Abstract: Neural networks are constructed (programmed), and trained on historical data relating the (i) alleles, to the (ii) clinical responses, of a large number of patients. The trained neural networks show which alleles are, in combination, of practical pertinence to a wide range of biological, social and clinical variables. The trained neural networks may be exercised to predict (i) the responses of populations to different therapies, and (ii) the occurrences of adverse reactions. The trained neural networks are exercised in consideration of the genomic data of an individual patient to predict the response(s) of the individual patient to, most particularly usefully, any of (1) optimal drug dosage, (2) drug dosage sensitivity, (3) expected therapeutic outcome(s), and/or (4) adverse side effects may can be predicted in consideration of the alleles of the patient.

Abstract: Neural networks are constructed (programmed), trained on historical data, and used to predict any of (1) optimal patient dosage of a single drug, (2) optimal patient dosage of one drug in respect of the patient's concurrent usage of another drug, (3a) optimal patient drug dosage in respect of diverse patient characteristics, (3b) sensitivity of recommended patient drug dosage to the patient characteristics, (4a) expected outcome versus patient drug dosage, (4b) sensitivity of the expected outcome to variant drug dosage(s), (5) expected outcome(s) from drug dosage(s) other than the projected optimal dosage. Both human and economic costs of both optimal and sub-optimal drug therapies may be extrapolated from the exercise of various optimized and trained neural networks. Heretofore little recognized sensitivities—such as, for example, patient race in the administration of psychotropic drugs—are made manifest.

Abstract: Neural networks are constructed (programmed), and trained on historical data relating the (i) alleles, to the (ii) clinical responses, of a large number of patients. The trained neural networks show which alleles are, in combination, of practical pertinence to a wide range of biological, social and clinical variables. The trained neural networks may be exercised to predict (i) the responses of populations to different therapies, and (ii) the occurrences of adverse reactions. The trained neural networks are exercised in consideration of the genomic data of an individual patient to predict the response(s) of the individual patient to, most particularly usefully, any of (1) optimal drug dosage, (2) drug dosage sensitivity, (3) expected therapeutic outcome(s), and/or (4) adverse side effects may can be predicted in consideration of the alleles of the patient.

Abstract: Neural networks are constructed (programmed), and trained on historical data relating the (i) alleles, to the (ii) clinical responses, of a large number of patients. The trained neural networks show which alleles are, in combination, of practical pertinence to a wide range of biological, social and clinical variables. The trained neural networks may be exercised to predict (i) the responses of populations to different therapies, and (ii) the occurrences of adverse reactions. The trained neural networks are exercised in consideration of the genomic data of an individual patient to predict the response(s) of the individual patient to, most particularly usefully, any of (1) optimal drug dosage, (2) drug dosage sensitivity, (3) expected therapeutic outcome(s), and/or (4) adverse side effects may can be predicted in consideration of the alleles of the patient.

Abstract: A capsule, or a matrix, of a substance, most typically a polymer, that is degraded by a photo-acid or, less preferably, by a photo-base, physically contains or incorporates (i) a photo-acid, or a photo-base, or precursors to same, and (ii) one or more molecular agents, normally drugs. Placed in vivo, the photo-acid or photo-base or its precursors is (are) changed into an acid or base, as the case may be, by impinging radiation, most preferably by one or more light beams of green or longer wavelengths to which tissues are transparent, or else x-rays. The preferred light beams are two in number, spatially and temporally intersecting to produce the acid or base in vivo at precise regions and times by process of two-photon absorption. The photogenerated acid or base ruptures or dissolves the containment capsules or matrix, loosing the contained molecular agents (i) at precise subcutaneous tissue locations (ii) at precise rates (iii) over precise time intervals.

Abstract: Neural networks are constructed (programmed), and trained on historical data relating the (i) alleles, to the (ii) clinical responses, of a large number of patients. The trained neural networks show which alleles are, in combination, of practical pertinence to a wide range of biological, social and clinical variables. The trained neural networks may be exercised to predict (i) the responses of populations to different therapies, and (ii) the occurrences of adverse reactions. The trained neural networks are exercised in consideration of the genomic data of an individual patient to predict the response(s) of the individual patient to, most particularly usefully, any of (1) optimal drug dosage, (2) drug dosage sensitivity, (3) expected therapeutic outcome(s), and/or (4) adverse side effects may can be predicted in consideration of the alleles of the patient.