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Linking with BIAM: searching for drugs and pharmaceutical substances

100%

Węgrzyn-Wolska K.

tom Vol. 11

255--261

The quantity of biomedical publications is growing at an exponential rate. With such explosive growth of the content, it is more and more difficult to locate, retrieve and manage the resulting information. This is why text mining has become a necessity. The main goal of biomedical research is to put knowledge to practical use in the form of diagnoses, prevention, and treatment. It is important to pool the resources between the different individuals researching results. The objective of this paper is to discuss the variety of issues and challenges surrounding the perspectives regarding the use of Information Retrieval and Text Mining methods in biomedicine. The article will first look at the directions in biomedical TM and then describe the work done for the BIAM project, the French on-line Medical Data Base.

Missing-data imputation using wearable sensors in heart rate variability

51%

Tlija A. , Węgrzyn-Wolska K. , Istrate D.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2020

tom Vol. 68, nr 2

255--261

The objective of this work is to set up a methodology that considers missing data from a connected heartbeat sensor in order to propose a good replacement methodology in the context of heart rate variability (HRV) computation. The framework is a research project, which aims to build a system that can measure stress and other factors influencing the onset and development of heart disease. The research encompasses studying existing methods, and improving them by use of experimental data from case study that describe the participant’s everyday life. We conduct a study to modelize stress from the HRV signal, which is extracted from a heart rate monitor belt connected to a smart watch. This paper describes data recording procedure and data imputation methodology. Missing data is a topic that has been discussed by several authors. The manuscript explains why we choose spline interpolation for data values imputation. We implement a random suppression data procedure and simulate removed data. After that, we implement several algorithms and choose the best one for our case study based on the mean square error.