The Positive Impact of Data Mining:  
Health / Medical Progress
The Positive Impact of Data Mining:  
Health / Medical Progress

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
    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

  • 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

  • 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

  • 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.
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