AI Medical Compendium Topic:
Disease Progression

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Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.

Journal of diabetes science and technology
BACKGROUND: Application of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease processes. We utilized this approach to build predictive models for progression to prediabetes and type 2...

Serial change of C1 inhibitor in patients with sepsis--a preliminary report.

The American journal of emergency medicine
OBJECTIVE: C1 inhibitor (C1INH) regulates not only the complement system but also the plasma kallikrein-kinin, fibrinolytic, and coagulation systems. The biologic activities of C1INH can be divided into the regulation of vascular permeability and ant...

Modified citrus pectin stops progression of liver fibrosis by inhibiting galectin-3 and inducing apoptosis of stellate cells.

Canadian journal of physiology and pharmacology
Modified citrus pectin (MCP) is a pH modified form of the dietary soluble citrus peel fiber known as pectin. The current study aims at testing its effect on liver fibrosis progression. Rats were injected with CCl4 (1 mL/kg, 40% v/v, i.p., twice a wee...

A context-aware approach for progression tracking of medical concepts in electronic medical records.

Journal of biomedical informatics
Electronic medical records (EMRs) for diabetic patients contain information about heart disease risk factors such as high blood pressure, cholesterol levels, and smoking status. Discovering the described risk factors and tracking their progression ov...

Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: A case study on early-stage diagnosis of Parkinson disease.

Journal of neuroscience methods
BACKGROUND: The development of MRI based methods could prove extremely valuable for identification of reliable biomarkers to aid diagnosis of neurodegenerative diseases (NDs). A great deal of current research has been aimed at identification biomarke...

Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration.

Biometrics
Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although, kernel-based st...

Lack of association between anemia and renal disease progression in Chinese patients with type 2 diabetes.

Journal of diabetes investigation
AIMS/INTRODUCTION: Anemia has a close interaction with renal dysfunction in diabetes patients. More proof is still awaited on the relationship between anemia and the progression of renal disease in this population.

Development of a cervical cancer progress prediction tool for human papillomavirus-positive Koreans: A support vector machine-based approach.

The Journal of international medical research
OBJECTIVES: To develop a Web-based tool to draw attention to patients positive for human papillomavirus (HPV) who have a high risk of progression to cervical cancer, in order to increase compliance with follow-up examinations and facilitate good doct...

Predicting outcome in clinically isolated syndrome using machine learning.

NeuroImage. Clinical
We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clini...