Algorithms to predict clinical response, adherence, and shunting with thiopurines
First Claim
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1. A method of determining the effectiveness of thiopurine treatment in a patient receiving the thiopurine treatment for inflammatory bowel disease (IBD) comprising:
- receiving on a computer device a set of data inputs related to characteristics of a patient receiving a thiopurine treatment for inflammatory bowel disease; and
calculating on the computer device a clinical response probability based on the set of data inputs using an algorithm that applies a weight to each data input of the set of data inputs, wherein each weight relates to a quantified importance of a particular data input associated with a fixed set of patient data from patients receiving a thiopurine treatment for inflammatory bowel disease in which the clinical response of the patients is known,wherein the set of data comprises at least three data inputs selected from the group consisting of hemoglobin (HGB), hematocrit (HCT), platelet count (PLT), red blood cell count (RBC), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), mean platelet volume (MPV), sodium level (SOD), potassium level (POT), chloride (CHLOR), carbon dioxide (CO2), urea nitrogen (UN), creatinine level (CREAT), glucose level (GLUC), calcium level (CAL), protein level (PROT), albumin level (ALB), aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALK), bilirubin total (TBIL), neutrophil count (NEUT), monocyte count (MONO), basophil count (BASO), and age of patient (Age).
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Abstract
A method of using a variable set from complete blood counts and blood chemistry panels to generate a machine learned algorithm for determining the effectiveness of thiopurine treatment on inflammatory bowel disease (IBD) patients using CART, boosted trees, random forest classification, RuleFit and/or logistic regression analysis.
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9 Claims
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1. A method of determining the effectiveness of thiopurine treatment in a patient receiving the thiopurine treatment for inflammatory bowel disease (IBD) comprising:
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receiving on a computer device a set of data inputs related to characteristics of a patient receiving a thiopurine treatment for inflammatory bowel disease; and calculating on the computer device a clinical response probability based on the set of data inputs using an algorithm that applies a weight to each data input of the set of data inputs, wherein each weight relates to a quantified importance of a particular data input associated with a fixed set of patient data from patients receiving a thiopurine treatment for inflammatory bowel disease in which the clinical response of the patients is known, wherein the set of data comprises at least three data inputs selected from the group consisting of hemoglobin (HGB), hematocrit (HCT), platelet count (PLT), red blood cell count (RBC), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), mean platelet volume (MPV), sodium level (SOD), potassium level (POT), chloride (CHLOR), carbon dioxide (CO2), urea nitrogen (UN), creatinine level (CREAT), glucose level (GLUC), calcium level (CAL), protein level (PROT), albumin level (ALB), aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALK), bilirubin total (TBIL), neutrophil count (NEUT), monocyte count (MONO), basophil count (BASO), and age of patient (Age). - View Dependent Claims (2, 3, 4, 5, 6, 8, 9)
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7. A method of determining the effectiveness of thiopurine treatment in a patient receiving the thiopurine treatment for inflammatory bowel disease (IBD) comprising:
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receiving on a computer device a set of data inputs related to characteristics of a patient receiving a thiopurine treatment for inflammatory bowel disease; and calculating on the computer device a clinical response probability based on the set of data inputs using an algorithm that applies a weight to each data input of the set of data inputs, wherein each weight relates to a quantified importance of a particular data input associated with a fixed set of patient data from patients receiving a thiopurine treatment for inflammatory bowel disease in which the clinical response of the patients is known, wherein the set of data inputs comprises white blood cell count, red blood cell volume distribution width, eosinophile count, albumin level, and age patent, and wherein the clinical response probability is calculated as
probability=1/(1+exp(−
[constant+coefficient1(white blood cell count)+coefficient2(red blood cell volume distribution width)+coefficient3(eosinophil count)+coefficient4(albumin level)+coefficient 5(age of patient)])),wherein coefficient1 is about 0.754, coefficient2 is about 0.804, coefficient3 is about 8.051, coefficient4 is about 2.903, and coefficient5 is about 0.972.
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Specification