OBJECTIVE: To analyze the influencing factors of early-onset preeclampsia (EOPE). And to construct and validate the prediction model of EOPE using machine learning algorithm.
To explore in depth the characteristics of the risk factors for diabetes and prediabetes pathogenesis and progression in special regions. We investigated medical data from 160 thousand cases in the newly developing urban area of a large modern city f...
Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been unable to resolve this problem, a previously reported machine learning model was developed using pre...
BACKGROUND: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a l...
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Jan 30, 2025
In this paper, we address the question: what practices would be required for the responsible design and operation of real-world swarm robotic systems? We argue that swarm robotic systems must be developed and operated within a framework of ethical go...
International journal of medical informatics
Jan 29, 2025
PURPOSE: Neuroblastoma (NB) is a childhood malignancy with a poor prognosis and a propensity for distant metastasis (DM). We aimed to establish machine learning (ML) based model to accurately predict risk of DM and prognosis of NB patients with DM.
BACKGROUND: Hypertension (HTN) is a global public health concern and a major risk factor for cardiovascular disease (CVD) and mortality. Insulin resistance (IR) plays a crucial role in HTN-related metabolic dysfunction, but its assessment remains cha...
BACKGROUND: Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. Recent studies using benchmark datasets and real-world social media data have demonstrated the ...
BACKGROUND: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).
International journal of medical informatics
Jan 28, 2025
PURPOSE: Stroke remains a leading cause of morbidity and mortality. Despite this, current risk stratification tools such as CHADS-VASc and QRISK3 are of limited accuracy, particularly in those without a diagnosis of atrial-fibrillation. Hence, there ...