Journal of Medical Internet Research

Acute respiratory distress syndrome (ARDS) is a critical condition commonly encountered in the intensive care unit (ICU), characterized by a high incidence and substantial mortality rate. Early detection and accurate prediction of ARDS can significantly improve patient outcomes. While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. This study is the first to systematically evaluate the performance of ARDS prediction models based on multiple quantitative data sources. We compare the effectiveness of ML models via a meta-analysis, revealing factors affecting performance and suggesting strategies to enhance generalization and prediction accuracy.

Perioperative adverse events (PAEs) pose a substantial global health burden, contributing to elevated morbidity, mortality, and health care expenditures. The adoption of clinical decision support systems (CDSS), particularly mobile-based solutions, offers a promising avenue to address these challenges. However, successful implementation hinges on understanding anesthesia providers’ knowledge, attitudes, and willingness to embrace such technologies.

Escalating mental health demand exceeds existing clinical capacity, necessitating scalable digital solutions. However, engagement remains challenging. Conversational agents can enhance engagement by making digital programs more interactive and personalized, but they have not been widely adopted. This study evaluated a digital program for anxiety in comparison to external comparators. The program used an artificial intelligence (AI)–driven conversational agent to deliver clinician-written content via machine learning, with clinician oversight and user support.

As the optimal treatment for end-stage renal disease, kidney transplantation has proven instrumental in enhancing patient survival and quality of life. Suboptimal medication adherence is recognized as an independent risk factor for poor prognosis, graft rejection, and graft loss. In recent years, the advancement of IT has facilitated the integration of eHealth technologies into medical medication management, offering potential solutions to improve patient adherence. However, their efficacy in kidney transplant recipients remains inconclusive.

It is important to explain early diagnosis and treatment plans to patients of prostate cancer due to the different stages that diagnosis is made at and the corresponding stage-specific treatment options, as well as the varying prognoses depending on the choices made. Although various studies have implemented metaverse-based interventions across diverse clinical settings for medical education, there is a lack of publications addressing the implementation and validation of patient education using this technology.

The increasing prevalence of pediatric atopic diseases in China poses substantial risks to children’s physical health, mental well-being, and quality of life. Cognitive behavioral interventions for caregivers are effective in managing pediatric atopic diseases. Existing interventions are typically siloed and lack integration across the comorbidities of the atopic march. The protection motivation theory (PMT) could provide an integrated cognitive behavioral intervention framework for addressing shared pathophysiological mechanisms and unifying management strategies across atopic diseases, while online interventions offer advantages in accessibility, cost-effectiveness, and scalability, particularly for caregiver-mediated pediatric care.

Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction.

Stigma associated with HIV/AIDS continues to be a major barrier to prevention, management, and care. HIV stigma can negatively influence health behaviors. Surveys of the general public in Japan also demonstrated substantial gaps in knowledge of HIV/AIDS. Tweets from the social networking service X (formerly known as Twitter) have been studied to identify stigmas in other disorders but have not yet been used to study HIV stigma in Japan.

Domestic violence (DV) is a significant public health concern affecting the physical and mental well-being of numerous women, imposing a substantial health care burden. However, women facing DV often encounter barriers to seeking in-person help due to stigma, shame, and embarrassment. As a result, many survivors of DV turn to online health communities as a safe and anonymous space to share their experiences and seek support. Understanding the information needs of survivors of DV in online health communities through multiclass classification is crucial for providing timely and appropriate support.

Social media platforms have shown considerable potential in shaping behaviors and have become a central component of public health and organizational outreach efforts. Blood collection agencies increasingly rely on social media not only for donor recruitment and retention but also for promoting donation-related behaviors. Regular, day-to-day status updates form a significant part of the communication strategies implemented by blood banks.

College students and young adults (18-25 years) frequently experience poor sleep quality, with insomnia being particularly prevalent among this population. Given the widespread use of digital devices in the modern world, electronic device–based sleep interventions present a promising solution for improving sleep outcomes. However, their effects in this population remain underexplored.

Redirecting avoidable presentations to alternative care service pathways (ACSPs) may lead to better resource allocation for prehospital emergency care. Stratifying emergency department (ED) presentations by admission risk using diagnosis codes might be useful in identifying patients suitable for ACSPs.