Systems and methods for predicting kidney function decline

A system, comprising:

• one or more processors;
• and
• one or more hardware storage devices storing instructions that are executable by the one or more processors to configure the system to:
• access a training dataset comprising (i) a first set of medical laboratory data associated with a plurality of patients, (ii) an age of each patient included in the plurality of patients, and (iii) a sex of each patient included in the plurality of patients, the first set of medical laboratory data indicating, for at least a combination of patients included in the plurality of patients:
• estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (ACR), urea, serum sodium, serum chloride, serum hemoglobin, serum potassium, glucose, serum albumin, alkaline phosphatase, serum phosphate, serum bicarbonate, serum magnesium, serum calcium, aspartate aminotransferase (AST), alanine transaminase (ALT), bilirubin, gamma-glutamyl transferase (GGT), hematocrit, and platelet count;
• and
• generate a machine learning model by applying the training dataset to an untrained model, the machine learning model being configured to generate a prediction of chronic kidney disease (CKD) progression for a new patient by applying an input dataset associated with the new patient to the machine learning model, the input dataset comprising an age of the new patient, a sex of the new patient, and a second set of medical laboratory data indicating for the new patient one or more of:
• eGFR, urine ACR, urea, serum sodium, serum chloride, serum hemoglobin, serum potassium, glucose, serum albumin, alkaline phosphatase (ALKP), serum phosphate, serum bicarbonate, serum magnesium, serum calcium, AST, ALT, bilirubin, GGT, hematocrit, and platelet count.

One or more hardware storage devices storing instructions that are executable by one or more processors of a system to configure the system to:

• access a machine learning model configured to generate a prediction of chronic kidney disease (CKD) progression, the machine learning model being trained on a training dataset comprising (i) a first set of medical laboratory data associated with a plurality of patients, (ii) an age of each patient included in the plurality of patients, and (iii) a sex of each patient included in the plurality of patients, the first set of medical laboratory data indicating, for at least a combination of patients included in the plurality of patients:
• urine albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), urea, hemoglobin;
• and
• generate a prediction of CKD progression for a new patient by applying an input dataset associated with the new patient to the machine learning model, the prediction of CKD progression for the new patient being based upon output of the machine learning model resulting from applying the input dataset associated with the new patient to the machine learning model, the input dataset comprising an age of the new patient, a sex of the new patient, and a second set of medical laboratory data comprising one or more components of a urine chemistry test, a comprehensive metabolic panel, a complete blood cell count, a liver panel, or a uric acid test for the new patient.