Machine learning and AI technologies can identify complex relations and patterns in data, revealing insights that would otherwise remain hidden. Our future is moving towards cognitive AI models that are inspired by deep learning methodologies. We are integrating a multitude of medical challenges and studying the interactions between data to reach powerful and complex algorithmic models.
Big data tools were first used in the finance and business disciples, helping experts to uncover hidden trends and patterns, such as categorizing the email inbox, recommending a Netflix movie, detecting credit card fraud, and all the way to present day, where these technologies make up the brains of autonomous vehicles. Now, as we apply the same models to create a new healthcare paradigm, we have an opportunity to evaluate their potential in diagnosis, treatment and prevention of different illnesses, diseases, and health conditions.
Traditional methods of healthcare decision-support systems required experts to provide the system with rules and guidelines in order to draw conclusions and insights. With machine learning, we can train the system to deliver cognitive health insights by supplying the data and outcomes. In this way, the system builds a vector across thousands of parameters, analyzes the position of each patient’s EHR data points within the range and implements sophisticated, predictive AI-based modelling that links data to results.
This powerful, AI-based machine-learning engine leverages thousands of cases recorded in real medical data to identify complex trends, patterns and interrelationships over time, taking various health conditions and lifestyle data into consideration. The system empowers healthcare practitioners with a toolbox to create accurate predictions and classifications through a variety of algorithms, such as the likelihood of an individual to harbor cancer, or the estimated effectiveness of a particular treatment.
Download ● McKinsey Executive Guide to Machine Learning