Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.
- MeSH
- Algorithms MeSH
- Fuzzy Logic * MeSH
- Heuristics physiology MeSH
- Humans MeSH
- Decision Support Techniques MeSH
- Probability MeSH
- Decision Trees * MeSH
- Decision Making physiology MeSH
- Models, Theoretical MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.
3rd ed. XXIII, 659 s. : il. ; 30 cm
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- anesteziologie a intenzivní lékařství
Neurology, ISSN 0028-3878 vol. 50, no. 3, suppl. 3, 1998
57 s. : il., tab. ; 28 cm
- MeSH
- Algorithms MeSH
- Disease Management MeSH
- Parkinson Disease therapy MeSH
- Decision Trees MeSH
- Quality Assurance, Health Care MeSH
- Publication type
- Collected Work MeSH
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- neurologie
Neurology, ISSN 0028-3878 vol. 56, no. 11, suppl. 5, 2001
88 s. : il. ; 28 cm + 2 plakáty
- MeSH
- Algorithms MeSH
- Disease Management MeSH
- Neuroprotective Agents therapeutic use MeSH
- Nutrition Therapy MeSH
- Parkinson Disease diagnosis drug therapy surgery MeSH
- Parkinsonian Disorders complications MeSH
- Decision Trees MeSH
- Publication type
- Guideline MeSH
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- neurologie
- neurochirurgie
- farmacie a farmakologie
Implantology is rapidly developing interdisciplinary field providing enormous amounts of data to be classified, evaluated and interpreted. The analysis of clinical data remains a big challenge, because each new system has specific requirements. The aim of study was prepare specific tool for treatment planning. Decision support system is built on Expert system. It is interactive software which provides clinical recommendations and treatment planning. Expert systems are knowledge-based computer programs designed to provide assistance in diagnosis and treatment planning. These systems are used for health care (dentistry, medicine, pharmacy etc.). The application contained the medical history analysis to obtaining information useful in formulating a diagnosis and providing implant insertion and prosthetic reconstruction to the patient; the diagnostic examination of dental implant procedure; implant positioning diagnosis – 3-D measurement; diagnostic information for treatment planning; treatment plan in the form of objective measurement of implant placement that helps surgeon and prosthodontics. The decision algorithm implemented by programming language is used. Core of program is an expert knowledge programming like a decision tree. The analysis of the decision-making process for implant treatment in general practice is prepared and analyzed.
- MeSH
- Dental Implantation MeSH
- Humans MeSH
- Prosthodontics * trends MeSH
- Decision Support Systems, Clinical * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
Aim: The aim of the survey is to identify factors of the work environment which are important for general nurses when they are considering whether or not to leave their current employer. Design: The research consists of an observational and a crosssectional study. Methods: Based on a modified interpretation of Herzberg's theory, we created a structured interview to investigate environmental factors. Interviewers carried out 1,992 interviews with hospital nurses working in the Czech Republic, between 2011 and 2012. The data gathered were analyzed with data mining tools – a decision tree and nonparametric tests. Results: If a good opportunity arose, 34.7% of nurses would leave their current employer. The analysis of the decision tree identified the factor “Patient care”, i.e. a factor concerning the nature of the work itself, as the most important. Data mining offers a new view of the data and can reveal valuable information existing within the primary data. Conclusion: Data mining has great potential in nursing. In this research, the decision tree shows that the essence of the nursing profession is the nursing work itself and it is also the most significant stabilizing factor. The management of healthcare providers should create and maintain a work environment which will ensure nursing work can be performed without impediment, thus minimizing staff turnover.
- MeSH
- Data Mining MeSH
- Humans MeSH
- Motivation MeSH
- Salaries and Fringe Benefits MeSH
- Patient Care psychology MeSH
- Occupational Stress MeSH
- Job Satisfaction * MeSH
- Decision Trees MeSH
- Employment MeSH
- Nurses * economics classification psychology statistics & numerical data MeSH
- Check Tag
- Humans MeSH
- Female MeSH
- Publication type
- Observational Study MeSH
- Research Support, Non-U.S. Gov't MeSH
Background and Purpose: Currently, there is no available Finnish version of the Genomic Nursing Concept Inventory tool (GNCI). This study tested the validity, reliability, and clinical usability of a Finnish translation. Methods: A decision tree algorithm was used to guide the translation, as per International Society for Pharmacoeconomics and Outcomes Research guidelines. Item-Content Validity Index (I-CVI), modified kappa (k*) statistics, and Cronbach's alpha were calculated. Results: The I-CVI and k* values were "good" to "excellent" (I-CVI = 0.63-1.00, k* = 0.52-1.00), and Cronbach's alpha value was "good" (α = 0.816; 95% confidence interval: 0.567-0.956). Conclusion: The Mandysova's decision tree algorithm provided clear and rigorous direction for the translation and validity of the Finnish GNCI.
- MeSH
- Genomics * MeSH
- Humans MeSH
- Linguistics * MeSH
- Surveys and Questionnaires MeSH
- Reproducibility of Results MeSH
- Decision Trees MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Finland MeSH
Cílem příspěvku je ukázat výhody zapojení experta do tvorby znalostního obsahu systému pro podporu rozhodování nejen v klasickém pojetí formalizace znalostí experta do báze znalostí, ale i poněkud méně tradičním způsobem. Tento přístup se začal v nedávné době nazývat „human/expert-in-the-loop“. Základní myšlenkou je využít znalosti experta, které nemusejí být obsaženy v datech, jejichž analýzu chceme provádět, ale které mohou významně zlepšit kvalitu rozhodovacího procesu. Často je možné v rámci tohoto kroku integrovat znalosti více expertů. Pokud se takové přístupy využívají přímo v metodách strojového učení, označují se také jako aktivní učení. Na několika případových studiích z různých oblastí medicíny ukážeme, v jakých fázích procesu vývoje může expert vhodně do procesu zasáhnout. První případová studie je věnována porovnání výsledků získaných pomocí znalostního systému, jehož báze znalostí byla vytvářena manuálně formalizací slovně popsaných znalostí, a pomocí metod strojového učení, konkrétně rozhodovacího stromu, naučeného na větším souboru dat. Následně byla báze znalostí porovnána s rozhodovacím stromem a doplněna o vybraná pravidla z tohoto stromu. Takto upravená báze znalostí poskytovala lepší rozhodnutí než původní. Další dvě případové studie jsou zaměřené na úlohu klasifikace v dlouhodobých záznamech biologických signálů, konkrétně elektroencefalografických a polysomnografických. U této úlohy je klíčové nalézt vhodný poměr mezi zobecněnými metodami, použitelnými na záznamy všech pacientů, a metodami nastavenými na konkrétního pacienta (jedna z možností personalizace). Motivací pro takové řešení je velká interpersonální variabilita, a u řady diagnóz i intrapersonální variabilita. Možnost interaktivního vstupu experta do procesu analýzy záznamů může přispět ke zvýšení kvality a konzistence hodnocení.
This paper aims to show the benefits of expert involvement in the creation of knowledge-based content system to support decision making not only in the classic conception of formalization of expert knowledge into the knowledge base, but also somewhat less traditional way. This approach has recently started to be called "human / expert-in-the-loop". The basic idea is to use the expert knowledge that may not be included in the data, whose analysis we perform, but which can significantly improve the quality of decision making. In this step it is often possible to integrate knowledge of more experts. If such approaches are used in machine learning methods, they are known as active learning. We present several case studies from different areas of medicine, in which we show how experts can interact with the system. The first case study is devoted to compare the results obtained using the knowledge system whose knowledge base was created manually from verbally described expert knowledge, and by using machine learning techniques (specifically the decision tree) learned on a larger data set. Subsequently, the knowledge base was compared with the decision tree and supplemented by selected rules from the tree. This adjusted knowledge base provided better results than the original one. The other two case studies are focused on classification in long-term records of biological signals, namely electroencephalographic and polysomnographic. The key issue is to find the appropriate balance between the generalized methods applicable to the records of all patients, and methods adjusted for each patient (one of the personalization options). The motivation for this approach is the large interpersonal variability, and intrapersonal variability at certain diagnoses. An interactive expert input into the process of analyzing the records can contribute to improvement of the quality and consistency of assessment.
Epilepsy Expert is a decision support system based on the International Classification of Epilepsies and Epileptic Syndromes (1989). The aim of this study was to evaluate the Epilepsy Expert. First the diagnostic performance was validated. This was done in 3 stages: collection of the patient cases, determination of the 'correct diagnoses' and testing the system. How the users perceived the functionality of the system was studied by using an inquiry. Three physicians, experts of epilepsy, from different hospitals were asked to choose 10 patients. In the patient description was a short history, a detailed description of the seizure, EEG findings and their own diagnosis. Next, each expert made a diagnosis of the cases supplied by other experts by using the International Classification of Epilepsies and Epileptic Syndromes. The 'correct diagnosis' (so-called majority agreement) was the diagnosis given by the majority of the experts. The diagnosis of each expert was compared with the 'correct diagnosis'. The diagnoses obtained by the Epilepsy Expert were then compared with the 'correct diagnoses'. In the evaluation the expert physicians agreed on 37% of cases and all 3 disagreed on 17%. A majority agreed on 25 cases, which were used in the evaluation. In these 25 cases the experts' (A,B,C) diagnoses were correct or partly correct in 100, 64, 80% of cases, respectively. The program's diagnoses were correct or partly correct in 80% (module I) and 76% (modules IV and V) of cases. In the evaluation Epilepsy Expert was found to be only partly successful. The main reason for this was the weakness of the international classification. However, the program seems to be very close to the level of the experts. According to this limited inquiry Epilepsy Expert is not suitable for clinical use, because it is, for example, too simple and does not contain enough information.
- MeSH
- Diagnosis, Computer-Assisted * MeSH
- Adult MeSH
- Electroencephalography MeSH
- Epilepsy diagnosis etiology classification MeSH
- Expert Systems * MeSH
- Evaluation Studies as Topic MeSH
- Middle Aged MeSH
- Humans MeSH
- Decision Trees MeSH
- Decision Making, Computer-Assisted MeSH
- Software MeSH
- User-Computer Interface MeSH
- Software Validation MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Female MeSH
- Publication type
- Case Reports MeSH
