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Chronic Disease

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Identifying Risk Factors for Graft Failure due to Chronic Rejection < 15 Years Post-Transplant in Pediatric Kidney Transplants Using Random Forest Machine-Learning Techniques.

Pediatric transplantation
BACKGROUND: Chronic rejection forms the leading cause of late graft loss in pediatric kidney transplant recipients. Despite improvement in short-term graft outcomes, chronic rejection impedes comparable progress in long-term graft outcomes.

Prediction of phenotypes by secretory biomarkers and machine learning in patients with chronic rhinosinusitis.

European review for medical and pharmacological sciences
OBJECTIVE: Chronic rhinosinusitis (CRS) has traditionally been classified phenotypically according to the presence (CRSwNP) or absence (CRSsNP) of nasal polyps. However, the phenotypic dichotomy does not represent the complexity of the disease. Curre...

A Comprehensive Natural Language Processing Pipeline for the Chronic Lupus Disease.

Studies in health technology and informatics
Electronic Health Records (EHRs) contain a wealth of unstructured patient data, making it challenging for physicians to do informed decisions. In this paper, we introduce a Natural Language Processing (NLP) approach for the extraction of therapies, d...

A Conformal Prediction Approach to Enhance Predictive Accuracy and Confidence in Machine Learning Application in Chronic Diseases.

Studies in health technology and informatics
Heterogeneity in chronic malignancies raises an increasing interest for the integration and study of predictive models. This study presents a machine learning model approach to predict outcomes and improve their trustworthiness in multi-factorial dis...

Learning Physiological Mechanisms that Predict Adverse Cardiovascular Events in Intensive Care Patients with Chronic Heart Disease.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Chronic heart disease is a burdensome, complex, and fatal condition. Learning the mechanisms driving the development of heart disease is key to early risk assessment and intervention. However, many current machine learning approaches lack sufficient ...

[Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) complicated by lung infections using interpretable machine learning.

[Identification of oxidative stress-related biomarkers in chronic rhinosinusitis with nasal polyps using WGCNA combined with machine learning algorithms].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
To identify diagnostic markers related to oxidative stress in chronic rhinosinusitis with nasal polyps (CRSwNP) by analyzing transcriptome sequencing data, and to investigate their roles in CRSwNP. Utilizing four CRSwNP sequencing datasets, differe...

[Exploration of prognostic models for chronic rhinosinusitis with nasal polyps based on machine learning].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
To analysis the molecular characteristics of chronic rhinosinusitis with nasal polyps (CRSwNP), to unravel its pathophysiological mechanisms, and to develop a prognostic model capable of effectively predicting postoperative recurrence. The data fro...