Why predictive medicine

How predictive medicine is improving healthcare for patients

Studies have shown that 40% of all deaths in the United States are preventable. The main reason they weren't prevented was because predicting the risk of most diseases is quite difficult. The need for a system that can make accurate predictions based on large amounts of data is more pressing today than ever before.


Today predictive medicine is finally allowing doctors to easily determine a patient's susceptibility to certain diseases. With extremely powerful algorithms and huge data-sets, we can significantly enhance healthcare for everyone.



The 3 Technologies

How technology enhances predictive medicine

Predictive medicine has become significantly more efficient with the advent of machine learning. Machine learning algorithms can look at multiple data sets and search for relationships between multiple variables. When applied to medicine, ML algorithms can quickly analyze massive patient health records and clearly identify trends.

The increased availability of big data has made predictive medicine more accurate than ever before. For example, with the completion of the HapMap project, we now have a complete haplotype map of the human genome. The HapMap has greatly sped up the process of Genome Wide Association Studies. The more medical data we collect, the more reliable predictive medicine will become.
The rise of the Internet of Things has allowed predictive medicine to become even more accurate. More and more people are using internet-connected health-related devices that provide medical data in real time. Machine learning algorithms can then quickly analyze this data and provide helpful information.

Machine learning, big data, and the Internet of Things all work together to make reliable predictions for clinicians. Find out how




How it works

The 3 procedures of predictive medicine

There are three methods of predictive medicine : Analysis of Association uses health records to determine the association between a gene and a medical disease(MD). Analysis of Linkage looks at a patient's family members to try to link genetic differences to a MD. GWAS(Genome Wide Associations Studies), the most recent procedure, uses haplotypic maps we already have to find associations between SNPs and MDs.
Read more


Research

A few of the companies developing predictive analytics for medicine

Pathway Genomics is developing a blood test which could detect and possibly predict certain cancers. Its blood-based non-invasive test called Cancer Intercept can detect circulating tumor DNA. The company has also developed a genetic test that analyzes over 70 genetic variants associated with 7 categories of skin health. Both of these tests rely on machine learning algorithms that have been formed from large amounts of data sets.
A company called Existence Genetics has developed a patent-pending preconception testing technology called Pythia Approach. Pythia Approach combines the genetic makeup of 2 parents to predict the risk of a large number of diseases if they were to have a child.


Another company, Lumiata, is working on an AI-power predictive tool built from over 175 million patient records. Their algorithms can create a "Risk Matrix" which can determine the future health of patients.