Heart failure (HF) is a major cause of morbidity, mortality, and healthcare utilization worldwide, yet early risk stratification remains challenging because prognostic information is distributed across heterogeneous clinical narratives and subtle electrophysiological abnormalities. We propose AGraphHF, a multimodal framework for incident HF risk prediction that integrates 12-lead electrocardiograms (ECGs) with demographic-aware clinical text under a survival objective. The prediction task is temporally anchored at an index ECG and estimates the relative risk of subsequent HF onset under a longitudinal time-to-event formulation. The ECG encoder models individual leads as graph nodes and learns both inter-lead relationships and temporal dependencies using dual graph attention mechanisms. To enhance sensitivity to weak but prognostically relevant signal deviations, the model employs a two-phase adversarial training strategy with dual decoders, where stable reconstruction of typical ECG morphology is followed by deviation-focused refinement conditioned on reconstruction residuals. In parallel, clinical notes are encoded using a demographic-aware language model that extends BioClinicalBERT through age- and sex-conditioned contrastive pre-training. ECG and text representations are aligned in a shared latent space and fused hierarchically to produce patient-level risk estimates. We evaluate AGraphHF on high-risk cohorts derived from the MIMIC-IV database, including patients with hypertension and myocardial infarction. Experimental results demonstrate improved survival risk discrimination compared with established baselines on the hypertension and myocardial infarction cohorts, while also revealing cohort-dependent differences in the contribution of ECG and clinical text. These findings support the use of coordinated graph-based ECG modelling and demographic-aware multimodal integration for HF risk stratification, while emphasizing the need for cohort-specific interpretation of model performance.
Map2VidSeg uses classifier-derived localization maps to generate prompts for video segmentation without segmentation training data. We revisit the pipeline under a fixed 224 × 224 visual-prompt regime and test whether newer foundation-model components improve performance. Starting from the CBMS 2025 system of DINOv2 attention, YOLOE, and SAM-2 on SUN-SEG polyp video, we replace the components along three axes: DINOv3 for map generation, YOLOE-26, for prompt-based segmentation, and SAM-3 for temporal refinement. A completed 2 × 2 prompt-source × YOLOE-stage factorial with SAM-3 fixed shows that the full DINOv3 + YOLOE-26 + SAM-3 upgrade reaches 0.6040 IoU and 0.6684 Dice on SUN-SEG Unseen, whereas the DINOv2 + YOLOE-11 + SAM-3 control reaches 0.6750 IoU and 0.7426 Dice. The prompt-source swap is near neutral with YOLOE-11, whereas YOLOE-26 is weaker at this prompt resolution and the full upgrade underperforms the additive expectation. In a matched comparison using identical cached YOLOE masks, SAM-3 tracker double-pass produced a modest paired gain over SAM-2 double-pass (＋0.0275 IoU; 95% BCa CI [+0.0122,+0.0458]; p=0.0012). A second-task diagnostic study on CholecSeg8k binary instruments shows the same pattern: integration changes improve the YOLOE stage, whereas SAM-3 reduced performance on multi-tool clips under shared-identity tracking, and class-aware tracking eliminated this loss. The results support pipeline-level evaluation and identify class factoring as a third design axis alongside prompt quality and prompt-model compatibility.
Objective: Tabular data learning is crucial in medical data analysis, but it has consistently overlooked a vital component within tables: column names. We aim to leverage column names and pertinent clinical knowledge when training medical tabular data, bridging the gap between the decision-making process of medical experts and conventional models. To protect privacy, all pre-processing is done locally. Materials and methods: We introduce a novel algorithm, named Interpretable Context-Aware Medical Concept Embedding strategy (ICAME), designed to emulate human experts’ data understanding. ICAME is among the first methods that explicitly utilize clinical knowledge in data analysis, applying LLMs for context-aware embedding generation. To ensure the patient data privacy, ICAME stores the embedding of predefined pseudo-values in the local database. This keeps private data local and minimizes the cost of online LLM API usage. Results: ICAME consistently improves performance across classification, regression, and prediction tasks when applied to the MIMIC-III public benchmark (average gains of 1.6%p and 2.9%p on AUROC and AUPRC, respectively). The performance improvement of ICAME is notable in a data-scarce scenario (average gains of 10.5%p and 6.3%p on AUROC and AUPRC, respectively). ICAME also provides interpretability of embeddings, confirming the incorporation of cell values and clinical knowledge. Discussion and Conclusion: ICAME effectively integrates explicit clinical knowledge into tabular data learning for the first time, boosting performance. Additionally, it showcases high scalability, covering numerical, categorical, and binary data types. Through extensive experiments, we demonstrate the potential of ICAME as a promising tool for future medical tabular data analysis.
Remote rehabilitation, or telerehabilitation, has emerged as a scalable modality for delivering rehabilitative care outside clinical settings. Human Digital Twin (HDT) offers a promising paradigm for enabling personalized and adaptive telerehabilitation by continuously representing a patient’s functional condition through digital technologies. Despite the growing interest in this field, current research still lacks a comprehensive understanding of how enabling technologies contribute to HDT-based telerehabilitation. This systematic review provides a technological perspective on telerehabilitation with the aim of identifying recurring architectural patterns, analyzing current technological limitations, and deriving design guidelines for future HDT-based rehabilitation systems. Following the PRISMA methodology, fifty-four studies published between 2021 and 2026 were retrieved from Scopus, ScienceDirect, and PubMed and analyzed according to six dimensions: clinical domain, type of Extended Reality (XR) technology, integration of Internet of Medical Things (IoMT) devices, Artificial Intelligence (AI) task, HDT implementation level, and study maturity level. Across the reviewed studies, a common technological workflow emerged in which sensing technologies, AI models, XR interfaces, and network infrastructures jointly support remote monitoring, rehabilitation assessment, and clinical supervision. To translate these findings into actionable recommendations, a Quality Function Deployment (QFD) analysis was conducted to map clinical and operational requirements to technological features. Based on the QFD analysis, a set of guidelines is proposed to translate the identified technological priorities into practical design principles for future research and the development of integrated, scalable, and clinically deployable telerehabilitation frameworks.
