Scholarlink

<p>The increasing volume and heterogeneity of healthcare data pose significant challenges for developing reliable and interpretable clinical decision support systems. Conventional machine learning approaches often struggle to integrate structured electronic health records, real-time patient inputs, and unstructured clinical narratives at scale, limiting their effectiveness in complex medical settings. This study proposes a scalable, knowledge graph driven big data framework for explainable clinical decision support that unifies heterogeneous healthcare data into a semantically structured representation. The framework integrates RDF-based semantic modeling, domain-specific natural language processing for entity extraction, and graph-based reasoning to map patient-reported symptoms to evidence-based treatment guidelines. Large-scale clinical data from the MIMIC-III database comprising over 40,000 hospital admissions real-time patient records, and international clinical protocols from the International Diabetes Federation (IDF) are incorporated to enable dynamic, data-driven decision making. Experimental evaluation demonstrates strong predictive performance in detecting critical diabetic conditions under controlled settings, achieving perfect precision and recall for hypoglycemia and a recall of 0.90 for diabetic ketoacidosis. A macro-averaged F1-score of 0.79 is achieved across all four diabetic condition classes, comparing favorably with rule-based clinical decision support systems and classical machine learning baselines while offering superior explainability. In addition to predictive accuracy, the framework provides transparent, traceable decision paths through knowledge graph reasoning addressing key challenges of interpretability and trust in clinical AI systems. The results highlight the effectiveness of knowledge graph based big data integration for scalable, explainable, and guideline-compliant clinical decision support. The proposed framework is generalizable to other data-intensive healthcare applications, offering a robust foundation for next-generation big data analytics and intelligent decision systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A knowledge graph–driven big data framework for explainable clinical decision support using heterogeneous healthcare data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月7日

查看全文
  • Ubaid Ul Rehman,
  • Hufsa Mohsin,
  • Ghulam Mustafa,
  • Saif Ur Rehman Khan,
  • Husnain Rafiq

摘要

The increasing volume and heterogeneity of healthcare data pose significant challenges for developing reliable and interpretable clinical decision support systems. Conventional machine learning approaches often struggle to integrate structured electronic health records, real-time patient inputs, and unstructured clinical narratives at scale, limiting their effectiveness in complex medical settings. This study proposes a scalable, knowledge graph driven big data framework for explainable clinical decision support that unifies heterogeneous healthcare data into a semantically structured representation. The framework integrates RDF-based semantic modeling, domain-specific natural language processing for entity extraction, and graph-based reasoning to map patient-reported symptoms to evidence-based treatment guidelines. Large-scale clinical data from the MIMIC-III database comprising over 40,000 hospital admissions real-time patient records, and international clinical protocols from the International Diabetes Federation (IDF) are incorporated to enable dynamic, data-driven decision making. Experimental evaluation demonstrates strong predictive performance in detecting critical diabetic conditions under controlled settings, achieving perfect precision and recall for hypoglycemia and a recall of 0.90 for diabetic ketoacidosis. A macro-averaged F1-score of 0.79 is achieved across all four diabetic condition classes, comparing favorably with rule-based clinical decision support systems and classical machine learning baselines while offering superior explainability. In addition to predictive accuracy, the framework provides transparent, traceable decision paths through knowledge graph reasoning addressing key challenges of interpretability and trust in clinical AI systems. The results highlight the effectiveness of knowledge graph based big data integration for scalable, explainable, and guideline-compliant clinical decision support. The proposed framework is generalizable to other data-intensive healthcare applications, offering a robust foundation for next-generation big data analytics and intelligent decision systems.