Health Care, Analytics and Big Data

The evolution of electronic health records and health information exchanges is producing large amounts of data on disease, treatments, demographics and treatment outcomes. The data is beginning to expose unknown relationships between different diseases in the same patients.

An unlikely investigator is a  physicist from Vienna Austria.   Stefan Thurner is a physicist, not a biologist. But not long ago, the Austrian national health insurance clearinghouse asked Thurner and his colleagues at the Medical University of Vienna to examine some data for them. The data, it turned out, were the anonymized medical claims records—every diagnosis made, every treatment given—of most of the nation, which numbers some 8 million people.

Thurber reports in an article he authored in Quantia.

Network maps reveal hidden molecular connections between disparate diseases.In a recent paper in the New Journal of Physics, Thurner and his colleagues Peter Klimek and Anna Chmiel started by looking at the prevalence of 1,055 diseases in the overall population. They ran statistical analyses to uncover the risk of having two diseases together, identifying pairs of diseases for which the percentage of people who had both was higher than would be expected if the diseases were uncorrelated — in other words, a patient who had one disease was more likely than the average person to have the other. They applied statistical corrections to reduce the risk of drawing false connections between very rare and very common diseases, as any errors in diagnosis will get magnified in such an analysis. Finally, the team displayed their results as a network in which the diseases are nodes that connect to one another when they tend to occur together.

A human disease network maps out connections between diseases — if patients who have one disease tend to also have another, the two disease nodes are connected.

One disease module they’ve studied is for pulmonary hypertension (elevated BP in the pulmonary artery. The researchers published their findings in the journal Pulmonary Circulation.

Another module looks at Type 2 diabetes.   Researchers have linked diabetes to about 200 spots on the genome through genome-wide association studies.  We know genes have multi-factorial effects, but have less evidence for association in different diseases. Empirical clinical cases reveal a co-incidence of Type II  diabetes, and hypertension.  Mapping disease networks may explain more objectively the association between these two diseases.

The real promise is to reveal previously unknown associations of diseases.

This article was reprinted on ScientificAmerican.com.