Objective
Personalized medicine at scale is on the horizon, and AI is playing a key role in getting there. The data that exists, such as genetic information and electronic patient records of their medical history, have provided healthcare professionals a gold mine of data to gather greater insight into individual patients and their conditions. With this, they can use machine learning to identify trends, patterns and anomalies in the data that can help experts make better-informed decisions
Outcome
With AI and machine learning capabilities, pharma companies can collect, store and analyze large data sets at a far quicker rate than by manual processes. This enables them to carry out research faster and more efficiently, based on data about genetic variation from many patients, and develop targeted therapies effectively. In addition, it gives a clearer view on how specific groups of patients with certain shared characteristics react to treatments, helping to precisely map the right quantities and doses of treatments to prescribe,
Business Value
The solution, leverages machine learning to identify trends, patterns and anomalies in the data that can help experts make better-informed decisions.
H2O's AI and Data Approaches
This solution is powered by the H2O AI Cloud Driverless AI AutoML, H2O-3, and H2O.ai Wave. The data science approaches include genetic algorithm, advanced feature engineering, classification and regression algorithms, GLM, GBM, XGBoost, ensemble stacking, unsupervised clustering, and various machine learning interpretability algorithms.