The application relates to a computing device for identifying pharmacological phenotypes. Pharmacological phenotypes are predicted for patients who exhibit or are likely to exhibit a
primary disease or complication. A
machine learning engine may generate statistical models based on training data from a trained patient to predict pharmacological phenotypes, including
drug responses and doses,
drug adverse events, risk of diseases and complications,
drug-
gene interactions, drug-drug interactions, and multi-
pharmacy interactions. The model can then predict the pharmacological
phenotype of a new patient and can make decisions in clinical and research contexts, including drug selection and dosage, changes in
drug treatment regimens, multi-
pharmacy optimization, monitoring, and the like, to benefit from additional predictive capabilities, as well as to improve the ability to predict the pharmacological
phenotype of the new patient. Therefore, adverse events and drug abuse are avoided, drug response is improved, patient
recovery results are improved, treatment cost is reduced,
public health benefits are improved, and
research efficiency in
pharmacology and other biomedical fields is improved.