Top 6 Machine Learning Applications in Healthcare

The healthcare sector has been an early adopter of and also benefited from technological advances for a long time. Nowadays, Machine Learning (a part of Artificial Intelligence) plays an essential role in many medical concerns, including the development of the medical treatments, the handling of patient data, their records, and the treatment of various chronic diseases.

As per great scientists and researchers, ‘’Artificial Intelligence (AI) is the future of healthcare”. Machine Learning (a subset of Artificial Intelligence) is greatly affecting the physicians and hospitals, as it plays a key role in clinical decision support, making earlier identification of disease, and tailored treatment plans to make sure the best outcomes. It can even be used to demonstrate & educate patients on potential disease pathways & outcomes given different treatment options. It can impact hospitals & health systems in improving efficiency, while also reducing the cost of care.

In this article, we bring 6 major areas in Healthcare where Machine Learning is being implemented:

Drug Discovery & Manufacturing: One of the primary clinical applications of machine learning lies in the early-stage drug discovery process. This also adds R&D technologies like next-generation sequencing & precision medicine which can help in finding alternative paths for therapy of various diseases. Presently, the machine learning methods involve unsupervised learning which can identify multiple patterns in data without providing any sort of predictions. Project Hanover that was created by Microsoft is using ML-based technologies methods for various initiatives by including creating AI-based technology for personalizing drug combinations & cancer treatment for AML.

Health Forecasting with Machine Learning Applications: Health forecasting is a noble area of forecasting, and also a valuable tool that helps in predicting future health events/ situations like demands for health services & healthcare needs. It even facilitates preventive medicine & health care intervention strategies, by priorly informing the health service providers to take necessary actions in order to minimize risks & manage demand. Health forecasting requires information, reliable data, and necessary analytical tools for the prediction of particular health situations or conditions. There is no one approach to health forecasting, and hence, various methods have generally been adopted to forecast aggregate or specific health conditions.

Applications like the Sales Forecast App, help in the future prediction of healthcare medicines as per the needs and demands. Forecast app helps to predict future events based on the analysis of past and present data. Forecasting these trends not only helps the doctors with demand estimation for particular medicine but also helps in managing the medicines stocks based on demands.

Medical Imaging Diagnosis: Both Machine learning and Deep Learning are highly responsible for the breakthrough technology called Computer Vision. This has found acceptance in the InnerEye take up that is developed by Microsoft that works on image diagnostic tools for image analysis. As Machine Learning becomes accessible and as they grow in the explanatory capacity, expect to see more and more data sources from the varied medical imagery become a part of the AI-driven diagnostic process.

Personalized Medicine: Personalized treatments can not only be more effective by pairing individual health with predictive analytics but are also ripe for further research and better disease assessment. Presently, physicians are limited to choosing from a specific set of diagnoses or estimate the risk to the patient based on his symptomatic history and available genetic information. However, Machine Learning in medicine is making great advancements, & IBM Watson Oncology is at the forefront of this movement by allowing patient medical history to help generate multiple treatment options. In the next few years, we will see more devices and biosensors with more sophisticated health measurement abilities hitting the market, hence allowing more data to become readily available for the cutting-edge ML-based healthcare technologies.

Smart Health Records: Maintaining updated health records is a tedious process, and while technology has played its share in easing out the data entry procedure, the truth is that even now, a majority of the procedures take a lot of time to complete. The major role of machine learning in healthcare is to just ease processes to save effort, time, and also the money. Document classification processes using the vector machines and the ML-based OCR recognition techniques are also gathering steam, such as MATLAB’s machine learning-based recognition technology and Google’s Cloud Vision API.

Clinical Trial & Research: Machine learning has many potential applications in the field of research and clinical trials. As anyone in the pharma industry will tell you, clinical trials cost big money and can even take years to complete in many clinical cases. Applying some ML-based predictive analytics in order to identify the various clinical trial candidates can help the researchers draw a pool from a wide variety of data points, such as social media, previous doctor visits, etc. Machine learning has also found usage in making sure the real-time monitoring & data access of the various trial participants, hence, finding the best sample size to be tested & leveraging the power of electronic records to decrease the number of data-based errors.

Conclusion:

Individuals need to stop considering Machine Learning as a concept from the future and must embrace the tools & opportunities that it is making available for us. The applications of Machine Learning are highly advancing the field of healthcare into a new domain that makes it exciting to think about where it can go in the near future.

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