As a new concept that emerged in the middle of 1990's, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets. Data mining can uncover new biomedical and healthcare knowledge for clinical and administrative decision making as well as generate scientific hypotheses from large experimental data, clinical databases, and/or biomedical literature. This review first introduces data mining in general (e.g., the background, definition, and process of data mining), discusses the major differences between statistics and data mining and then speaks to the uniqueness of data mining in the biomedical and healthcare fields. A brief summarization of various data mining algorithms used for classification, clustering, and association as well as their respective advantages and drawbacks is also presented. Suggested guidelines on how to use data mining algorithms in each area of classification, clustering, and association are offered along with three examples of how data mining has been used in the healthcare industry. Given the successful application of data mining by health related organizations that has helped to predict health insurance fraud and under-diagnosed patients, and identify and classify at-risk people in terms of health with the goal of reducing healthcare cost, we introduce how data mining technologies (in each area of classification, clustering, and association) have been used for a multitude of purposes, including research in the biomedical and healthcare fields. A discussion of the technologies available to enable the prediction of healthcare costs (including length of hospital stay), disease diagnosis and prognosis, and the discovery of hidden biomedical and healthcare patterns from related databases is offered along with a discussion of the use of data mining to discover such relationships as those between health conditions and a disease, relationships among diseases, and relationships among drugs. The article concludes with a discussion of the problems that hamper the clinical use of data mining by health professionals.

Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients.

The finger length ratio (2D:4D) is a biomarker for the prenatal levels of testosterone. The prenatal effects of testosterone on the development of brain hemispheres are considered as a key factor in the etiology of left-handedness. We tested whether 2D:4D correlates with the type of handedness in males (18 right-handers and 18 left-handers). Right hand 2D:4D, left hand 2D:4D, average 2D:4D, and the difference between right and left 2D:4D (D(r-l)) were compared. The value of D(r-l) in left-handers was significantly lower than D(r-l) in right-handers. This result can be interpreted as proof that left-handers were subjected to higher levels of testosterone in utero.

The most exciting phrase to hear in science, the one that heralds new discoveries, is not 'Eureka!' but 'That's funny...' -Isaac AsimovFrom the discovery of penicillin and x-rays to the development of many of today's chemotherapy agents, serendipitous findings tangential to the researcher's intended purpose, those "That's funny…" moments, have greatly impacted the health and well-being of society. As an information behavior, these unexpected findings are an example of the Opportunistic Discovery of Information (ODI). ODI has been described in many contexts, from information behavior in virtual worlds to the impact of information encountering on health behaviors. Yet, little is known about instances of ODI within the context of scientific research. This study uses content analysis to reveal reported instances of ODI in recently published biomedical literature. Our findings propose a taxonomy of term use indicating the presence of serendipity in the research process and reveal the relationship between the authors' word choice for serendipity and specific types of ODI experiences.

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