Neural network drug dosage estimation

A computerized method of predicting an optimal dosage of a particular drug for a Particular Patient in consideration of previously determined optimal dosages of the drug for members of a patient population, the computerized optimal drug dosage prediction method comprising:

• programming a plurality of neural networks each having an architecture of one or more slabs collectively relating input data to output data,
• wherein said input data includes at least a selected three (3) of a person's traits drawn from at least two (2) of the following three (3) groups consisting of
• Group 1 overt indications of
• (1a) age,
• (1b) gender,
• (1c) race,
• (1d) ethnicity,
• (1e) diet type,
• (1f) height,
• (1g) weight, and
• (1h) body surface area,
• Group 2 medical diagnostic indications of
• (2a) blood pressure,
• (2b) use of a drug other than the particular drug at the same time as use of the particular drug,
• (2c) fitness,
• (2d) peptide levels, and
• (2e) genetic predisposition to a particular disease, and
• Group 3 pharmacological indications of
• (3a) pharmacokinetic parameters,
• (3b) pharmacodynamic parameters, and
• wherein said output data includes clinically-determined optimal drug dosage for the same person;
• training each of the plurality of programmed neural networks with a training data set drawn from a multiplicity of historical medical records of a multiplicity of persons historically administered the particular drug, the records relating the selected input data to the output data;
• selecting one of plurality of neural networks that performs best on the training data set to be a selected trained neural network;
• using the selected trained neural network to predict an optimal dosage of the particular drug for the Particular Patient, the using transpiring by inputting the selected input data, which input data is at least a selected three (3) of The Particular Patient's traits drawn from at least two (2) of the three (3) groups consisting of Group 1 overt indications and Group 2 medical diagnostic indications and Group 3 pharmacological indications, to ascertain as (2) output of the trained neural network the output data, which output data is the predicted optimal dosage for the Particular Patient.

A method of creating a neural network both (i) optimized and (ii) trained to predict the optimal dosage of a particular drug for a particular patient, the optimal trained neural network creation method comprising:

• acquiring neural network training data as
• raw data from patient medical records, the raw data being in the form of recorded patient responses to recorded patient drug dosages in respect of patient characteristics,
• the patient medical records being categorized for relevance, so as to produce
• relevant categorized patient drug response data in respect of patient characteristics;
• designing and programming a plurality of neural networks each of which may suitably operate upon the neural network training data by
• programming a framework for each of the plurality of neural networks;
• programming a genetic algorithm for framework for each of the plurality of neural networks, and
• interfacing the programmed framework with the programmed genetic algorithm;
• then
• training each of the plurality of neural networks with the neural network training data until, by operation of the genetic algorithm, a single optimal, trained neural network is selected;
• wherein the selected single optimal, trained neural network is then both (i) optimized, meaning selected as the best of many, and (ii) trained, meaning proven on data of relevance, to predict the optimal dosage of a particular drug for a particular patient.

A computerized method of predicting an optimal dosage of a particular drug for a Particular Patient, the method comprising:

• performing in a recurring, iterative, looped process each of the steps of
• (1) programming a neural network that relates drug dosage to drug efficacy and to drug side effects, and
• (2) training the neural network in consideration of a historical data set of both efficacy and side effect measures from usages of the drug at determined dosages on members of a population,
• wherein the (1) programming transpires in consideration of the results of the (ii) training,
• so long as, and until, an optimally programmed and trained neural network adequately accurately predicts both drug efficacy and drug side effects relative to drug dosage;
• then
• exercising the optimally programmed and trained neural network on a range of drug dosages in order to examine the predicted sensitivity of both drug efficacy and drug side effects relative to the range of drug dosages;
• wherein a physician prescribing the drug can, by observation of the results of the exercising, make a better informed decision as to what patient dosages of the drug are likely to deliver both tolerable efficacy and tolerable side effects.