Pd35-03 Machine Learning Models Su to Predict Kidney Stone Composition and 24-hour Urine Abnormalities From Electronic Health Record-derived Features
Year: 2020
Type: article
Abstract: with kidney stone diagnosis encounters.The algorithm was validated on members with kidney stones between January 2019 to June 2019.RESULTS: A total of 108,000 patients were identified with the diagnosis of kidney stones.The algorithm was applied to 516,000 kidney stone encounters attributed to this cohort.The algorithm was validated on 1,123 patients with kidney stones between January 2019 to June 2019.The models consider 655 attributes that were identified through data analysis and clinician input.The models can predict which patients are at risk for symptomatic kidney stone recurrence with AUCROC [ 0.83 (see Figure 1).The time to recurrence model prioritized patients within the high risk group.C-statistic value is 0.79.CONCLUSIONS: Artificial Intelligence can analyze large and complex data sets and improve clinicians ability to triage patients into low, moderate and high-risk groups.This will improve patient counseling and may improve quality of care by implementing preventive measures on high risk patients and reduce metabolic workup, invasive endoscopy and ionizing radiation exposure for low risk kidney stone patients.
Source: The Journal of Urology
Authors: Chi Le, Nicholas Kavoussi*, Wilson Sui, Cosmin Bejan, Nicole Miller, Ryan His
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W3020891914