Biomedical informatics

BACKGROUND: A digital health technology's success or failure depends on how it is received by users. OBJECTIVES: We conducted a user experience (UX) evaluation among persons who used the Food and Drug Administration-approved Digital Health Feedback System incorporating ingestible sensors (ISs) to capture medication adherence, after they were prescribed oral pre-exposure prophylaxis (PrEP) to prevent HIV infection. We performed an association analysis with baseline participant characteristics, to see if "personas" associated with positive or negative UX emerged...

OBJECTIVES: Ischemic heart disease (IHD) is a significant contributor to global mortality and disability, imposing a substantial social and economic burden on individuals and healthcare systems. To enhance the efficient allocation of medical resources and ultimately benefit a larger population, accurate prediction of healthcare costs is crucial. METHODS: We developed an interpretable IHD hospitalization cost prediction model that integrates network analysis with machine learning...

BACKGROUND: Mobile health (mHealth) wearable devices are increasingly being adopted by individuals to help manage and monitor physiological signals. However, the current state of wearables does not consider the needs of racially minoritized low-socioeconomic status (SES) communities regarding usability, accessibility, and price. This is a critical issue that necessitates immediate attention and resolution. OBJECTIVE: This study's aims were 3-fold, to (1) understand how members of minoritized low-SES communities perceive current mHealth wearable devices, (2) identify the barriers and facilitators toward adoption, and (3) articulate design requirements for future wearable devices to enable equitable access for these communities...

BACKGROUND: Mobile health (mHealth) interventions have immense potential to support disease self-management for people with complex medical conditions following treatment regimens that involve taking medicine and other self-management activities. However, there is no consensus on what discrete behavior change techniques (BCTs) should be used in an effective adherence and self-management-promoting mHealth solution for any chronic illness. Reviewing the extant literature to identify effective, cross-cutting BCTs in mHealth interventions for adherence and self-management promotion could help accelerate the development, evaluation, and dissemination of behavior change interventions with potential generalizability across complex medical conditions...

BACKGROUND: Wearables that measure vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart T-shirt with embedded sensors. Initially, smart T-shirts were designed to aid athletes in their performance analyses. Recently however, researchers have been investigating the use of smart T-shirts as supportive tools in health care. In general, the knowledge on the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are lacking...

Many people in the healthcare industry mistake the turbulent cryptocurrency market for a technology that offers significant benefits to healthcare administration, including improved interoperability, revenue recapture, and enhanced security and patient safety. Recently, these subjects were explored during the ConVerge2Xcelerate (ConV2X) 2023 Symposium held at Loyola University in New Orleans, Louisiana, USA. Who benefits most from this discussion - service providers, those seeking to transform outdated business models, or both? The mention of blockchain technology often discourages conversation, causing those who have sound success solutions to approach the topic from alternate perspectives...

In an era dominated by digital advancements, cybersecurity plays a pivotal role in safeguarding information and systems from evolving threats. The escalating sophistication of cyber threats necessitates a critical examination of the efficacy of contemporary defenses. Recognizing the limitations and gaps in current solutions, this research introduces a pioneering framework aimed at fortifying cyber defenses. Motivated by a comprehensive exploration of research articles, surveys, online media, and practical studies, this study scrutinizes the intricacies of cyber threats and assesses the strengths and weaknesses of existing solutions...

Decentralized clinical trials (DCTs) recently gained attention in research necessary for drug development. While the COVID-19 pandemic proved to be a challenging time in this arena, drug development was a critical area of emphasis in the rapid advancement of vaccines. The DCTs were necessary to allow research activities to occur across many locations. The use of DCTs can profoundly impact reshaping healthcare by enabling participants to partake in clinical trials remotely; however, implementation challenges must be considered as technology expands...

Properly managing healthcare data is a complex endeavor that must balance the requirements and interests of many stakeholders. In this paper, we present the findings from a panel discussion with healthcare professionals and academics, who elaborate on the current situation in healthcare data management as well as the future role that blockchain could play in this sector. Based on the findings of this panel, we structure the research field of healthcare data management and provide numerous avenues for future research...

INTRODUCTION: Scientists use donated biospecimens to create organoids, which are miniature copies of patient tumors that are revolutionizing precision medicine and drug discovery. However, biobanking platforms remove donor identifiers to protect privacy, precluding patients from benefiting from their contributions or sharing information that may be relevant to research outcomes. Decentralized biobanking (de-bi) leverages blockchain technology to empower patient engagement in biospecimen research...

Blockchain technology has become crucial in improving the privacy and security of enterprise applications in the cyber world. However, scalability has become a significant concern for researchers in large organizations, especially those with complex hierarchies and access privileges. As a result, the existing models and consensus algorithms suffer from various issues. Medical centers and healthcare providers are particularly affected by this problem due to the vast amount of data, making it a critical weakness of traditional database management systems...

METHODS: We utilized a 4D framework using ease of implementation, novelty, necessity, and fit of the overall industry to examine the adoption of blockchain technology in the pharmaceutical industry. Based on the 2D framework of difficulty and novelty as driving factors for the development of foundational technologies in the world of business by Iansiti and Lakhani, each application was ranked and scored for the best potential implementation. The potential applications proposed in this paper can be grouped into two main categories...

In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit or produce low prediction accuracy due to overfitting. To address this issue, the focus has shifted towards introducing a novel approaches for feature selection and survival prediction. In this article, we propose a new hybrid feature selection approach that handles high-dimensional bioinformatics datasets for improved survival prediction...

Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results...

A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study explores the use of synthetic images for data augmentation to address the challenge of limited annotated data in colonoscopy lesion classification. We demonstrate that synthetic colonoscopy images generated by Generative Adversarial Network (GAN) inversion can be used as training data to improve polyp classification performance by deep learning models. We invert pairs of images with the same label to a semantically rich and disentangled latent space and manipulate latent representations to produce new synthetic images...

Solubility is not only a significant physical property of molecules but also a vital factor in smallmolecule drug development. Determining drug solubility demands stringent equipment, controlled environments, and substantial human and material resources. The accurate prediction of drug solubility using computational methods has long been a goal for researchers. In this study, we introduce MSCSol, a solubility prediction model that integrates multidimensional molecular structure information. We incorporate a graph neural network with geometric vector perceptrons (GVP-GNN) to encode 3D molecular structures, representing spatial arrangement and orientation of atoms, as well as atomic sequences and interactions...

Achieving accurate bladder wall and tumor segmentation from MRI is critical for diagnosing and treating bladder cancer. However, automated segmentation remains challenging due to factors such as comparable density distributions, intricate tumor morphologies, and unclear boundaries. Considering the attributes of bladder MRI images, we propose an efficient multiscale hierarchical hybrid attention with a transformer (MH2AFormer) for bladder cancer and wall segmentation. Specifically, a multiscale hybrid attention and transformer (MHAT) module in the encoder is designed to adaptively extract and aggregate multiscale hybrid feature representations from the input image...

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Deep Learning (DL) has emerged as an efficient tool for the classification problem in electrocardiogram (ECG)-based SA diagnoses. Despite these advancements, most common conventional feature extractions derived from ECG signals in DL, such as R-peaks and RR intervals, may fail to capture crucial information encompassed within the complete ECG segments. In this study, we propose an innovative approach to address this diagnostic gap by delving deeper into the comprehensive segments of the ECG signal...

BACKGROUND: Posttraumatic stress disorder (PTSD) causes heightened fight-or-flight responses to traumatic memories (i.e., hyperarousal). Although hyperarousal is hypothesized to cause irregular breathing (i.e., respiratory variability), no quantitative markers of respiratory variability have been shown to correspond with PTSD symptoms in humans. OBJECTIVE: In this study, we define interpretable markers of respiration pattern variability (RPV) and investigate whether these markers respond during traumatic memories, correlate with PTSD symptoms, and differ in patients with PTSD...

Background The use of telemedicine in contemporary healthcare has become essential, providing a novel method of delivering care, particularly in rural and underdeveloped areas. This study assesses the telemedicine awareness, knowledge, attitude, skills, and challenges among physicians working in tertiary care hospitals. Methods A cross-sectional study was carried out with 100 doctors from diverse specialties at a tertiary care institution. The questionnaire evaluated five domains: telemedicine awareness and knowledge, telemedicine attitude, telemedicine technology skills, telemedicine utilization patterns, and perceived barriers and educational needs...