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There have been some efforts to define standards for the data mining process, for example, the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006 but has stalled since.
The use of Clinical Data Repositories could provide a wealth of knowledge about patients, their medical conditions, and their outcome. The database could serve as a way to study the relationship and potential patterns between disease progression and management. The term "Medical Data Mining" has been coined for this method of research.
Biomedical data science is a multidisciplinary field which leverages large volumes of data to promote biomedical innovation and discovery. Biomedical data science draws from various fields including Biostatistics , Biomedical informatics , and machine learning , with the goal of understanding biological and medical data.
Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare: (1) claims and cost data, (2) pharmaceutical and research and development (R&D) data, (3) clinical data (such as collected from electronic medical records (EHRs)), and (4) patient behaviors and preferences data (e.g. patient satisfaction or retail ...
The data collected during a clinical trial form the basis of subsequent safety and efficacy analysis which in turn drive decision making on product development in the pharmaceutical industry. The clinical data manager is involved in early discussions about data collection options and then oversees development of data collection tools based on ...
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
In the healthcare industry, health informatics has provided such technological solutions as telemedicine, surgical robots, electronic health records (EHR), Picture Archiving and Communication Systems (PACS), and decision support, artificial intelligence, and machine learning innovations including IBM's Watson and Google's DeepMind platform.
Unstructured health data, unlike structured data, is not standardized. [4] Emails, audio recordings, or physician notes about a patient are examples of unstructured health data. While advances in health information technology have expanded collection and use, the complexity of health data has hindered standardization in the health care industry ...