Methodology of ECG Interpretation in the Padova Program

1990 ◽  
Vol 29 (04) ◽  
pp. 386-392 ◽  
Author(s):  
R. Degani ◽  
G. Bortolan
Keyword(s):  
Decision Making ◽  
Medical School ◽  
Fuzzy Set ◽  
Medical Knowledge ◽  
Main Aspect ◽  
Measurement Evaluation ◽  
Possibility Distributions ◽  
Linguistic Decision Making ◽  
The University ◽  
Ecg Interpretation

AbstractThe main lines ofthe program designed for the interpretation of ECGs, developed in Padova by LADSEB-CNR with the cooperation of the Medical School of the University of Padova are described. In particular, the strategies used for (i) morphology recognition, (ii) measurement evaluation, and (iii) linguistic decision making are illustrated. The main aspect which discerns this program in comparison with other approaches to computerized electrocardiography is its ability of managing the imprecision in both the measurements and the medical knowledge through the use of fuzzy-set methodologies. So-called possibility distributions are used to represent ill-defined parameters as well as threshold limits for diagnostic criteria. In this way, smooth conclusions are derived when the evidence does not support a crisp decision. The influence of the CSE project on the evolution of the Padova program is illustrated.

Prospective feasibility study of a novel Diagnostic Decision Support System for medical professionals (Preprint)

2021 ◽  
Author(s):  
Joanna Timiliotis ◽  
Bibiana Blümke ◽  
Peter Serfözö ◽  
Stephen Gilbert ◽  
Marta Ondresik ◽  
...  
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Decision Support ◽  
Feasibility Study ◽  
Medical Knowledge ◽  
University Hospital ◽  
Medical Professionals ◽  
Main Diagnosis ◽  
Convenience Sample ◽  
The University

BACKGROUND Continuously growing medical knowledge and the increasing amount of data make it difficult for medical professionals to keep track of all new information, and to place this in context of existing information. Digital technologies and artificial intelligence (AI)- based methods have recently emerged as impressively persuasive tools to empower physicians in clinical decision making and improve healthcare quality. A novel DDSS prototype, developed with a focus on traceability, transparency and usability by Ada Health GmbH will be examined more closely in this study. OBJECTIVE Feasibility and functionality test of a novel DDSS prototype, exploring the potential and performance to identify the underlying cause of acute dyspnea in patients at the University Hospital Basel. METHODS A prospective, observational feasibility study was conducted at the Emergency Department (ED) and Internal Medicine ward of the University Hospital Basel Switzerland. A convenience sample of 20 adult patients entering the ED with dyspnea as the chief complaint and a high probability for inpatient admission were selected. A study physician followed the patients admitted to the ED through the hospitalisation without any interference with the routine clinical work. Routinely collected, health-related, personal data from those patients were entered in the DDSS prototype. The DDSS prototype’s resulting disease probability list was compared with the gold standard main diagnosis provided by the treating physician. A panel of three physicians with different levels of clinical experience and expertise evaluated the matching diagnoses from the hospital and from the DDSS prototype. RESULTS The study of the feasibility and functionality of the tool was successful with some limitations. The DDSS had high clarity of information presentation and a user-friendly, novel and transparent interface. The DDSS prototype was not perfectly suited for the emergency department because case entry was time consuming. It provided accurate decision support in the clinical inpatient setting in many patients with dyspnea as a main presenting complaint. CONCLUSIONS Used in the right place, the DDSS has the potential to support doctors in their decision-making process by showing new pathways and unintentionally ignored diagnoses. The DDSS prototype had some limitations regarding the process of data input, diagnostic accuracy and the completeness of integrated medical knowledge. The results of this study provide a basis for the tool’s further development. Additionally future studies should be conducted with the aim to overcome the tool’s and study design’s present limitations. CLINICALTRIAL clinicaltrials.gov RN: NCT04827342

An Intuitionistic Fuzzy Set-Based Model for Intelligent Traditional Chinese Medicine Diagnosis

2011 ◽  
Vol 480-481 ◽  
pp. 944-949
Author(s):  
Mei Hong Wu
Keyword(s):  
Decision Making ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Medical Knowledge ◽  
Syndrome Differentiation ◽  
New Approach ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Match

According to the characteristics of Traditional Chinese Medicine, this paper introduces an intuitionistic fuzzy set-based method to realize the intelligent diagnostic decision making. We firstly concentrate on diagnosis of diseases and differentiation of syndromes by modeling medical diagnosis rules via intuitionistic fuzzy relations as well as how to obtain intuitionistic medical knowledge on the basis of intuitionistic fuzzy sets. Subsequently we develop a new approach to point out the final proper diagnosis by largest degree of intuitionistic cognitive fuzzy match between symptoms characteristic for a patient and symptoms indicate the considered illnesses in decision-making process. The new approach allows reaching the intelligent diagnoses reasonably and easily, which benefit TCM syndrome differentiation for the whole diagnosis.

Evaluation of the Academic Setting in Spain 1This study was financed by the University of Oviedo.

1995 ◽  
Vol 11 (2) ◽  
pp. 133-137 ◽  
Author(s):  
Juan Fernández ◽  
Miguel A. Mateo ◽  
José Muñiz
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Social Climate ◽  
Three Dimensions ◽  
Academic Setting ◽  
Teaching And Research ◽  
Evaluation Questionnaire ◽  
University Teachers ◽  
The University

The conditions are investigated in which Spanish university teachers carry out their teaching and research functions. 655 teachers from the University of Oviedo took part in this study by completing the Academic Setting Evaluation Questionnaire (ASEQ). Of the three dimensions assessed in the ASEQ, Satisfaction received the lowest ratings, Social Climate was rated higher, and Relations with students was rated the highest. These results are similar to those found in two studies carried out in the academic years 1986/87 and 1989/90. Their relevance for higher education is twofold because these data can be used as a complement of those obtained by means of students' opinions, and the crossing of both types of data can facilitate decision making in order to improve the quality of the work (teaching and research) of the university institutions.

Some new Pythagorean fuzzy correlation techniques via statistical viewpoint with applications to decision-making problems

2021 ◽  
pp. 1-13
Author(s):  
Paul Augustine Ejegwa ◽  
Shiping Wen ◽  
Yuming Feng ◽  
Wei Zhang ◽  
Jia Chen
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Superior Performance ◽  
Statistical Techniques ◽  
New Techniques ◽  
Reliable Technique ◽  
Performance Indexes ◽  
Fuzzy Correlation

Pythagorean fuzzy set is a reliable technique for soft computing because of its ability to curb indeterminate data when compare to intuitionistic fuzzy set. Among the several measuring tools in Pythagorean fuzzy environment, correlation coefficient is very vital since it has the capacity to measure interdependency and interrelationship between any two arbitrary Pythagorean fuzzy sets (PFSs). In Pythagorean fuzzy correlation coefficient, some techniques of calculating correlation coefficient of PFSs (CCPFSs) via statistical perspective have been proposed, however, with some limitations namely; (i) failure to incorporate all parameters of PFSs which lead to information loss, (ii) imprecise results, and (iii) less performance indexes. Sequel, this paper introduces some new statistical techniques of computing CCPFSs by using Pythagorean fuzzy variance and covariance which resolve the limitations with better performance indexes. The new techniques incorporate the three parameters of PFSs and defined within the range [-1, 1] to show the power of correlation between the PFSs and to indicate whether the PFSs under consideration are negatively or positively related. The validity of the new statistical techniques of computing CCPFSs is tested by considering some numerical examples, wherein the new techniques show superior performance indexes in contrast to the similar existing ones. To demonstrate the applicability of the new statistical techniques of computing CCPFSs, some multi-criteria decision-making problems (MCDM) involving medical diagnosis and pattern recognition problems are determined via the new techniques.

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

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