For patients who exhibit or may exhibit primary or comorbid
disease, pharmacological phenotypes may be predicted through the collection of panomic, physiomic, environmental, sociomic, demographic, andoutcome
phenotype data over a period of time. A
machine learning engine may generate a
statistical model based on training data from training patients to predict pharmacological phenotypes, includingdrug response and dosing,
drug adverse events,
disease and comorbid
disease risk,
drug-
gene,
drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable
decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring,etc., to benefit from additional
predictive power, resulting in adverse event and
substance abuse avoidance, improved drug response, better patient outcomes, lower
treatment costs,
public health benefits, and increases in the effectiveness of research in
pharmacology and other biomedical fields.