Data Mining, also known as knowledge discovery in data (KDC), is the process of uncovering patterns and other valuable information from large data sets. According to https://www.geeksforgeeks.org/data-mining/, it can ...Data Mining, also known as knowledge discovery in data (KDC), is the process of uncovering patterns and other valuable information from large data sets. According to https://www.geeksforgeeks.org/data-mining/, it can be referred to as knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. With advance research in health sector, there is multitude of Data available in healthcare sector. The general problem then becomes how to use the existing information in a more useful targeted way. Data Mining therefore is the best available technique. The objective of this paper is to review and analyse some of the different Data Mining Techniques such as Application, Classification, Clustering, Regression, etc. applied in the Domain of Healthcare.展开更多
In the previous publication on Volume 15 No 9, September 30, 2022 of IJCN, we analyzed “Data Mining as a Technique for Healthcare Approach”. In this edition, emphasis has been made on the “Development of Data Minin...In the previous publication on Volume 15 No 9, September 30, 2022 of IJCN, we analyzed “Data Mining as a Technique for Healthcare Approach”. In this edition, emphasis has been made on the “Development of Data Mining Model to Detect Cardiovascular Diseases (CVD)”. A Software was developed using the internationally accepted Software Engineering Methodology (SSADM), coding by OOP and packing by prototyping methodologies. Among others, this paper discusses;Cardiovascular diseases, Data Mining Algorithm, Analysis and Information flow of the Present System, Data flow and High level flow of the Proposed System, Modulating, System Design and Development, Hardware and Software Specifications, System Testing, Evaluation and Documentation.展开更多
Personalized medicine is the development of “tailored” therapies that reflect traditional medical approaches with the incorporation of the patient’s unique genetic profile and the environmental basis of the disease...Personalized medicine is the development of “tailored” therapies that reflect traditional medical approaches with the incorporation of the patient’s unique genetic profile and the environmental basis of the disease. These individualized strategies encompass disease prevention and diagnosis, as well as treatment strategies. Today’s healthcare workforce is faced with the availability of massive amounts of patient- and disease-related data. When mined effectively, these data will help produce more efficient and effective diagnoses and treatment, leading to better prognoses for patients at both the individual and population level. Designing preventive and therapeutic interventions for those patients who will benefit most while minimizing side effects and controlling healthcare costs requires bringing diverse data sources together in an analytic paradigm. A resource to clinicians in the development and application of personalized medicine is largely facilitated, perhaps even driven, by the analysis of “big data”. For example, the availability of clinical data warehouses is a significant resource for clinicians in practicing personalized medicine. These “big data” repositories can be queried by clinicians, using specific questions, with data used to gain an understanding of challenges in patient care and treatment. Health informaticians are critical partners to data analytics including the use of technological infrastructures and predictive data mining strategies to access data from multiple sources, assisting clinicians’ interpretation of data and development of personalized, targeted therapy recommendations. In this paper, we look at the concept of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of “big data” and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, and 3) the impact of electronic medical record systems 展开更多
本文从科学研究基本构成角度,对Web of Science中有关医疗健康大数据的文献进行了全面梳理。首先通过文献计量分析了开展医疗健康大数据研究的国家、学科领域、机构和资助来源;接着通过内容分析总结了这些文献的主要研究对象及其特征,...本文从科学研究基本构成角度,对Web of Science中有关医疗健康大数据的文献进行了全面梳理。首先通过文献计量分析了开展医疗健康大数据研究的国家、学科领域、机构和资助来源;接着通过内容分析总结了这些文献的主要研究对象及其特征,介绍了世界范围内较为著名的开放医学数据资源;最后,从基础研究、应用研究和开发研究三方面剖析了医疗健康大数据研究现状,并归纳了各类研究的主要内容和主题。展开更多
文摘Data Mining, also known as knowledge discovery in data (KDC), is the process of uncovering patterns and other valuable information from large data sets. According to https://www.geeksforgeeks.org/data-mining/, it can be referred to as knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. With advance research in health sector, there is multitude of Data available in healthcare sector. The general problem then becomes how to use the existing information in a more useful targeted way. Data Mining therefore is the best available technique. The objective of this paper is to review and analyse some of the different Data Mining Techniques such as Application, Classification, Clustering, Regression, etc. applied in the Domain of Healthcare.
文摘In the previous publication on Volume 15 No 9, September 30, 2022 of IJCN, we analyzed “Data Mining as a Technique for Healthcare Approach”. In this edition, emphasis has been made on the “Development of Data Mining Model to Detect Cardiovascular Diseases (CVD)”. A Software was developed using the internationally accepted Software Engineering Methodology (SSADM), coding by OOP and packing by prototyping methodologies. Among others, this paper discusses;Cardiovascular diseases, Data Mining Algorithm, Analysis and Information flow of the Present System, Data flow and High level flow of the Proposed System, Modulating, System Design and Development, Hardware and Software Specifications, System Testing, Evaluation and Documentation.
文摘Personalized medicine is the development of “tailored” therapies that reflect traditional medical approaches with the incorporation of the patient’s unique genetic profile and the environmental basis of the disease. These individualized strategies encompass disease prevention and diagnosis, as well as treatment strategies. Today’s healthcare workforce is faced with the availability of massive amounts of patient- and disease-related data. When mined effectively, these data will help produce more efficient and effective diagnoses and treatment, leading to better prognoses for patients at both the individual and population level. Designing preventive and therapeutic interventions for those patients who will benefit most while minimizing side effects and controlling healthcare costs requires bringing diverse data sources together in an analytic paradigm. A resource to clinicians in the development and application of personalized medicine is largely facilitated, perhaps even driven, by the analysis of “big data”. For example, the availability of clinical data warehouses is a significant resource for clinicians in practicing personalized medicine. These “big data” repositories can be queried by clinicians, using specific questions, with data used to gain an understanding of challenges in patient care and treatment. Health informaticians are critical partners to data analytics including the use of technological infrastructures and predictive data mining strategies to access data from multiple sources, assisting clinicians’ interpretation of data and development of personalized, targeted therapy recommendations. In this paper, we look at the concept of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of “big data” and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, and 3) the impact of electronic medical record systems
文摘本文从科学研究基本构成角度,对Web of Science中有关医疗健康大数据的文献进行了全面梳理。首先通过文献计量分析了开展医疗健康大数据研究的国家、学科领域、机构和资助来源;接着通过内容分析总结了这些文献的主要研究对象及其特征,介绍了世界范围内较为著名的开放医学数据资源;最后,从基础研究、应用研究和开发研究三方面剖析了医疗健康大数据研究现状,并归纳了各类研究的主要内容和主题。
