Proceedings of the first Workshop on Data Analysis in Medicine (WDAM-2017)
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.
Clinical informatics has been undergoing radical transformation. What are the causes and the drivers of this transformation? Which task can be solved well, and which cannot? How we should implement data analysis in clinical informatics projects in new reality? What is an importance of interpretability (comprehensibility) and explanation of data analysis methods in clinical informatics? At the workshop, we will try to answer some of such questions and setup a framework for later discussion.
Modern medicine aspire to improve the effectiveness of treatment for some diseases through, so called, personalized medicine. However, totally personalized medicine or personalized treatment of even one disease is a very ambitious goal. Subgroup analysis of patients is a preliminary step to the total personalization. Several completely different views on the principles and usefulness of subgroup analysis for treatment personalization exist. This paper is limited to data-driven subgroup discovery, when collected data analyzed for significant treatment-biomarker interactions in post-hoc manner, and presents a brief overview of key methods for this type of subgroup analysis.
This paper is devoted to mathematical modelling of the progression considering stages of breast cancer. Given the relation between primary tumor (PT) and metastases (MTS), the problem of discovering breast cancer (BC) process seems to be twofold: firstly, it is im- portant to describe the whole natural history of BC to understand the process as a whole; secondly, it is necessary to predict the period of a clinical MTS manifestation. In order to understand growth processes of BC on each stage CoMBreC was proposed as a new research tool. The CoMBreC is threefold: CoMPaS (stages I-II), CoM-III (stage III) and CoM-IV (stage IV). A new model rests on exponential growth model and complementing formulas. For the first time, it allows us to calculate different growth periods of PT and MTS in patients with/without lymph nodes MTS: 1) non-visible period for PT; 2) non- visible period for MTS; 3) visible period for MTS. Calculations via CoMBreC correspond to survival data considering stage of BC. It may help to improve predicting accuracy of BC process using an original mathematical model referred to CoMBreC and corresponding software. Consequently, thesis concentrated on: 1) modelling the whole natural history of PT and MTS in patients with/without lymph nodes MTS; 2) developing adequate and precise CoMBreC that reflects relations between PT and MTS; 3) analysing the CoMBreC scope of application. The CoMBreC was implemented to iOS application as a new predictive tool: 1) is a solid foundation to develop future studies of BC models; 2) does not require any expensive diagnostic tests; 3) is the first predictor of survival in breast cancer that makes forecast using only current patient data.