How data science is transforming health care industry using machine learning technology?

How data science is transforming health care industry using machine learning technology?

A huge science behind data

Data science can be referred as the heartthrob of industries today. With data being one of the most important assets of business, with huge volume of data, companies can have an edge over their competitors. Data science is a combination of variegated fields such as information science, mathematics, data mining, database system and much more. Having known what data science is, now you might have a confusion between big data and data science. Big data is a separate field all by itself where data are arranged and pre-processed. Whereas data science analyses the data completely.

With the collected, huge volume of raw data, analysis is conducted to get an insight on the data, its pattern in order to make use of the data for the business. By facilitating the communication of a brand’s story with the target audience, the brand achieves core connectivity with its target audience. Data science and its algorithms are generic in nature and can be utilized for any type of business. They help in understanding the causes based on the analysis of the available data.

Machine learning and artificial intelligence

We often come across the terms machine learning and artificial intelligence in the field of data science. Most of us use them interchangeably; but they are different. Machine learning is an application of artificial intelligence where machines consume data when user provides an access to it, get trained by the algorithms and produces results to the user. Artificial intelligence on the other hand is a broader aspect encompassing machine learning whose outcome will be a smarter device.

Medicine is one of the fields which sees constant evolution. At the current scenario, need for new drugs and patient care is given utmost importance.  Here comes in the role of data science. It helps in making informed decisions pertaining to patient care and their satisfaction. Healthcare data are sensitive and needs secured analysis masking the identity of the patients when the data set is utilized in a project. Medical records have plenty of data including patient’s demographics, their diagnostic reports, laboratory test reports, medical prescriptions and much more. The volume and frequency of receiving such data increases if the patients are having chronic diseases. These patients will have long history of health related data and these can be used to build machine learning models. The outcome of the model will be the expected result.

Analytics on disease management- a brief insight into VdMA

DMA, a disease management analytics project intends in providing prediction, prevention and management of chronic and other diseases in patients which is done through the Vinnovate Disease Management Analytics platform. Risk assortment, identifying the critical medical illness and predicting future events are encrusted within VdMA platform. VdMA utilizes the patient data, supplies it to the suitable machine learning algorithms that are built using artificial intelligence and predicts the current medical conditions and the possible risks that could occur in the future.

Embracing VdMA platform, we have developed models for prediction of diabetes, CAD risks, CVD risks, 30 day readmission, diabetic retinopathy, hypoglycaemia using machine learning algorithms. These predictions prevent further worsening in the patient’s health conditions. Diabetic retinopathy predictions are made by analysing the fundus images of patients and segregating them into healthy and presence of retinopathy conditions using machine learning algorithms. In other predictions, data from different sources were analysed thoroughly, different models were designed among which the optimal model has been selected and best possible results were obtained. Data science is tied up with constant research and development process and more predictive models are to be developed in the near future.

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