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Zaitseva E, Levashenko V, Rabcan J, Kvassay M. A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine. Bioengineering (Basel) 2023; 10:838. [PMID: 37508865 PMCID: PMC10376790 DOI: 10.3390/bioengineering10070838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/08/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The development of information technology has had a significant impact on various areas of human activity, including medicine. It has led to the emergence of the phenomenon of Industry 4.0, which, in turn, led to the development of the concept of Medicine 4.0. Medicine 4.0, or smart medicine, can be considered as a structural association of such areas as AI-based medicine, telemedicine, and precision medicine. Each of these areas has its own characteristic data, along with the specifics of their processing and analysis. Nevertheless, at present, all these types of data must be processed simultaneously, in order to provide the most complete picture of the health of each individual patient. In this paper, after a brief analysis of the topic of medical data, a new classification method is proposed that allows the processing of the maximum number of data types. The specificity of this method is its use of a fuzzy classifier. The effectiveness of this method is confirmed by an analysis of the results from the classification of various types of data for medical applications and health problems. In this paper, as an illustration of the proposed method, a fuzzy decision tree has been used as the fuzzy classifier. The accuracy of the classification in terms of the proposed method, based on a fuzzy classifier, gives the best performance in comparison with crisp classifiers.
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Affiliation(s)
- Elena Zaitseva
- Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, 01026 Zilina, Slovakia
| | - Vitaly Levashenko
- Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, 01026 Zilina, Slovakia
| | - Jan Rabcan
- Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, 01026 Zilina, Slovakia
| | - Miroslav Kvassay
- Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, 01026 Zilina, Slovakia
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Takhar A, Surda P, Ahmad I, Amin N, Arora A, Camporota L, Denniston P, El-Boghdadly K, Kvassay M, Macekova D, Munk M, Ranford D, Rabcan J, Tornari C, Wyncoll D, Zaitseva E, Hart N, Tricklebank S. Timing of Tracheostomy for Prolonged Respiratory Wean in Critically Ill Coronavirus Disease 2019 Patients: A Machine Learning Approach. Crit Care Explor 2020; 2:e0279. [PMID: 33225305 PMCID: PMC7673767 DOI: 10.1097/cce.0000000000000279] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES To propose the optimal timing to consider tracheostomy insertion for weaning of mechanically ventilated patients recovering from coronavirus disease 2019 pneumonia. We investigated the relationship between duration of mechanical ventilation prior to tracheostomy insertion and in-hospital mortality. In addition, we present a machine learning approach to facilitate decision-making. DESIGN Prospective cohort study. SETTING Guy's & St Thomas' Hospital, London, United Kingdom. PATIENTS Consecutive patients admitted with acute respiratory failure secondary to coronavirus disease 2019 requiring mechanical ventilation between March 3, 2020, and May 5, 2020. INTERVENTIONS Baseline characteristics and temporal trends in markers of disease severity were prospectively recorded. Tracheostomy was performed for anticipated prolonged ventilatory wean when levels of respiratory support were favorable. Decision tree was constructed using C4.5 algorithm, and its classification performance has been evaluated by a leave-one-out cross-validation technique. MEASUREMENTS AND MAIN RESULTS One-hundred seventy-six patients required mechanical ventilation for acute respiratory failure, of which 87 patients (49.4%) underwent tracheostomy. We identified that optimal timing for tracheostomy insertion is between day 13 and day 17. Presence of fibrosis on CT scan (odds ratio, 13.26; 95% CI [3.61-48.91]; p ≤ 0.0001) and Pao2:Fio2 ratio (odds ratio, 0.98; 95% CI [0.95-0.99]; p = 0.008) were independently associated with tracheostomy insertion. Cox multiple regression analysis showed that chronic obstructive pulmonary disease (hazard ratio, 6.56; 95% CI [1.04-41.59]; p = 0.046), ischemic heart disease (hazard ratio, 4.62; 95% CI [1.19-17.87]; p = 0.027), positive end-expiratory pressure (hazard ratio, 1.26; 95% CI [1.02-1.57]; p = 0.034), Pao2:Fio2 ratio (hazard ratio, 0.98; 95% CI [0.97-0.99]; p = 0.003), and C-reactive protein (hazard ratio, 1.01; 95% CI [1-1.01]; p = 0.005) were independent late predictors of in-hospital mortality. CONCLUSIONS We propose that the optimal window for consideration of tracheostomy for ventilatory weaning is between day 13 and 17. Late predictors of mortality may serve as adverse factors when considering tracheostomy, and our decision tree provides a degree of decision support for clinicians.
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Affiliation(s)
- Arunjit Takhar
- Department of Otolaryngology and Head and Neck Surgery, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Pavol Surda
- Department of Otolaryngology and Head and Neck Surgery, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Imran Ahmad
- Department of Anaesthesia, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
- Kings College London, United Kingdom
| | - Nikul Amin
- Department of Otolaryngology and Head and Neck Surgery, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Asit Arora
- Department of Otolaryngology and Head and Neck Surgery, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Luigi Camporota
- Department of Critical Care, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Poppy Denniston
- Department of Respiratory Medicine, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Kariem El-Boghdadly
- Department of Anaesthesia, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
- Kings College London, United Kingdom
| | - Miroslav Kvassay
- Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, Zilina, Slovakia
| | - Denisa Macekova
- Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, Zilina, Slovakia
| | - Michal Munk
- Department of Informatics, Constantine the Philosopher University, Nitra, Slovakia
| | - David Ranford
- Department of Critical Care, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Jan Rabcan
- Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, Zilina, Slovakia
| | - Chysostomos Tornari
- Department of Otolaryngology and Head and Neck Surgery, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Duncan Wyncoll
- Department of Critical Care, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Elena Zaitseva
- Department of Informatics, Constantine the Philosopher University, Nitra, Slovakia
| | - Nicholas Hart
- Lane Fox Respiratory Unit, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
- Kings College London, United Kingdom
| | - Stephen Tricklebank
- Department of Critical Care, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
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