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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · arXiv cs.LG English(EN) · · [2 sources]

    Context-Aware Optimization of Follow-Up Intervals for Type 2 Diabetes Care Using Markov Decision Processes

    Researchers have developed a Contextual Markov Decision Process (CMDP) model to optimize follow-up intervals for Type 2 Diabetes (T2D) patients, moving beyond the American Diabetes Association's fixed guidelines. By analyzing electronic health records from over 22,000 patients, the model identified two distinct risk subpopulations. The CMDP-derived policies recommend adaptive follow-up schedules, suggesting intervals from 1 month for unmeasured labs to 6-12 months for sustained glycemic control, with shorter intervals for higher-risk patients. This approach demonstrated a significant reduction in expected cumulative cost compared to fixed-interval benchmarks. AI

    IMPACT This research demonstrates how AI can personalize chronic disease management, potentially leading to more efficient and cost-effective healthcare delivery.

  2. TOOL · arXiv cs.AI English(EN) ·

    ClinicBot: A Guideline-Grounded Clinical Chatbot with Prioritized Evidence RAG and Verifiable Citations

    Researchers have developed ClinicBot, an AI system designed to provide accurate and verifiable clinical support by grounding responses in official medical guidelines. The system addresses the hallucination problem common in large language models by employing a novel retrieval-augmented generation (RAG) approach. ClinicBot prioritizes evidence based on clinical significance and guideline structure, rather than simple textual similarity, and presents answers with verifiable citations. It has been demonstrated using diabetes-related questions and the American Diabetes Association's Standards of Care. AI

    ClinicBot: A Guideline-Grounded Clinical Chatbot with Prioritized Evidence RAG and Verifiable Citations

    IMPACT This system could improve the reliability of AI in high-stakes medical contexts by ensuring answers are grounded in established guidelines.