As other sectors advance through the aid of cognitive computing, whereas the health care sector is still evolving, offering more advantages for all consumers. The growing complexities of healthcare are compounded by an aging population that contributes to underprivileged decision-making contributing to adverse impacts on the standard of treatment and raises the cost of treatment. Advances in this field, however, is hampered by numerous challenges that create a gap between the knowledge base and user queries, query inconsistencies, and user domain information set. In recent years, the rapid development with the use of machine learning and artificial intelligence for medical applications has already been shown, from diagnostic heart failure to 1-D cardiovascular beatings and automated finding using multi-dimensional clinical data. Consequently, smart decision support structures are required, which can enable clinicians to make more informed treatment decisions. An innovative solution is to harness increasing healthcare digitization that produces enormous volumes of clinical data contained in e-HCR and merge it with advanced ML software to improve clinical decision-making, thus extending the medication evidence base at the same time. Through this work, we are investigating new methodologies as well as digging at specific real-life technologies already being implemented in the medical sector and concentrating mainly on studying about accurate depictions of patients from e-HCR.

随着其他部门借助认知计算的发展，而医疗保健部门仍在发展，为所有消费者提供更多优势。人口老龄化加剧了医疗保健的复杂性，人口老龄化加剧了决策的不足，对治疗标准产生了不利影响，并提高了治疗成本。但是，该领域的进步受到众多挑战的阻碍，这些挑战在知识库和用户查询，查询不一致以及用户域信息集之间造成了差距。近年来，已经显示出将机器学习和人工智能用于医疗应用的快速发展，从诊断性心力衰竭到一维心血管跳动以及使用多维临床数据的自动查找。所以，需要智能的决策支持结构，这可以使临床医生做出更明智的治疗决策。一种创新的解决方案是利用不断增长的医疗保健数字化技术，该技术可生成e-HCR中包含的大量临床数据，并将其与高级ML软件合并以改善临床决策，从而同时扩展了药物证据基础。通过这项工作，我们正在研究新的方法，并研究已在医疗领域实施的特定的现实生活技术，并将主要精力集中在研究对e-HCR患者的准确描述上。一种创新的解决方案是利用不断增长的医疗保健数字化技术，该技术可生成e-HCR中包含的大量临床数据，并将其与高级ML软件合并以改善临床决策，从而同时扩展了药物证据基础。通过这项工作，我们正在研究新的方法，并研究已在医疗领域实施的特定的现实生活技术，并将主要精力集中在研究对e-HCR患者的准确描述上。一种创新的解决方案是利用不断增长的医疗保健数字化技术，该技术可生成e-HCR中包含的大量临床数据，并将其与高级ML软件合并以改善临床决策，从而同时扩展了药物证据基础。通过这项工作，我们正在研究新的方法，并研究已在医疗领域实施的特定的现实生活技术，并将主要精力集中在研究对e-HCR患者的准确描述上。