There’s no doubt about it, the machines are taking over. However, the reality is more Bicentennial Man than Terminator. Data science and machine learning are revolutionizing the way we live, and one industry that is embracing this wholeheartedly is health care.
Enthusiasm for superior cognitive computing in health care is on the up, as providers recognize the need to remain on the cutting edge. Through the use of machine learning (ML) algorithms and big data, health care professionals can provide next-level patient care, and researchers can speed up their work considerably.
So what’s the big deal with big data?
Research And Drug Creation
The research and creation of pharmaceuticals is a long process, but data science and machine learning algorithms can streamline it — from the initial research and screening of known compounds to success rate prediction. Advanced modeling and simulations allow researchers to quickly experiment with different biological variables, giving them the flexibility they need.
Additionally, this data could potentially be scaled to fit with new pharmacy technology, aiding pharmacists with dispensing medication. This could allow pharmacists to check whether prescribed medication is compatible with medications that patients are already taking. Current technology notifies pharmacists when potential interactions exist between medications, but this only includes medications the patients have filled at the local pharmacy or chain. Pharmacists are unaware of health conditions, genetic conditions or other medications the patient may be taking unless it is disclosed.
Advanced Treatment Personalization
Data science techniques allow integration of different kinds of data — everything from medical histories to DNA profiles — which provides a deeper understanding of the patient’s health. Utilizing pharmacogenetic testing, providers and patients are able to get DNA samples that can then be sent to labs for testing. These tests are available to consumers directly or from providers in office.
With the increase in utilization of health-centered technology for consumer use, there is more data than ever that is captured and available for analysis by providers. In the future, data from wearable technology, such as fitness trackers, could very well be used to provide live, up-to-date data with which these predictive diagnoses can be made.
Predictive analysis can also be used to learn from historical data and make accurate predictions about future outcomes. The impacts of biological and clinical variables are considered in order to predict the evolution of certain diseases or health complications.
Medical Image Analysis
Medical imaging technologies — MRI, X-ray, etc. — can often be plagued with quality issues which affect the resolution, clarity and dimensions of the resulting image. Obviously, this is an issue when it comes to evaluating and diagnosing diseases or traumas, and so techniques are being developed to combat these problems.
Data science and machine learning algorithms can be used to improve the image quality, extract data efficiently and even provide accurate interpretations. With deep-learning, ML algorithms can improve their diagnostic accuracy by learning from examples, and theoretically, the more it’s shown, the more accurate it becomes.
The Future
These are just three examples of what data science and machine learning can do for health care, and there are many more potential applications. We may not be welcoming in independent robot surgeons any time soon, but health care and pharmacy technology are changing. Data science and machine learning are allowing us to build healthier tomorrows, today.
