Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification.

Background: Antiretroviral therapy is essential for human immunodeficiency virus (HIV) infected patients to inhibit viral replication and therewith to slow progression of disease and prolong a patient's life. However, the high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure and thereby to the evolution of resistant variants. In turn, these variants will lead to the failure of antiretroviral treatment.

[Diagnosis in context - broadening the perspective].

In a primary care setting the diagnostic process typically starts with a symptom or sign reported by the patient. Primary care physicians face the challenge to consider a broad spectrum of possible aetiologies or differential diagnoses when choosing appropriate diagnostic tests. The classical diagnostic cross-sectional study investigates the accuracy of a diagnostic test or a combination of several tests in regard to just one target disease.

Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction.

Motivation: Antiretroviral treatment regimens can sufficiently suppress viral replication in human immunodeficiency virus (HIV)-infected patients and prevent the progression of the disease. However, one of the factors contributing to the progression of the disease despite ongoing antiretroviral treatment is the emergence of drug resistance. The high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure, thus to failure of antiretroviral treatment due to the evolution of drug-resistant variants.

Multivariate modeling to identify patterns in clinical data: the example of chest pain.

Background: In chest pain, physicians are confronted with numerous interrelationships between symptoms and with evidence for or against classifying a patient into different diagnostic categories. The aim of our study was to find natural groups of patients on the basis of risk factors, history and clinical examination data which should then be validated with patients' final diagnoses.

Methods: We conducted a cross-sectional diagnostic study in 74 primary care practices to establish the validity of symptoms and findings for the diagnosis of coronary heart disease.