Smart nutrition dosing
AssigneeOptimDosing, LLC
InventorsDavid Inwald, Kenneth I. Kohn
Life of this patent
Estimating the term…
Abstract
A method of dosing food and nutrients for an individual patient, by collecting data from the individual patient including food and nutrients to be taken by the patient, analyzing the data in view of dosing criteria established based on outside data, and determining a dose for each food and nutrient taken by the individual patient. A logic engine for dosing food and nutrients, including an algorithm stored on non-transitory computer readable media for collecting outside data to establish criteria for dosing food and nutrients to an individual patient and patient data and storing outside data and patient data in a database, analyzing the individual patient data in view of criteria established from the outside data, and determining a dose for each food and nutrient to be taken.
Claims14
A method of dosing food and nutrients for an individual patient, consisting essentially of the steps of:
- collecting data from the individual patient being dosed including electronic medical records, past and current nutritional intake habits, laboratory results, and food and nutrients to be taken by the patient to improve the patient's condition and inputting the data into a database with a central artificial intelligence (AI) stored on computer readable media, wherein the patient has cancer;
- the central AI analyzing the individual patient's collected data from the database in view of dosing criteria established based on outside clinical trial data,
- wherein the AI extracts from the outside clinical trial data all features relating variables that effect food and nutrient metabolism in a patient and creates a model relating dosing to patient condition and effect of food and nutrients on the condition that effect efficacy of all food and nutrients taken by the patient, wherein the variables include age of patient, weight of patient, disease state, effect of disease state on nutrition, drugs currently being taken along with known side effects of drugs alone and in combinations with other drugs, known toxicity range as related to ED 50, efficacy ranges, and chronic treatment effect versus acute treatment, the AI conducting natural language processing on unstructured data and extracting features from unstructured text of patient notes, notes from informal exams, and patient descriptions of current intake and habits, wherein the AI identifies nearest neighbors of patients from the clinical trial data having similar patient data and/or underwent a treatment plan with similar food and nutrient combinations to the individual patient and identifies related study and trial data with a K-Nearest Neighbor algorithm that calculates a distance between the patient and a neighbor patient using both continuous data and discrete data that is assigned a value and manually supplied to the database, wherein the AI compares the individual patient data to neighboring clinical trial patient data with weighting schemes;
- the central AI determining a dose for each food and nutrient taken by the individual patient and maximizing therapeutic effect of each food and nutrient for the individual patient while minimizing adverse effects for the combination of food and nutrients taken for the individual patient, wherein the nutrients include each of vitamin D, magnesium, calcium, vitamin A, vitamin K, vitamin B1, vitamin B2, vitamin B3, vitamin B5, vitamin B6, vitamin B12, omega-3, omega-6, vitamin E, vitamin C, sodium, chloride, zinc, potassium, and HPO42 Phosphate, and displaying the dose in a readable report for a practitioner;
- and
- the AI monitoring changes in the patient in real time due to changes in a disease state or drug induced changes, and the AI adjusting the dose for each food and nutrient in real time.
A logic engine for dosing food and nutrients, consisting essentially of an algorithm stored on non-transitory computer readable media for collecting outside clinical trial data to establish criteria for dosing food and nutrients to an individual patient having cancer and storing the outside clinical trial data and individual patient data in a database, analyzing the individual patient's collected data from the database in view of criteria established from the outside clinical trial data with artificial intelligence (AI), wherein the AI extracts from the outside clinical trial data all features relating variables that effect food and nutrient metabolism in a patient and creates a model relating dosing to patient condition and effect of food and nutrients on the condition that effect efficacy of all food and nutrients taken by the patient, wherein the variables include age of patient, weight of patient, disease state, effect of disease state on nutrition, drugs currently being taken along with known side effects of drugs alone and in combinations with other drugs, known toxicity range as related to ED 50, efficacy ranges, and chronic treatment effect versus acute treatment, wherein the AI conducts natural language processing on unstructured data and extracting features from unstructured text of patient notes, notes from informal exams, and patient descriptions of current intake and habits, wherein the AI identifies nearest neighbors of persons having similar patient data and/or underwent a treatment plan with similar food and nutrient combinations to the individual patient and identifies related study and trial data with a K-Nearest Neighbor algorithm that calculates a distance between the patient and a neighbor patient using both continuous data and discrete data that is assigned a value and manually supplied to the database, wherein the AI compares the individual patient data to neighboring clinical trial patient data with weighting schemes, and determining a dose for each food and nutrient to be taken by the individual patient, wherein the nutrients include each of vitamin D, magnesium, calcium, vitamin A, vitamin K, vitamin B1, vitamin B2, vitamin B3, vitamin B5, vitamin B6, vitamin B12, omega-3, omega-6, vitamin E, vitamin C, sodium, chloride, zinc, potassium, and HPO42 Phosphate, wherein the algorithm provides an output in the form of a practitioner readable report, and wherein the AI monitors changes in the patient in real time due to changes in a disease state or drug induced changes, and the AI adjusts the dose for each food and nutrient in real time.
Dependencies are inferred from the claim text.