Reliability Estimation of Healthcare Systems using Fuzzy Decision Trees

Levashenko, Vitaly; Zaitseva, Elena; Deserno, Thomas M.

doi:10.15439/2016f150

Cited by 18 publications

(7 citation statements)

References 27 publications

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“…More and more data are collected either by interviews, equipment [39] or extraction from text [45], speech [46] or images [47], including medical imaging [48]. In high-dimensional datasets missing data may be more frequent [49] and appropriate feature selection technique [50], [51] may improve the imputation accuracy [10]. Novel solutions of outlier detection based on linguistically quantified statements may be also considered to remove impurities from the data [52].…”

Section: Discussionmentioning

confidence: 99%

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

Wosiak

Glinka

Zamecznik

et al. 2018

Annals of Computer Science and Information Systems

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The paper describes the study on the problem of missing values in medical data collected to discover new dependencies between parameters in children born with intrauterine growth restriction disorder. The aim of the research is to propose a procedure that may be taken to improve the medical inference in the presence of missing data. The approach with use of unconditional mean and k-nearest neighbor imputation has been applied. The experiments proved that application of missing data imputation in original dataset yields more valuable dependencies when compared to original data, maintaining the confidence interval for goodness of fit with the original distribution above 90%. The discovered dependencies in data may establish the basis for new treatment procedures of children with intrauterine growth restriction disorder.

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Section: Discussionmentioning

confidence: 99%

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

Wosiak

Glinka

Zamecznik

et al. 2018

Annals of Computer Science and Information Systems

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show abstract

“…For example, consider a simplified laparoscopic surgery procedure from [43]. This system consists of four components (n = 4): two doctors (an anaesthesiologist (x 1 ) and a surgeon (x 2 )), a nurse (x 3 ), and a laparoscopic robotic surgery machine (technical component (x 4 )).…”

Section: Structure Functionmentioning

confidence: 99%

“…Table 1. Binary-State System (BSS) structure function of the system of a simplified laparoscopic surgery procedure [43].…”

Section: Structure Functionmentioning

confidence: 99%

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Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

2021

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Health and safety is a problem that is intensively discussed nowadays. The failures in healthcare are called medical errors: if the patient’s condition worsens or he/she contracts an illness, then the actions that led to this are interpreted as medical errors. Medical errors can be the result of new procedures, extremes of age, complex or urgent care, improper documentation, illegible hand-writing, or patient actions. One of the ways to reduce medical error is an evaluation of its possibility, and then using the result of this evaluation to improve the medical organization units and processes in patient diagnosis, treatment, and care. This evaluation is possible based on methods of reliability engineering. The reliability engineering methods allow evaluating of different systems’ reliability and the influence of external and internal factors on system reliability. These methods’ application needs the system to be investigated or objective interpretation in terms of reliability engineering. Therefore, such a system in healthcare, for the diagnosis of disease, a patient’s treatment, the influence of different factors on a patient’s condition, and others, should be presented according to the rules and demands of reliability engineering. The first step is development of the mathematical representation of the investigated system or object according to the demands of the reliability analysis. One of the often-used mathematical representations in the reliability analysis of a system is the structure function. However, this mathematical representation needs completely specified initial data. The initial data from the healthcare domain for medical error analysis is uncertain and incompletely specified. Therefore, the development of this mathematical representation needs special methods. In this paper, a new method for the mathematical representation of system development based on uncertain and incompletely specified data is proposed. The system evaluation based on the structure function allows computing of many reliability indices and measures used in reliability engineering. The approbation of this method is considered based on an example of COVID-19 patients.

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“…Otherwise, the services may fail to operate as expected leading to severe consequences, including threatening the lives of patients. To establish reliability, various efforts [10]- [12] have identified different factors (e.g., battery, memory, computational power, enhancement of QoS) that can improve the reliability of the healthcare services by using modern solutions e.g., blockchainbased technologies.…”

Section: Introductionmentioning

confidence: 99%

Reliable and Resilient AI and IoT-Based Personalised Healthcare Services: A Survey

Taimoor

Rehman

2022

IEEE Access

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Recent technological (e.g., IoT, 5G), and economic (e.g., UN 2030 Sustainable Development Goals) developments have transformed the healthcare sector towards more personalized and IoT-based healthcare services. These services are realized through control and monitoring applications that are typically developed using artificial intelligence (AI)/machine learning (ML) based algorithms, that play a significant role to highlight the efficiency of traditional healthcare systems. Current personalized healthcare services are dedicated in a specific environment to support technological personalization (e.g., personalized gadgets/devices). However, they are unable to consider different inter-related health conditions, leading to inappropriate diagnosis and affect sustainability and the long-term health/life of patients. Towards this problem, the state-of-the-art Healthcare 5.0 technology has evolved that supersede previous healthcare technologies. The goal of healthcare 5.0 is to achieve a fully autonomous healthcare service, that takes into account the interdependent effect of different health conditions of a patient. This paper conducts a comprehensive survey on personalized healthcare services. In particular, we first present an overview of key requirements of comprehensive personalized healthcare services (CPHS) in modern healthcare Internet of Things (HIoT), including the definition of personalization and an example use case scenario as a representative for modern HIoT. Second, we explored a fundamental three-layer architecture for IoTbased healthcare systems using both AI and non-AI-based approaches, considering key requirements for CPHS followed by their strengths and weaknesses in the frame of personalized healthcare services. Third, we highlighted different security threats against each layer of IoT architecture along with the possible AI and non-AI-based solutions. Finally, we propose a methodology to develop reliable, resilient, and personalized healthcare services that address the identified weaknesses of existing approaches.

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Reliability Estimation of Healthcare Systems using Fuzzy Decision Trees

Cited by 18 publications

References 27 publications

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

Reliable and Resilient AI and IoT-Based Personalised Healthcare Services: A Survey

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