Development and validation of an algorithm for identifying patients undergoing dialysis from patients with advanced chronic kidney disease.

Background: Identifying patients on dialysis among those with an estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m remains challenging. To facilitate clinical research in advanced chronic kidney disease (CKD) using electronic health records, we aimed to develop algorithms to identify dialysis patients using laboratory data obtained in routine practice.

Deep Learning for Predicting Acute Exacerbation and Mortality of Interstitial Lung Disease.

Rationale: Some patients with interstitial lung disease (ILD) have a high mortality rate or experience acute exacerbation of ILD (AE-ILD) that results in increased mortality. Early identification of these high-risk patients and accurate prediction of the onset of these important events is important to determine treatment strategies. Although various factors that affect disease behavior among patients with ILD hinder the accurate prediction of these events, the use of longitudinal information may enable better prediction.

Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer.

Background: Multiple first-line treatment options have been developed for advanced non-small cell lung cancer (NSCLC) in each subgroup determined by predictive biomarkers, specifically driver oncogene and programmed cell death ligand-1 (PD-L1) status. However, the methodology for optimal treatment selection in individual patients is not established. This study aimed to develop artificial intelligence (AI)-based personalized survival prediction model according to treatment selection.