Patient portals are now considered an integral part of medical care. The aim of this study was to design and evaluate a patient portal for individuals with human T-lymphotropic virus type 1 (HTLV-1) to provide information about their health status.

Chronic kidney disease (CKD) is a global health burden with low awareness among both patients and healthcare providers. Deep learning models (DLMs) have shown promise in interpreting electrocardiograms (ECGs) for various disease and may offer new opportunities for early CKD detection.

In Traditional Chinese Medicine Electronic Medical Records (TCM EMRs), symptom descriptions are often semi-structured, and coarse-grained annotation can lead to symptom nesting and information loss. To address these limitations and improve the precision of symptom representation, this study proposes a fine-grained symptom entity annotation system. Its objective is to convert unstandardized symptom expressions into structured data, thereby enhancing the correlation and standardization of symptom information to support intelligent TCM diagnosis and treatment.

Epidemiological data on rare diseases (RDs) affect the accurate scientific assessment of these diseases and lead to many issues in policy-making, healthcare systems, and legislation. The coding system is crucial for accurately identifying and calculating the incidence rates of each RD. This study focuses on the effectiveness of collecting RD data via the ICD-11 and examines whether the ICD-11 can fully support RD statistics. The findings of this study should provide a foundation for replacing the ICD-10 with the ICD-11.

Cardiovascular magnetic resonance (CMR) offers state-of-the-art volume, function, fibrosis and oedema imaging. Quality assurance (QA) tasks, such as quantitative parameter reproducibility assessments, the evaluation of AI methods, and the assessment of trainees have become essential to CMR. However, the explainability of how qualitative differences impact quantitative differences remains underexplored. Our aim is to demonstrate a semi-automated QA tool, Lazy Luna's (LL) applicability to typical CMR QA application cases.

Hemodialysis patients are at high risk for ICU admission due to elevated mortality, cardiovascular disease, and infection rates. Traditional ICU scoring systems (e.g., APACHE-II, SOFA) demonstrate limited accuracy in this population. This study aimed to identify key risk factors and develop interpretable machine learning (ML) models for predicting ICU outcomes to enable early intervention.

The integration of clinical research data across various institutions faces hurdles due to differing definitions, inconsistent terminologies, and inadequate support for interoperable metadata. While biomedical ontologies offer valuable tools for structuring clinical data, they have not yet been fully utilized for creating comprehensive metadata descriptors, such as variable semantics, statistical summaries, and governance elements essential for data discovery and alignment. We present the Clinical Metadata Exploration Ontology (CMEO) that builds upon well-established ontologies to provide a cohesive representation of study designs, data elements, exploratory statistics, and data reuse permissions. CMEO facilitates semantic querying for study exploration and comparison of data elements across studies, particularly when individual-level data cannot be shared. We demonstrate its utility using metadata from five studies: four heart-failure studies and one wearable-based type 1 diabetes study. After serializing, we executed SPARQL queries that operationalized study-level discovery, variable alignment across studies, and governance-constrained reuse. This FAIR-compliant, metadata-driven integration across heterogeneous sources enables scalable, privacy-conscious research and underpins federated clinical data exploration.

The early and accurate detection of eye diseases play a pivotal role in preventing vision loss and improving patients’ quality of life. It is therefore important to search for methods for improving this detection. It turns out that Artificial Intelligence (AI) has shown great promise for the detection task. However, AI models are often opaque and complex and as a result there has been a slow adoption for these models in clinical settings. In this paper Eye-XAI is introduced which provides an effective approach to the detection combining Explainable Artificial Intelligence (XAI) techniques with symptom analysis to enhance the transparency and interpretability of eye disease detection models. Our results demonstrate that Eye-XAI not only achieves high accuracy (99.11%) for eye disease detection but also provides transparent and interpretable insights into the diagnostic process. The adoption of Eye-XAI can therefore significantly enhance the early detection and management of eye diseases while empowering clinicians with a deeper understanding of its AI-based diagnostic recommendations. Furthermore, this approach promotes patient engagement by facilitating communication and trust between patients and their healthcare providers. Eye-XAI represents a major step towards the integration of XAI in ophthalmology, unlocking new possibilities for improved eye disease diagnosis and treatment.

Shared decision making (SDM) is highly relevant in oncology and cancer care, yet its application within multiple myeloma (MM) remains underexplored. This study aims to (1) investigate SDM implementation in MM clinical practice, (2) assess the role of various stakeholders next to haematologists in the SDM process, and (3) identify barriers and potential solutions to SDM implementation in MM care.

The Short Physical Performance Battery (SPPB) is a widely used assessment tool to evaluate lower extremity function in older adults. However, it requires clinical settings which may not be feasible in all circumstances. This study aimed to develop alternative methods for indirectly estimating SPPB scores using questionnaire responses related to functional abilities.

Determining the appropriate end-of-life (EOL) care model within a short time frame is challenging and requires extensive experience. To the best of our knowledge, no studies have developed automatic systems for identifying the hospice care models: hospice home (HHC), inpatient (HIC), and shared care (HSC). This study aimed to determine the optimal hospice care model for EOL patients with machine learning (ML) methods based on health assessment data.

Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution and is often used for imaging and time series data, but there are no evaluations on its potential benefits for tabular health data. Augmentation increases sample size and is seen as a form of regularization that increases the diversity of small datasets, leading them to perform better on unseen data.