Data Science and Healthcare sector

Technology has helped medical and healthcare sectors to evolve and revolutionary changes have been possible due to these changes. Computerized medical records, lead generation for new drug molecules, exploration of genetic data to diagnose deadly diseases all have been possible due to technological advancements and data science is one such field that has made the healthcare sector to move to a whole new level.

Data science has helped to deal with piles of structured and unstructured data to fetch out meaningful data for healthcare experts and it is therefore very important for the two sectors to advance together. This, in turn, would not just help the medical sector to provide better service and find treatment for deadly diseases, but also help society by bringing the cost of medicine and treatments down.

Data Science Application in healthcare and medicine sectors:

Medical Imaging: A lot of research has been done in this area. Imaging techniques like MRI, CT scan, X-ray imaging and mammography etc. are using deep-learning to tackle with differences in various resolutions, modalities, and dimensions of images. Data science can be used to increase the accuracy by comparing the trends from previous studies and help in developing better treatment options. Hadoop analytical tool is used to discover alternatives for tasks such as lung texture categorization.

Genetics and genomics: Research work in genetics and genomics has helped design advanced and more personalized treatment. Data science helps in handling the huge data stored in one’s DNA and its impact on health, find connection between diseases, genetic makeup and drug response. This data is integrated to have a deeper understanding of various genetic responses towards drugs. Data science has made it possible to get results faster and accurate.

Drug Discovery: It is a very complicated and time taking process involving huge time (as long as ten to twelve years) and financial investment. Data science has significantly shortened this by forecasting the success rate of a drug molecule based on biological factors, pharmacokinetic and pharmacodynamic response using mathematical and simulation models in place of laboratory testing. This saves both time and money.

Accurate Prognosis and Diagnosis:  The failure to diagnose a disease results in as high as 5% to 10% deaths in U.S alone. The enormous amount of health data has to be dealt with making it difficult for humans to give accurate results every time. This problem inspired a deep-learning start up called Enlitic to design a program to process such large data and compare it with an extensive database of pathological reports, studies and patient history, giving accurate diagnostic results quicker.

Customer data Management:  Gone are the days when you used to encounter big registers in hospitals keeping patient records. Machine learning tools such as optical character recognition and support vector machine has allowed to create comprehensive record inventories of medical data which is easy to access and more promising and organized form to refer to.

Better understanding of the industry:  Data science is helping to impart better management of knowledge and bring fruitful results for the betterment of society. It has made it possible to effectively gather, store, integrate and distribute this knowledge in order to help health care organization achieve their targets.

The possibilities for better utilization of Data Science are always open as the data is expanding day by day and technology is advancing every second. The future in Data science is quite promising and with enormous opportunities for the healthcare sector and technology enthusiasts.

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