Statistical methods of SNP data analysis with applications

Various statistical methods important for genetic analysis are considered and developed. Namely, we concentrate on the multifactor dimensionality reduction, logic regression, random forests and stochastic gradient boosting. These methods and their new modifications, e.g., the MDR method with "independent rule", are used to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and external risk factors are examined. To perform the data analysis concerning the ischemic heart disease and myocardial infarction the supercomputer SKIF "Chebyshev" of the Lomonosov Moscow State University was employed

Mining heterogeneous information graph for health status classification

In the medical domain, there exists a large volume of data from multiple sources such as electronic health records, general health examination results, and surveys. The data contain useful information reflecting people’s health and provides great opportunities for studies to improve the quality of healthcare. However, how to mine these data effectively and efficiently still remains a critical challenge. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. By based on analytics of massive data in the National Health and Nutrition Examination Survey, the study builds a classification model to classify patients’health status and reveal the specific disease potentially suffered by the patient. This paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. Moreover, this research contributes to the healthcare community by providing a deep understanding of people’s health with accessibility to the patterns in various observations

Benchmarking machine learning models on multi-centre eICU critical care dataset

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabled the possibility of establishing public benchmarks. Taking advantage of this opportunity, we propose a public benchmark suite to address four areas of critical care, namely mortality prediction, estimation of length of stay, patient phenotyping and risk of decompensation. We define each task and compare the performance of both clinical models as well as baseline and deep learning models using eICU critical care dataset of around 73,000 patients. This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model. We also investigate the impact of numerical variables as well as handling of categorical variables on each of the defined tasks. The source code, detailing our methods and experiments is publicly available such that anyone can replicate our results and build upon our work.Comment: Source code to replicate the results https://github.com/mostafaalishahi/eICU_Benchmar

Magnetic resonance imaging 3t and total fibrotic volume in autosomal dominant polycystic kidney disease

INTRODUCTION: Autosomal dominant polycystic kidney disease (ADPKD) is the most common renal hereditary disorder. Several authors have attempted to identify a kidney damage marker for predicting the prognosis and the effectiveness of therapy in ADPKD. The aim of this study was to identify and quantify in ADPKD, through a novel MR protocol with 3 Tesla (MRI 3Tesla), the presence of parenchymal fibrotic tissue at early stage of disease, able to correlate the glomerular filtrate and to predict the loss of the function renal. MATERIAL AND METHODS: 15 ADPKD patients undergone to renal MRI 3Tesla at T0 and revaluated after follow up (T1) of 5 years. We have evaluated renal function, plasma aldosterone concentration (PAC), insulin resistance and surrogate markers of atherosclerosis (carotid intima media thickness (IMT), ankle/brachial index (ABI) and left ventricular mass index (LVMI). RESULTS: Our study showed a significant negative correlation between total kidney volume and estimated glomerular filtration rate (eGFR) during observational observation (p<0.02). Moreover, we showed a negative correlation between eGFR with Total Fibrotic Volume (TFV) (p<0.04) and Total Perfusion Volume/Total kidney Volume(<0.02). Moreover TFV was correlated positively with PAC (p<0.05), insulin values (p<0.05), ABI (p <0.05) and LVMI(p<0.01). CONCLUSIONS: The MRI 3Tesla, despite the high costs, could be considered an useful and non-invasive method in the evaluation of fibrotic tissue and progression of the disease in ADPKD patients. Further clinical trials on larger group are due to confirm the results of this pilot study, suggesting that MRI 3Tesla can be useful to evaluate the effectiveness of new therapeutic strategies. This article is protected by copyright. All rights reserved