Detecting consumer decisions within messy data

   
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MIT spinout dMetrics believes this online chatter is an information treasure-trove for the health care industry. “In health care, there’s this gigantic world of unstructured data that needs to be translated into useable information,” says Paul Nemirovsky PhD ’06, who co-founded dMetrics with Ariadna Quattoni PhD ’09.

MIT spinout dMetrics believes this online chatter is an information treasure-trove for the health care industry. “In health care, there’s this gigantic world of unstructured data that needs to be translated into useable information,” says Paul Nemirovsky PhD ’06, who co-founded dMetrics with Ariadna Quattoni PhD ’09.

The startup has developed a platform called DecisionEngine that uses machine learning and natural language processing — which helps computers better understand human speech — to mine billions of conversations about drugs, medical devices, and other health care products. These discussions are happening on blogs, Facebook, Twitter, forums, and even in comments accompanying news articles and videos.

From those vast stores of messy data, the software reveals insights into consumer decisions, Nemirovsky says: “What people do, don’t do, consider doing, may do, did in the past, as well as what needs, fears, and hopes they have.”

Today, Nemirovsky explains, dMetrics has a database that includes every public comment about patient-reported illnesses, solutions, and outcomes, pulled from more than 1 million online sources. This includes information on more than 14,000 health care products.

Clients, including Fortune 500 companies and nonprofit organizations, can use dMetrics software to answer specific questions, such as how many patients used a specific medication for a particular reason in certain time frame, or which customers are considering switching from their drug to a competitor’s drug.

Although focusing on the health care industry, dMetrics, headquartered in Brooklyn, New York, is also trialing its platform with consumer finance and political organizations. Credit card companies, for instance, can analyze why consumers favor specific credit cards over others. Political scientists could use the software to determine which issues people care about and how strongly they stand behind their opinions.

“For all these types of questions, you have to understand not only the words people use but the concepts behind the words,” Nemirovsky says.

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