Patient filtering based on likelihood of side effects
OwnerRitankar Das, Samson Mataraso +1inventors
InventorsRitankar Das, Samson Mataraso, Jacob Calvert
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Abstract
Methods for identifying to which patients a drug should be administered, based on underlying drug mechanism of action, are provided. Multiple types of data, including demographic, physiological, treatment, and clinical notes data, can be used to train a 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.
Claims20
A method of filtering out a patient likely to suffer from a side effect from drug treatment, comprising:
- acquiring patient data from a plurality of patients;
- comparing the acquired patient data to classified anonymized patient health record data;
- and
- filtering out the patient from the plurality of patients, who is likely to suffer from a side effect from drug treatment based on whether or not the drug's mechanism of action is likely to cause a side effect in treating a specific manifestation of the patient's condition as evidenced by the classified anonymized patient health record data.
A method of using a machine learning algorithm for filtering out a patient likely to suffer from a side effect from drug treatment, 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
- filtering out the patient from the plurality of patients, who is likely to suffer from a side effect from drug treatment based on whether or not the drug's mechanism of action is likely to cause a side effect in treating a specific manifestation of the patient's condition as evidenced by the classified anonymized patient health record data.
The method of claim 14, wherein the anonymized patient health record data is sourced from a database of the plurality of patients, a database of one or more care centers and patient populations, or from a database of multiple care centers and patient populations.
Dependencies are inferred from the claim text.