Материалы Всероссийской научно-практической конференции с международным участием «ПРОФИЛАКТИЧЕСКАЯ МЕДИЦИНА – 2017»
Materials of the all-Russian scientific-practical conference with international participation
Despite the fact that user-generated data are widely used in medical informatics in general and for revealing side-effects of various pharmaceuticals in particular, recent studies have focused merely on methods of extracting information on side effects from unstructured or semi-structured reviews of specific medications without linking side effects to any outcomes.
In this study we demonstrate how user-generated online content on side effects experienced by patients while taking a pharmaceutical product can be used to do research after the drug has been introduced to the market, thus allowing to complement the results of official clinical studies and market research. In particular, we concentrate on revealing the contribution of various side effects to reported customer satisfaction with Tamiflu, a popular antiviral drug.
Publicly available data from an online platform with reviews from patients are used as an input to the analysis that applies statistical and machine learning methods (multivariate logit models and classification trees) to investigate the relationships of side effects to demographic characteristics and to the overall satisfaction with the medication.
We prioritized side effects of Tamiflu based on the significance of their association with patient’s ratings published at one of the well-known drug discussion forums. Among all types of side effects used in our study, the neuropsychiatric symptoms and body pain are the most influential, followed by skin problems. Specific combinations of side-effects that are associated with low satisfaction were detected.
The proposed analytical approach can help pharmaceutical companies to improve their products and/or medical guidelines associated with their products and figure out fighting which adverse effects should be given a priority from the customer satisfaction perspective.
Preeclampsia (PE) is a pregnancy-specific syndrome, characterized in general by hypertension with proteinuria or other systemic disturbances. PE is the major cause of maternal and fetal morbidity and mortality worldwide. However, the etiology of PE still remains unclear. Our study involved 38 patients: 14 with uncomplicated pregnancy; 13 with early-onset PE (eoPE); and 11 with late-onset PE (loPE). We characterized the immunophenotype of cells isolated from the placenta and all biopsy samples were stained positive for Cytokeratin 7, SOX2, Nestin, Vimentin, and CD44. We obtained a significant increase in OPA1 mRNA and protein expression in the eoPE placentas. Moreover, TFAM expression was down-regulated in comparison to the control (p < 0.01). Mitochondrial DNA copy number in eoPE placentas was significantly higher than in samples from normal pregnancies. We observed an increase of maximum coupled state 3 respiration rate in mitochondria isolated from the placenta in the presence of complex I substrates in the eoPE group and an increase of P/O ratio, citrate synthase activity and decrease of Ca(2+)-induced depolarization rate in both PE groups. Our results suggest an essential role of mitochondrial activity changes in an adaptive response to the development of PE.
The study of clinical terminology has always occupied a significant place in the discipline "Latin language and outlines of medical terminology." Undoubtedly, surgical terminology is one of the most voluminous terminology in the clinical block. Moreover, there are a lot of terms used in it in other departments of the clinical direction, so-called "common" terms.
This volume contains proceedings of the first Workshop on Data Analysis in Medicine held in May 2017 at the National Research University Higher School of Economics, Moscow. The volume contains one invited paper by Dr. Svetla Boytcheva, 6 regular contributions and 2 project proposals, carefully selected and reviewed by at least two reviewers from the international program commit- tee. The papers accepted for publication report on different aspects of analysis of medical data, among them treatment of data on particular diseases (Consoli- dated mathematical growth model of Breast Cancer CoMBreC, Artificial neural networks for prediction of final height in children with growth hormone deficiency), methods of data analysis (analysis of rare diseases, methods of machine learning and Big Data, subgroup discovery for treatment optimization), and instrumental tools (explanation-oriented methods of data analysis in medicine, information support features of the medical research process, modeling frame- work for medical data semantic transformations, radiology quality management and peer-review system). Organizers of the workshop would like to thank the reviewers for their careful work and all contributors and participants of the workshop.