In the 5th Annual Survey (2011) data miners shared examples of situations where data mining is having a positive impact on society. A summary of the top five positive impact example topic areas is available. Below is the full text of the positive impact examples they shared in the topic area of Health / Medical Progress:

• Fast and accurate diagnosis of infectious diseases in patients admitted to ER unit. Reference: "Classification of infectious diseases based on chemiluminescent signatures of phagocytes in whole blood" by Daria Prilutsky, M.Sc.; Boris Rogachev, M.D.; Robert S Marks, Ph.D.; Leslie Lobel, M.D., Ph.D.; Mark Last, Ph.D., to appear in Artificial Intelligence in Medicine.
• Created an improved health risk model tuned to our specific population that helps employees identify their personal risk of chronic disease (CVD, diabetes, cancer), then recommends actions that they can take to avoid them. See http://cathealthbenefits.cat.com/cda/layout?
• m=164481&x=7 for an example.
• More than 90% of the chemicals on the market have not been tested on their potential hazards on humans and environment like carcinogenicity, developmental toxicity, mutagenicity, skin sensitization. If they would be tested in traditional way, i.e., by animal testing, it would require more than 10 million additional animals, billions of euro, and more than 50 years in time. Mathematical models (qsar models) obtained from observation data are going to help reducing, refining, and replacing animal tests in the near future.
• Using analytics to identify health plan members that are non-compliant in keeping their diseases under control (e.g. diabetes). That way the health plan can send educational literature/programs to those most likely to be out of compliance and keep them healthier.
• Too many examples; note what CDC is doing to use DM to increase vaccination rates, etc. Much impact in the public health area.
• Medication adherence--rank-order patients by likelihood of adherence to prescription regimen, allowing healthcare providers to target likely low adherers with incentives, thereby saving healthcare expense and saving lives.
• Developing a system to automatically identify patients who are likely to become addicted to painkillers.
• Data mining of medical research cohort databases to determine patterns of disease, disability and predictors of positive treatment outcomes.
• Understanding patient concerns via analysis of blogs
• Data mining in medical world, prevention of natural disease, data mining to prevent criminal activities.
• Data mining can identify meaningful relationships among the mountains of data available in our current times which would otherwise go overlooked. In the case of Medicine, for example, once a previously unknown relationship is identified, the scientists can then develop a research plan to improve the state of medicine.
• Data Mining of insurance claims data can unearth hidden patterns and can help in better understanding of disease trends and claims patterns.
• Pharmaceutical and clinical trials, genome research to cure disease, optimization for benefit ecology.
• WHO analysis of rare or orphan deceases and world wide disasters.
• Epidemiology applications to curb spread of disease. Disaster response using real time geo data and social/unofficial reporting of impacted areas.
• Quantify Quality of Healthcare to dramatically reduce the costs and risks.
• Early detection of cancer based on genomics.
• Over the course of the year, I have [become] more intrigued about the data visualization. With so much data now available, the key will be on how we present and share this data to provide context and a greater understanding of realms that have not been explored. This can be used to provide better products for consumers, improved experiences and advancement of health care.
• Pharmaceutical area: Identified new drug candidates in the database
• Discovery of genetic links to disease.
• Target use of medical products more accurately
• Medical -- optimize treatments; most person-years per dollar
• Scientific research on micro array and bio signals analysis
• The life science applications have been remarkable in both the speed of knowledge acquisition, as well as their proof of the value of these techniques.
• Data mining will transform healthcare once we get the data into a semantically interoperable form.
• More and more applied in pharmaceuticals and medicine.
• Use of data mining methodologies and techniques in the fields of health-care and medicine.
• Discovering the molecular causes of disease.
• Use of DM in medicine, cost optimization
• Image analysis in medicine
• Detection of sources of diseases, cancer, etc.
• Human Genome Research
• Cancer diagnosis
• Data that can improve a person's health and wellbeing is important.
• The use of data mining in medical science.
• Medical research.