IBM Wants to Predict Heart Disease Through Big Data Analytics

Heart failure will remain among most deadly and costly diseases for human beings unless we discover new methods to detect the illness much earlier. Every individual is impacted by their health, social and environmental needs. To truly treat each patient as an individual, and provides professional the most comprehensive care, big data analytics technology today is being used in new ways to give us bold new insight.

IBM wants to be the front runner in health care with the help of technology available today. The Blue Chip Company joined by Sutter Health and Geisinger Health Systems, has received a $2 million grant to use big data analytics to detect the signs of heart disease years earlier. The project creates a needed overview of the continuity of patient care. With big data analytics capabilities, IBM strategy with the program is to improvement communications and integrated care among health, social services and other providers where the focus is on the best patient care possible. Market researchers at MarketsandMarkets predicts that the health care industry will invest $5.4 Billion in cloud computing by 2017.

Sophisticated analysis of EHR data could reveal the unique presentation of these symptoms at earlier stages and allow doctors and patients to work together sooner to do something about it. IBM is applying advanced tools for analyzing medical data, including text, and reviewing a patient’s health records for new insight. IBM utilizes Unstructured Information Management Architecture (UIMA) to extract the known signs and symptoms to heart failure from available text.

IBM said the challenge for differentiating heart failure patients from the controls, prior to diagnosis, is that there is no single strong indicator. But there are many weak indicators called co-morbidities, such as hypertension and diabetes, associated medications and Framingham heart failure symptoms that can be extracted from text.

IBM uses a Hadoop cluster to manage and schedule tens of thousands of models in parallel to facilitate and speed up the model development. Besides unstructured text and other structured medical information, IBM will also look into other data source such as Electrocardiography (ECG) and genomic data.

By pairing IBM’s expertise in Big Data analytics with the domain knowledge and data, this project will result in the development of new analytic algorithms for more accurate detection of the early onset of heart failure. Ultimately, health care industries hope to advance a smarter approach to care for patients with heart failure.

The research team from Geisinger said that earlier research showed that signs and symptoms of heart failure in patients are often documented years before a diagnosis and that the pattern of documentation can offer clinically useful signals for early detection of this deadly disease. Now they have the technology to enable earlier diagnosis and intervention of serious conditions like heart failure, leading to better outcomes for patients.

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