Machine learning-based outcome prediction and novel hypotheses generation for substance use disorder treatment.

Objective: Substance use disorder is a critical public health issue. Discovering the synergies among factors impacting treatment program success can help governments and treatment facilities develop effective policies. In this work, we propose a novel data analytics approach using machine learning models to discover interaction effects that might be neglected by traditional hypothesis-generating approaches.

A machine learning-based approach to prognostic analysis of thoracic transplantations.

Objective: The prediction of survival time after organ transplantations and prognosis analysis of different risk groups of transplant patients are not only clinically important but also technically challenging. The current studies, which are mostly linear modeling-based statistical analyses, have focused on small sets of disparate predictive factors where many potentially important variables are neglected in their analyses. Data mining methods, such as machine learning-based approaches, are capable of providing an effective way of overcoming these limitations by utilizing sufficiently large data sets with many predictive factors to identify not only linear associations but also highly complex, non-linear relationships.

Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology.

Background: Predicting the survival of heart-lung transplant patients has the potential to play a critical role in understanding and improving the matching procedure between the recipient and graft. Although voluminous data related to the transplantation procedures is being collected and stored, only a small subset of the predictive factors has been used in modeling heart-lung transplantation outcomes. The previous studies have mainly focused on applying statistical techniques to a small set of factors selected by the domain-experts in order to reveal the simple linear relationships between the factors and survival.

[Comparison of the serological tests used for the laboratory diagnosis of brucellosis].

Brucellosis which is an important public health problem is a zoonotic disease that causes economic loss, and seen all over the world as well as in our country. The aim of this study was to compare the results of Rose Bengal test, standard tube agglutination test (STA), Coombs tube agglutination test, Rivanol tube agglutination test and ELISA (IgA, IgG and IgM) method, in patients who were suspected to have brucellosis. Blood and serum samples collected from 77 patients were included to the study.