Drug administration timing
OwnerRitankar Das, Samson Mataraso +1inventors
InventorsRitankar Das, Samson Mataraso, Jacob Calvert
Life of this patent
Estimating the term…
Abstract
Methods for determining the time at which a drug should be administered, based on patient electronic health record data. Machine learning techniques are used to correlate trends in health record data with successful drug treatment, ultimately anticipating the optimal time of drug administration. Multiple types of data, including demographic, physiological, treatment, and clinical notes data, can be used to train the classification component. Multiple patient populations can be used as sources of patient data for training classification component. Data input requirements, dimensionality, and performance metrics may be optimized.
Claims22
A method of administering a drug treatment to a candidate patient, comprising:
- acquiring patient data from a plurality of patients;
- comparing the acquired patient data to classified anonymized patient health record data;
- and
- administering the drug treatment to the candidate patient based on whether or not the timing of the drug treatment is efficacious in treating a specific manifestation of the candidate patient's condition as evidenced by the anonymized patient health record data.
A method of using a machine learning algorithm for administering a drug treatment to a candidate patient, comprising:
- acquiring anonymized patient health record data from a plurality of patients;
- comparing acquired patient data to the acquired anonymized patient health record data;
- and
- administering the drug treatment to the candidate patient based on whether or not the timing of the drug treatment is efficacious in treating a specific manifestation of the candidate patient's condition as evidenced by the anonymized patient health record data.
Dependencies are inferred from the claim text.