JOURNAL ARTICLE

Cognitive IT-systems for big data analysis in medicine

J Isakova
International Journal of Risk & Safety in Medicine 2015, 27 Suppl 1: S108-9
26639685

BACKGROUND: Rapid development of medicine requires regular update of clinical data evidence. This task accomplishment requires participation of numerous specialists in evidence-based medicine, who are proficient in various statistical methods and can work with big data analysis tools in biomedical sciences. This, in turn, requires significant time and other resources. Today, at the peak of IT development, cognitive systems in the field of medicine with special technologies of data collection and analysis, is the start of a new trend.

OBJECTIVE: The development of cognitive IT system for drug prescription with the potential to analyze automatically the information about drugs effectiveness and safety on the basis of clinical practice experience and scientific data according to evidence levels and patients' personal characteristics.

METHODS: The cognitive system was developed with the use of United Medical Knowledge Base (UMKB). UMKB is a semantic network of medical knowledge, which is structured according to the medical ontologies and the theory of fuzzy logic. UMKB is being filled simultaneously in all the areas of medicine. From one side it is filled by means of the linguistic module analyzing medical texts, from the second side - by academic institutions, from the third side - by the cognitive IT systems with the data from electronic health records (EHRs). Native language of UMKB is Russian. It is designed primarily for use in the Russian clinical practice. However the platform for filling knowledge is multilingual and supports any other languages. This means that the practice of world schools may also be integrated and used in UMKB. The peculiarity lies in the fact that UMKB is presented as a semantic network where biomedical knowledge are structured according to certain medical ontologies (special rules of information storage that

RESULTS: On the basis of UMKB a prototype of the cognitive IT system PharmExpert with analytical potential was developed. PharmExpert is a clinical decision support system for drug prescribing, which is integrated into medical information system at health institutions and analyzes electronic health records (EHRs) in any format of the background mode, correcting drug therapy according to personal patient's profile and data about compatibility of the drugs. The system has a very important function - self-learning that will help it to absorb a huge mountain of medical data from routine clinical practice in the nearest future. Now it works on the basis of data from UMKB, handbooks in pharmacology, summaries of medical products characteristics (SmPCs), available reviews of scientific literature and clinical guidelines on drugs interactions and compatibility. In the short term, at the stage of clinical testing, PharmExpert memorizing all the cases of clinical experience and the reaction of the physicians (accepting or ignoring the recommendations of the system), will be able to realize self-learning function by rebuilding ties and remodeling knowledge of the semantic network according to clinical data and generating the best standards of drug therapy taking into account personal characteristics of the patient and levels of data evidence. Working in the background mode is one of the most important advantages of the system. The physician is not asked to enter any additional data beyond that the specialist enters into the EHR on an everyday basis. Now PharmExpert is installed in the medical information systems of the range of clinical centers in the Russian Federation.

CONCLUSIONS: We developed a prototype of cognitive IT system for drug prescription with the potential to analyze automatically the information about drugs effectiveness and safety on the basis of clinical practice experience and scientific data according to evidence levels and patients' personal characteristics. The system is based on the structured semantic network of medical knowledge from UMKB.

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