Současný technologický vývoj přispívá ke generování velkých objemů dat, která nelze vyhodnocovat pouze manuálně. Vývoj metod umělé inteligence a jejich aplikace v medicíně a zdravotnictví umožňuje podporu procesu péče o pacienta technologiemi a metodami analýzy dat. Existuje mnoho úspěšných aplikací, které pomáhají v procesu podpory rozhodování, zejména při zpracování komplexních vícerozměrných heterogenních a/nebo dlouhodobých dat. Na druhé straně se v aplikacích metod umělé inteligence objevují neúspěchy. V posledních letech se stalo velmi populární hluboké učení, které do jisté míry přináší slibné výsledky. Má však určité nedostatky, které mohou vést k chybné klasifikaci. V článku jsou stručně představeny správné metodické kroky při návrhu a implementaci vybraných metod pro zpracování dat.
The aim of the article to present the development of artificial intelligence (AI) methods and their applications in medicine and health care. Current technological development contributes to generation of large volumes of data that cannot be evaluated only manually. We describe the process of patient care and its individual parts that can be supported by technology and data analysis methods. There are many successful applications that help in the decision support process, in processing complex multidimensional heterogeneous and/or long-term data. On the other side, failures appear in AI methods applications. In recent years, deep learning became very popular and to a certain extend it delivered promising results. However, it has certain flaws that might lead to misclassification. The correct methodological steps in design and implementation of selected methods to data processing are briefly presented.
- MeSH
- lékařská informatika MeSH
- lidé MeSH
- umělá inteligence * dějiny MeSH
- veřejné zdravotnictví * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
During the last decade we have been witnessing fast development in the area of sensor technologies and communications that have enabled applications within the Internet of Things (IoT). Subsequently implementations of systems for continuous monitoring of human ́s vital parameters and daily activities started to appear. Since the ageing population is constantly increasing, the development of such applications is necessary. The growing number of sensor types and their producers introduces a problem concerning data formats and data representation. Sensor data representation is an important issue since we do not want to lose any useful information. Additional issue is the design of detection and evaluation algorithms. In the article we present briefly the considered types of sensors, proposed systems architecture, and experimental setup installed in a real apartment.
The paper describes the concept of the Industry 4.0 and its reflection in health care. Industry 4.0 connects intelligent production concepts with external factors, including those linked with the production and those linked more with human, as for example intelligent homes or social web systems. Communication, data and information play an important role in the whole system. After explaining basic characteristics of the Industry 4.0 concept and its main parts, we show how they can be utilized in the health care sector and what their advantages are. Key technologies and techniques include Internet of Things, big data, artificial intelligence, data integration, robotization, virtual reality, and 3D printing. Finally, we identify the main challenges and research directions. Among the most important ones are interoperability, standardization, reliability, security and privacy, ethical and legal issues.
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- big data MeSH
- lidé MeSH
- poskytování zdravotní péče * MeSH
- průmysl MeSH
- reprodukovatelnost výsledků MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Background: Information and communication technologies have become inevitable and an almost inseparable part of our lives. They have also penetrated many application areas including medicine. Objectives: The paper demonstrates the importance of correctly understanding the terms data, information and knowledge that represent the core of decision support and decision making processes. Methods: In the paper we describe the development of decision support systems from the first generation using a simple knowledge representation up to complex distributed solutions. Results: Based on the purpose, we identify three major groups of systems, namely recommendation, decision support and decision making systems. Conclusion: We summarize the development of decision support systems and estimate the future directions.
- MeSH
- systémy pro podporu klinického rozhodování klasifikace trendy MeSH
- Publikační typ
- práce podpořená grantem MeSH
