Objective Long-term hypertension self-management requires patients to make daily decisions and be cautious of when to seek medical attention. Although large language model (LLM)–based systems provide new opportunities for conversational self-management support, conventional question-answering conversational paradigms may be insufficient for providing accurate and personalized responses under conditions of fragmented patient expression, incomplete contextual reporting, and changing interaction intents. Therefore, this study aims to address the current limitations by developing and evaluating a context-constructive agentic framework that integrates longitudinal memory, interaction coordination, proactive communication, and safety escalation. Methods This study proposes the Longitudinal Context-constructive Agentic Framework (LongCAF) that structures longitudinal patient–LLM interaction as a unified workflow with five core modules: longitudinal context memory for constructing and maintaining patient-specific context, intent-aware interaction coordination for categorizing and routing heterogeneous interaction intents, interaction synthesis for organizing and generating context-grounded personalized responses, proactive communication for eliciting missing information and clarifying patient concerns when possible, and safety escalation for governing and escalating safety–critical interactions to clinicians. The framework was evaluated in a 6-month real-world deployment. Results LongCAF supported longitudinal context construction and context-grounded patient-system interaction during deployment. Patient-level longitudinal context completeness increased from 42.9% (IQR 14.3%–57.1%) at baseline to 57.1% (IQR 57.1%–71.4%) at follow-up, in which 79,290 clinically relevant entries were added, and non-blood-pressure domains accounted for 49.3% of newly acquired entries. Patient-facing outputs supported by retrieved patient-specific context accounted for 69% of outputs, with retrieved domains spanning multiple domains. Candidate-question adoption reached 84.4% among eligible interactions, and intent-aware interaction coordination achieved a micro-F1 score of 0.911. End-to-end safety escalation achieved a precision of 0.915, recall of 0.977, and F1 score of 0.945. Clinician and patient evaluations suggested that LongCAF-supported responses were clinically relevant, contextually appropriate, and useful for continued self-management. Conclusions The real-world deployment demonstrates the feasibility of LongCAF, highlighting its potential as a practical framework for developing more personalized and clinically governed conversational systems for chronic disease self-management.
Drug-target affinity (DTA) prediction is an important task in computer-aided drug discovery. Existing methods usually use a fixed drug-protein interaction strategy. This design is hard to adapt to heterogeneous samples. It is also limited in cold-start and low-similarity scenarios. This study proposes SIGMA-DTA, a similarity-guided adaptive interaction modeling framework for DTA prediction. The framework uses drug and protein similarities as explicit reasoning priors. It introduces a similarity-driven routing mechanism. The mechanism assigns weights to different interaction paths according to the distance between a sample and the training distribution. This enables sample-specific interaction modeling. Unlike conventional unified interaction schemes, SIGMA-DTA adjusts information propagation under different similarity levels. It can model complex drug-target relationships more effectively. Experiments on the Davis and KIBA datasets verify the effectiveness of the method. The results show that integrating similarity information into the interaction modeling process improves robustness and generalization in DTA prediction.
Objective: Large-scale medical instruction datasets often contain substantial noise and redundancy, leading to high training costs without significant performance gains. Existing data selection and distillation methods rely on single static criteria, failing to capture the diversity and dynamic nature of medical samples during learning. This study proposes TriFuse, a three-stage data distillation framework that integrates multiple complementary learning signals for efficient training of medical question-answering models. Methods: The framework comprises three stages: (1) Proxy-student filtering: utilizing masked language modeling loss to remove information-deficient samples; (2) Gradient-median filtering: under the guidance of a weak teacher model, retaining samples with stable and moderate gradient contributions to suppress noise and outliers; (3) Forgettable-count filtering: tracking sample stability across training epochs to identify and remove samples exhibiting unstable learning behaviors. The framework was applied to the large-scale medical instruction dataset MedS-Ins to construct a refined subset (MedCore) comprising less than 30% of the original data. Results: Models trained on MedCore (28% of the original data) achieved comparable or superior performance across multiple tasks while substantially reducing training time and computational cost compared with full-data training. MedCore-Llama outperformed GPT-4 on French medical QA (65.6%) and HeadQA (63.2%), and achieved state-of-the-art results in Participant Extraction (82.61%) and medical concept explanation. Ablation studies confirmed the necessity of all three filtering stages. Conclusion: TriFuse effectively distills high-value medical training data through multi-signal progressive filtering, significantly reducing computational overhead while maintaining model performance. This framework provides a scalable, low-cost solution for efficient medical language model training in resource-constrained environments.
OBJECTIVE:To evaluate challenges in managing multisite, longitudinal, and multisystem clinical data for Down syndrome (DS) research and identify approaches to improve standardization and reuse. METHODS:This report summarizes the outcomes and lessons learned from a NIH workshop convened by the INCLUDE Project to align and modernize clinical data management (CDM) practices using common data elements (CDEs) and artificial intelligence (AI)-enabled tools. RESULTS:(1) consensus that shared CDEs, ontologies, and consistent data models are foundational for cross-study harmonization and interoperability; (2) identification of complementary electronic health record strategies, including standards-based exchange and research data models to support scalable extraction and analysis; (3) recognition that AI-enabled methods, including natural language processing with human review, can accelerate abstraction of information from clinical narratives and support data harmonization across heterogeneous sources; and (4) prioritization of governance needs for privacy protection, transparency, bias mitigation, and ongoing oversight when applying AI to sensitive health data. CONCLUSION:The major conclusion is that combining standardized CDE-driven design with appropriately governed AI-enabled workflows can reduce manual burden, improve data quality, and enable integrated multimodal research, with lessons that are not only applicable the INCLUDE Project but also to other complex clinical research programs.
