Abstract: An example method of diagnosing chronic kidney disease (CKD) includes obtaining an electronic medical record for a patient having medical data. The medical data includes an indication if the patient had been previously diagnosed with CKD, an indication if the patient had previously undergone a kidney transplant, an indication if the patient had previously undergone a renal dialysis procedure, an indication if the patient had previously been diagnosed with another type of kidney disease, one or more glomerular filtration rate (GFR) measurements associated with the patient, an indication if the patient has type 2 diabetes, and/or an indication if the patient has hypertension. The method also includes automatically determining that the patient has CKD or does not have CKD based on the medical data in the electronic record.

Abstract: An example method of diagnosing chronic kidney disease (CKD) includes obtaining an electronic medical record for a patient having medical data. The medical data includes an indication if the patient had been previously diagnosed with CKD, an indication if the patient had previously undergone a kidney transplant, an indication if the patient had previously undergone a renal dialysis procedure, an indication if the patient had previously been diagnosed with another type of kidney disease, one or more glomerular filtration rate (GFR) measurements associated with the patient, an indication if the patient has type 2 diabetes, and/or an indication if the patient has hypertension. The method also includes automatically determining that the patient has CKD or does not have CKD based on the medical data in the electronic record.

Abstract: An example method of automated medical diagnosis includes obtaining an electronic longitudinal data set for each of a plurality of patients, where each data set includes a plurality of measurement values corresponding to a metric, where each measurement value is associated with a respective time point. The method also includes arranging the data sets into two or more clusters. Arranging the data sets includes aligning the data sets according to their respective time points, selecting a cluster center for each cluster, determining a similarity between each data set and each cluster center, assigning each data set to a particular cluster based on the similarities, and iteratively re-aligning one or more of the data sets and/or reselecting one or more cluster centers, determining an updated similarity between each data set and each cluster center, and re-assigning data sets to particular clusters based on the updated similarities until a stop criterion is met.

Abstract: An example method of diagnosing chronic kidney disease (CKD) includes obtaining an electronic medical record for a patient having medical data. The medical data includes an indication if the patient had been previously diagnosed with CKD, an indication if the patient had previously undergone a kidney transplant, an indication if the patient had previously undergone a renal dialysis procedure, an indication if the patient had previously been diagnosed with another type of kidney disease, one or more glomerular filtration rate (GFR) measurements associated with the patient, an indication if the patient has type 2 diabetes, and/or an indication if the patient has hypertension. The method also includes automatically determining that the patient has CKD or does not have CKD based on the medical data in the electronic record.

Abstract: The use of a medical software platform to collect, normalize, and aggregate clinical data. Supports clinical care and provides research and clinical tools for institutions, healthcare providers, researchers, and patients. The invention provides a graphical interface to customize searches in the database for specified subsets of conditions, treatments or outcomes. The invention also provides a system and method for searching through clinical databases for desired terms which may provide additional information to a physician regarding patient care. Furthermore, this invention facilitates personalized medicine-based practices as relationships between genetics, personal heath data from multiple sources, disease risk and drug response can be more easily visualized and utilized for patient care and research.