a suitable DICOM viewer for their healthcare IT applications.

Virtual reality among the elderly: a usefulness and acceptance study from Taiwan.

BACKGROUND: Virtual reality (VR) has several applications in the medical domain and also generates a secure environment to carry out activities. Evaluation of the effectiveness of VR among older populations revealed positive effects of VR as a tool to reduce risks of falls and also improve the social and emotional well-being of older adults. The decline in physical and mental health, the loss of functional capabilities, and a weakening of social ties represent obstacles towards active aging among older adults and indicate a need for support. Existing research focused on the effects of VR among older populations, and its uses and benefits. Our study investigated the acceptance and use of VR by the elderly. METHODS: This pilot study was conducted on 30 older adults who voluntarily participated during March to May 2018. Nine VR applications that promote physical activities, motivate users, and provide entertainment were chosen for this study. Participants were asked to use any one of the applications of their choice for 15 min twice a week for 6 weeks. At the end of 6 weeks, participants were asked to fill out a questionnaire based on the Technology Acceptance Model and a literature review, to evaluate their acceptance of VR technology. Cronbach's alpha reliability analysis was used to test the internal consistency of the questionnaire items. Pearson's product moment correlation was used to examine the validity of the questionnaire. A linear regression and mediation analysis were utilized to identify relationships among the variables of the questionnaire. RESULTS: In total, six male and 24 female participants aged 60~95 years volunteered to participate in the study. Perceived usefulness, perceived ease of use, social norms, and perceived enjoyment were seen to have had significant effects on the intention to use VR. Participants agreed to a large extent regarding the perceived usefulness, perceived enjoyment, and their experience of using VR. Thus, VR was seen to have high acceptance among this elderly population. CONCLUSIONS: Older people have positive perceptions towards accepting and using VR to support active aging. They perceived VR to be useful, easy to use, and an enjoyable experience, implying positive attitudes toward adopting this new technology.

Correlation between Diabetes Mellitus and Knee Osteoarthritis: A Dry-To-Wet Lab Approach.

Recent years have witnessed an increased prevalence of knee osteoarthritis (KOA) among diabetes mellitus (DM) patients-conditions which might share common risk factors such as obesity and advanced aging. Therefore, we conducted dry-to-wet lab research approaches to assess the correlation of type 1 DM (T1DM) and type 2 DM (T2DM) with KOA among all age and genders of Taiwanese population. The strength of association (odds ratio: OR) was analyzed using a phenome-wide association study portal. Populations of 37,353 T1DM and 1,218,254 T2DM were included. We observed a significant association of KOA with T1DM (OR: 1.40 (1.33⁻1.47), p< 0.0001) and T2DM (OR: 2.75 (2.72⁻2.78), p< 0.0001). The association between T1DM and KOA among the obese (OR: 0.99 (0.54⁻1.67), p = 0.0477) was insignificant compared to the non-obese (OR: 1.40 (1.33⁻1.48), p < 0.0001). Interestingly, a higher association between T2DM and KOA among non-obese persons (OR: 2.75, (2.72⁻2.79), p < 0.0001) compared to the obese (OR: 1.71 (1.55⁻1.89), p < 0.0001) was noted. Further, histopathologic and Western blot studies of diabetic mice knee joints revealed enhanced carboxymethyl lysine (advanced glycation end product), matrix metalloproteinase-1, and reduced cartilage-specific proteins, including type II collagen (Col II), SOX9, and aggrecan (AGN), indicating deteriorated articular cartilage and proteoglycans. Results indicate that DM is strongly associated with KOA, and obesity may not be a confounding factor.

Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients.

Evidence has revealed interesting associations of clinical and social parameters with violent behaviors of patients with psychiatric disorders. Men are more violent preceding and during hospitalization, whereas women are more violent than men throughout the 3days following a hospital admission. It has also been proven that mental disorders may be a consistent risk factor for the occurrence of violence. In order to better understand violent behaviors of patients with psychiatric disorders, it is important to investigate both the clinical symptoms and psychosocial factors that accompany violence in these patients. In this study, we utilized a dataset released by the Partners Healthcare and Neuropsychiatric Genome-scale and RDoC Individualized Domains project of Harvard Medical School to develop a unique text mining pipeline that processes unstructured clinical data in order to recognize clinical and social parameters such asage, gender, history of alcohol use, and violent behaviors, and explored the associations between these parameters and violent behaviors of patients with psychiatric disorders. The aim of our work was to demonstrate the feasibility of mining factors that are strongly associated with violent behaviors among psychiatric patients from unstructured psychiatric evaluation records using clinical text mining. Experiment results showed that stimulants, followed by a family history of violent behavior, suicidal behaviors, and financial stress were strongly associated with violent behaviors. Key aspects explicated in this paper include employing our text mining pipeline to extract clinical and social factors linked with violent behaviors, generating association rules to uncover possible associations between these factors and violent behaviors, and lastly the ranking of top rules associated with violent behaviors using statistical analysis and interpretation.

Benzodiazepines use and breast cancer risk: A population-based study and gene expression profiling evidence.

The aim of this study was to investigate whether long-term use of Benzodiazepines (BZDs) is associated with breast cancer risk through the combination of population-based observational and gene expression profiling evidence. We conducted a population-based case-control study by using 1998 to 2009year Taiwan National Health Insurance Research Database and investigated the association between BZDs use and breast cancer risk. We selected subjects age of >20years old and six eligible controls matched for age, sex and the index date (i.e., free of any cancer at the case diagnosis date) by using propensity scores. A bioinformatics analysis approach was also performed for the identification of oncogenesis effects of BZDs on breast cancer. We used breast cancer gene expression data from the Cancer Genome Atlas and perturbagen signatures of BZDs from the Library of Integrated Cellular Signatures database in order to identify the oncogenesis effects of BZDs on breast cancer. We found evidence of increased breast cancer risk for diazepam (OR, 1.16; 95%CI, 0.95-1.42; connectivity score [CS], 0.3016), zolpidem (OR, 1.11; 95%CI, 0.95-1.30; CS, 0.2738), but not for lorazepam (OR, 1.04; 95%CI, 0.89-1.23; CS, -0.2952) consistently in both methods. The finding for alparazolam was contradictory from the two methods. Diazepam and zolpidem trends showed association, although not statistically significant, with breast cancer risk in both epidemiological and bioinformatics analyses outcomes. The methodological value of our study is in introducing the way of combining epidemiological and bioinformatics approaches in order to answer a common scientific question. Combining the two approaches would be a substantial step towards uncovering, validation and further application of previously unknown scientific knowledge to the emerging field of precision medicine informatics.

Gender-based personalized pharmacotherapy: a systematic review.

PURPOSE: In general, male and female are prescribed the same amount of dosage even if most of the cases female required less dosage than male. Physicians are often facing problem on appropriate drug dosing, efficient treatment, and drug safety for a female in general. To identify and synthesize evidence about the effectiveness of gender-based therapy; provide the information to patients, providers, and health system intervention to ensure safety treatment; and minimize adverse effects. METHODS: We performed a systematic review to evaluate the effect of gender difference on pharmacotherapy. Published articles from January 1990 to December 2015 were identified using specific term in MEDLINE (PubMed), EMBASE, and the Cochrane library according to search strategies that strengthen the reporting of observational and clinical studies. RESULTS: Twenty-six studies fulfilled the inclusion criteria for this systematic review, yielding a total of 6309 subjects. We observed that female generally has a lower the gastric emptying time, gastric PH, lean body mass, and higher plasma volume, BMI, body fat, as well as reduce hepatic clearance, difference in activity of Cytochrome P450 enzyme, and metabolize drugs at different rate compared with male. Other significant factors such as conjugation, protein binding, absorption, and the renal elimination could not be ignored. However, these differences can lead to adverse effects in female especially for the pregnant, post-menopausal, and elderly women. CONCLUSION: This systematic review provides an evidence for the effectiveness of dosage difference to ensure safety and efficient treatment. Future studies on the current topic are, therefore, recommended to reduce the adverse effect of therapy.

Risk factors for ectopic pregnancy in the Taiwanese population: a retrospective observational study.

OBJECTIVE: The aim of this study is to explore the risk factors for ectopic pregnancy (EP) in the Taiwanese population and to identify any unknown risk factors for EP that could assist awareness and diagnosis. METHODS: This retrospective case-control study utilizes Taiwan's National Health Insurance Research Database (NHIRD). Analysis was conducted with a one million sample database representative of the country's population. The case group comprised of 6637 individuals in the database that coded for EP using the ICD-9 classification system from January 2003 to December 2011. The control group comprised of 13,270 females matched by age that were never diagnosed with EP during the study period. RESULTS: This study provides an analysis of the risk factors of EP utilizing NHIRD. Among the 6637 people diagnosed with EP, logistic regression with age adjustment showed that the highest risk factor was endometriosis (OR = 8.84, CI 5.13-15.23), followed by polycystic ovary (OR = 7.74, CI 3.37-17.79) and benign neoplasm of the ovary (6.01, CI 2.18-16.54). All odds ratios were determined to be statistically significant at p < 0.001. CONCLUSION: This study has identified the diagnosis of endometriosis and polycystic ovaries as the two largest risk factors for EP in Taiwan, and has also newly identified benign neoplasm of ovary and uterine leiomyoma as risk factors for EP.

Utilizing Health Information Technology to Support Universal Healthcare Delivery: Experience of a National Healthcare System.

Recent discussions have focused on using health information technology (HIT) to support goals related to universal healthcare delivery. These discussions have generally not reflected on the experience of countries with a large amount of experience using HIT to support universal healthcare on a national level. HIT was compared globally by using data from the Ministry of the Interior, Republic of China (Taiwan). Taiwan has been providing universal healthcare since 1995 and began to strategically implement HIT on a national level at that time. Today the national-level HIT system is more extensive in Taiwan than in many other countries and is used to aid administration, clinical care, and public health. The experience of Taiwan thus can provide an illustration of how HIT can be used to support universal healthcare delivery. In this article we present an overview of some key historical developments and successes in the adoption of HIT in Taiwan over a 17-year period, as well as some more recent developments. We use this experience to offer some strategic perspectives on how it can aid in the adoption of large-scale HIT systems and on how HIT can be used to support universal healthcare delivery.

Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST.

BACKGROUND: The completeness of adverse event (AE) reports, crucial for assessing putative causal relationships, is measured using the vigiGrade completeness score in VigiBase, the World Health Organization global database of reported potential AEs. Malaysian reports have surpassed the global average score (approximately 0.44), achieving a 5-year average of 0.79 (SD 0.23) as of 2019 and approaching the benchmark for well-documented reports (0.80). However, the contributing factors to this relatively high report completeness score remain unexplored. OBJECTIVE: This study aims to explore the main drivers influencing the completeness of Malaysian AE reports in VigiBase over a 15-year period using vigiGrade. A secondary objective was to understand the strategic measures taken by the Malaysian authorities leading to enhanced report completeness across different time frames. METHODS: We analyzed 132,738 Malaysian reports (2005-2019) recorded in VigiBase up to February 2021 split into historical International Drug Information System (INTDIS; n=63,943, 48.17% in 2005-2016) and newer E2B (n=68,795, 51.83% in 2015-2019) format subsets. For machine learning analyses, we performed a 2-stage feature selection followed by a random forest classifier to identify the top features predicting well-documented reports. We subsequently applied tree Shapley additive explanations to examine the magnitude, prevalence, and direction of feature effects. In addition, we conducted time-series analyses to evaluate chronological trends and potential influences of key interventions on reporting quality. RESULTS: Among the analyzed reports, 42.84% (56,877/132,738) were well documented, with an increase of 65.37% (53,929/82,497) since 2015. Over two-thirds (46,186/68,795, 67.14%) of the Malaysian E2B reports were well documented compared to INTDIS reports at 16.72% (10,691/63,943). For INTDIS reports, higher pharmacovigilance center staffing was the primary feature positively associated with being well documented. In recent E2B reports, the top positive features included reaction abated upon drug dechallenge, reaction onset or drug use duration of <1 week, dosing interval of <1 day, reports from public specialist hospitals, reports by pharmacists, and reaction duration between 1 and 6 days. In contrast, reports from product registration holders and other health care professionals and reactions involving product substitution issues negatively affected the quality of E2B reports. Multifaceted strategies and interventions comprising policy changes, continuity of education, and human resource development laid the groundwork for AE reporting in Malaysia, whereas advancements in technological infrastructure, pharmacovigilance databases, and reporting tools concurred with increases in both the quantity and quality of AE reports. CONCLUSIONS: Through interpretable machine learning and time-series analyses, this study identified key features that positively or negatively influence the completeness of Malaysian AE reports and unveiled how Malaysia has developed its pharmacovigilance capacity via multifaceted strategies and interventions. These findings will guide future work in enhancing pharmacovigilance and public health.

The Impact of Social and Broadcast Media on Public Health Initiatives: Study of the COVID-19 Infodemics.

The COVID-19 outbreak, declared a pandemic in March 2020, lacked specific treatments until vaccine development. Medication misinformation via media caused panic, self-prescription, and drug resistance. False propaganda led to shortages. This study analyzes Google Trends for hydroxychloroquine (HCQ), azithromycin, and BCG vaccine searches across six countries. US, Brazil, and India showed interest in HCQ, while Taiwan, Japan, and South Korea focused on BCG vaccine. This article aims to raise awareness of adverse drug reactions, cautioning against self-prescription, political assumptions, and social media during future emergencies.

Misleading health-related information promoted through video-based social media: anorexia on YouTube.

INTRODUCTION: The amount of information being uploaded onto social video platforms, such as YouTube, Vimeo, and Veoh, continues to spiral, making it increasingly difficult to discern reliable health information from misleading content. There are thousands of YouTube videos promoting misleading information about anorexia (eg, anorexia as a healthy lifestyle). OBJECTIVE: The aim of this study was to investigate anorexia-related misinformation disseminated through YouTube videos. METHODS: We retrieved YouTube videos related to anorexia using the keywords anorexia, anorexia nervosa, proana, and thinspo on October 10, 2011.Three doctors reviewed 140 videos with approximately 11 hours of video content, classifying them as informative, pro-anorexia, or others. By informative we mean content describing the health consequences of anorexia and advice on how to recover from it; by pro-anorexia we mean videos promoting anorexia as a fashion, a source of beauty, and that share tips and methods for becoming and remaining anorexic. The 40 most-viewed videos (20 informative and 20 pro-anorexia videos) were assessed to gauge viewer behavior. RESULTS: The interrater agreement of classification was moderate (Fleiss' kappa=0.5), with 29.3% (n=41) being rated as pro-anorexia, 55.7% (n=78) as informative, and 15.0% (n=21) as others. Pro-anorexia videos were favored 3 times more than informative videos (odds ratio [OR] 3.3, 95% CI 3.3-3.4, P<.001). CONCLUSIONS: Pro-anorexia information was identified in 29.3% of anorexia-related videos. Pro-anorexia videos are less common than informative videos; however, in proportional terms, pro-anorexia content is more highly favored and rated by its viewers. Efforts should focus on raising awareness, particularly among teenagers, about the trustworthiness of online information about beauty and healthy lifestyles. Health authorities producing videos to combat anorexia should consider involving celebrities and models to reach a wider audience. More research is needed to study the characteristics of pro-anorexia videos in order to develop algorithms that will automatically detect and filter those videos before they become popular.

Empowering Patients and Transforming Healthcare in the Post-COVID-19 Era: The Role of Digital and Wearable Technologies.

The COVID-19 pandemic has dramatically impacted the global healthcare system, revealing critical gaps in our capacity to provide efficient and effective care to patients, particularly those with chronic diseases [...].