1. © Copyright 2014 Gray Matter Analytics. All rights reserved.
Clustering Medical Data to Predict the Likelihood of Diseases
Objective: Build a statistical strategy from electronic medical data that can identify the population of a
homogenous set of complex patients who may benefit from targeted care management strategies.
Methodology: We identified 934 patients, some who had a medical history of various conditions and some
who are currently undergoing medical treatment for a specific condition. We used an agglomerative
hierarchical clustering method to identify clinically relevant subgroups with similar conditions. Clustering
compared each member based on data collected for variables such as - (i) Most recent drug fill, (ii) disease
conditions over the past 2 years, (iii) Diagnosis group code, (iv) Emergency room admission count, and (v)
Immunizations over the past 4 years. Patients were then added to a different cluster based on a
comparison of their medical data similarities. The results enabled us to show a clustering model of patients
who were at high risk of acquiring a specific chronic disease and expected to undergo the same kind of
treatment similar to other patients in the same cluster with the same chronic disease. With this method,
medical treatment administered prior to a diagnosis could possibly avoid the risk of acquiring those chronic
diseases.
For example: Let’s say patient “A” was diagnosed with diabetes. Prior to the diagnosis, they were treated
for ailments such as fatigue and blurred vision. Let’s say Patient “B” has been treated for fatigue and is
currently being treated for blurred vision. Because of the similarities in symptoms between the two
patients, there is a strong possibility Patient “B” may be diagnosed with diabetes as well. In this case,
clustering compares each patient’s clinical activities, and, based on similarity, puts patients into a different
cluster.
Benefit: Enables prediction of a diseased condition for a specific patient based on comparison between
other patients who went through a similar pattern of symptoms or treatment before they were diagnosed
with the same type of disease.
Cluster analysis can be used to address a wide variety of important issues for individual and the population
level of healthcare such as - (i) For any given cluster, one might track the outcome of patients going through
different clinical treatments and be able to identify which treatment is most effective or has the least side
effects. (ii) Clustering patients allows adoption of a potentially changing medical landscape.
For this example, if a new disease appears with a particular symptom, the model will identify the disease by
the symptom and create a new cluster of patients who have that disease.
Working with Gray Matter Analytics
Gray Matter Analytics provides professional services that empower our clients in healthcare and financial
services for success, enabling you to use data analytics and predictive modeling to gain a distinct edge over
the competition. We help you gain insight into data you need in order to make informed decisions and
ensure you have the technology platform to enable a data-driven environment.
visit www.graymatteranalytics.com.