An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis

Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules' evaluation process. The framework was tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitusThis work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science, ICT and Future Planning)-NRF-2017R1A2B2012337)S

Development of Image Based Model for Basic Standing Yoga Poses that Control Type-2 Diabetes

Yoga is one of the ancient practices originated in India that helps in balancing mind and body of human. For the past few decades it has got wide spread throughout the world. Many are practicing it in the presence of yoga tutor or following some online modes. But improper practice may cause major harm to muscles and ligaments of the human body. There are different asanas proposed in the Patanjali Yoga Sutra that can cure different diseases. This paper, proposes a mathematical model for a set of yoga asanas that can help cure Type -2 Diabetes. A noninvasive analysis has been implemented using Kinect Sensor and LabVIEW software to analyze the performance of the practitioner. The joints are subjected to the flexibility of the practitioner without any overstress

Clinical Decision Support Systems for Diabetes Care: Evidence and Development Between 2017 and Present

The clinical decision support systems (CDSs) for diabetes have improved significantly over the years. Multiple factors serve as driving forces for the uptake of CDSs. Newer technologies, initiatives, government mandates, and a competitive environment collectively facilitate advancement in diabetes care. This book chapter summarizes global CDSs development in recent years. Our review of the past few years’ publications on CDSs for diabetes shows that the United States is leading the world in technology development and clinical evidence generation. Developing countries worldwide are catching up in CDSs development and standards of patient care. Though most CDSs and published studies are on diabetes diagnosis, treatment, and management, a small portion of the research is devoted to prediabetes and type I diabetes. Increased efforts worldwide have been devoted to artificial intelligence and machine learning in diabetes care

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease riskThis work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2020R1A2B5B02002478). In addition, Dr. Jose M. Alonso is Ramon y Cajal Researcher (RYC-2016-19802), and its research is supported by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-B-I00, TIN2017-84796-C2-1-R, TIN2017-90773-REDT, and RED2018-102641-T) and the Galician Ministry of Education, University and Professional Training (grants ED431F 2018/02, ED431C 2018/29, ED431G/08, and ED431G2019/04), with all grants co-funded by the European Regional Development Fund (ERDF/FEDER program)S

A process model for quality in use evaluation on clinical decision support systems

Developing or purchasing software is an expensive investment and needs to be justified. Furthermore, the software must be useful in its purpose, reliable, efficient and, among other characteristics, meet the expectations of users [1, 2]. It would be no different in the case of a clinical Decision Support System - CDSS. CDSS are systems developed to support clinicians and other health professionals in a medical decision making [3]. They are developed within a clinical context, following medical guidelines, with varied purposes such as diagnoses [4, 5, 6] patient monitoring [7, 8, 9], prevention [10] and disease treatment [11, 12]. Conversely, even with all the benefits offered by a CDSS, its acceptance in the medical field is still a matter of debate [13, 14]. The CDSS acceptance is linked to the perception of the end user, such as 1) the system’s ease of use and utility, 2) the quality of its results and its reliability [14], 3) the contextual accessibility of the system, sometimes not included in the health professional’s routine and workflow, and 4) the fact that numerous CDSSs are not integrated with existing systems [15]. One manner to extend the use and disseminate positive contributions of CDSSs to the medical world is to develop them in a reliable and useful way. For this, one must follow the best practices of software engineering (SE, acronym in English) [16] and be concerned with its quality, both in the design and development process and in its effective use. Evaluating the quality of the software is to measure its characteristics and sub-characteristics of quality. In order to better structure the assessment, a series of international standards, with models and frameworks, were developed for assisting software developers in assessing the quality of software products. The latest series is the ISO/IEC 25000 - System and Software Quality Requirements and Evaluation (SQuaRE) [17]. Two of the SQuaRE divisions are addressed in this thesis: 1) Division of quality models standard (ISO/IEC 25010) [18], and 2) Quality measurement division standard (ISO/IEC 25022) [19]. The ISO/IEC 25010 are divided in product quality model and the quality model in use. Quality in use (QiU), a model of ISO/IEC 25010, is the focus of this study, through its evaluation in the context of a CDSS. The quality in use model refers to the quality of the software when executed, mentioning the result of the interaction between users and the software system/product in a specific context. This model consists of five quality characteristics: • Effectiveness - means the level of precision and completeness with which users achieve their specific goals when using the system; • Efficiency - refers to the resources spent to achieve the goals and its measure is related to the level of effectiveness achieved with the consumed resources; • Satisfaction - refers to whether user requirements are satisfied in a particular context of system use; • Freedom from risk - refers to the degree to which the quality of a system reduces or avoids potential risks to human life, the economic situation, and health of the environment; • Context coverage - deals with the use of the system in all specific contexts and/or in contexts that extend beyond the initially identified contexts. Context completeness and flexibility are the sub-characteristics that represent context coverage. Thus, when measuring the quality of a CDSS, we must consider both the context of use and the choice of the characteristic and sub-characteristic that best suits the purpose of the measurement [20]. The QiU model provides a powerful contribution to the practice of evaluating a system and determining its quality. According to Harrison et al. [21], Effectiveness, Efficiency and Satisfaction are considered the key criteria to reflect the quality of use. Therefore, these QiU characteristics meet the needs and expectations of the users of the systems, in our case of CDSSs, as they consider the user experience. As a contribution, we proposed a process model to evaluate two QiU characteristics in a CDSS: satisfaction and efficiency. We believe these characteristics are important in the evaluation of a CDSS because, due to its links with the user experience and the usability of the system, when measured, can corroborate the quality of the CDSS and mitigate the non-use and non-acceptance of this type of software. Other contributions from our work are 1) in the academic context, a significant study in the area of software quality, focusing on its characteristics, especially on the quality in use. A guideline for collecting and measuring these characteristics was built into our process model; 2) in the area of software development, professionals can make use of a simple and adaptable process, applicable to other types of systems, to measure the quality-in-use characteristics of their products.Desenvolver ou adquirir software é um investimento caro e precisa ser justificado. Além de útil, o sistema deve ser confiável, eficiente e, entre outras características, atender às expectativas dos usuários [1, 2]. Não seria diferente no caso de um sistema de apoio à decisão clínica (CDSS, acrônimo em inglês), sistemas desenvolvidos para apoiar médicos e outros profissionais de saúde na tomada de uma decisão médica [3]. CDSSs são elaborados dentro de um contexto clínico, seguindo guidelines com propósitos variados, sejam para diagnósticos [4, 5, 6], acompanhamento do paciente [7, 8, 9], na prevenção [10] e tratamento de doenças [11, 12]. No entanto, apesar de todo os benefícios oferecidos por um CDSS, sua aceitação na área médica ainda é motivo de debate [13, 14]. Essa aceitação está ligada à percepção do usuário final, como 1) a facilidade de uso e utilidade do sistema; 2) a qualidade dos resultados produzidos e sua confiabilidade [14]; 3) a acessibilidade contextual do sistema, muitas vezes não incluída na rotina e no fluxo de trabalho do profissional de saúde, e 4) o fato de muitos CDSSs não estarem integrados aos sistemas existentes [15]. Uma forma de estender o uso de CDSSs e disseminar suas contribuições positivas entre os profissionais de saúde é garantir a confiabilidade de seus resultados e a satisfação do usuáriofinal. Para tal deve-se seguir as melhores práticas da engenharia de software (SE, acrônimo em inglês) em sua concepção [16]. Isso implica em preocupar-se com a qualidade do sistema tanto no processo do projeto e desenvolvimento quanto em sua efetiva utilização. Uma forma de certificar se um software obedece a essa premissa é realizando avaliações de qualidade. Avaliar a qualidade do software é medir suas características e subcaracterísticas de qualidade. Para uma melhor estruturação desta medição foram desenvolvidos séries de padrões internacionais como guidelines de avaliação de qualidade de produtos de software. A série mais recente trata-se da ISO/IEC 25000 System and Software Quality Requirements and Evaluation (SQUARE) [17]. Dois padrões desta série foram abordadas nesta tese, sendo 1) o modelos de qualidade de software e sistemas (ISO/IEC 25010) [18], no qual trabalhamos especificamente com o modelo de qualidade em uso, e 2) o padrão de medição da qualidade em uso (ISO/IEC 25022) [19]. Qualidade em uso é o foco desta tese, através de sua avaliação no contexto de utilização de um CDSS. O Modelo de qualidade em uso trata da qualidade do software quando em execução, referindose ao resultado da interação dos usuários e o software em um cenário específico. Este modelo é composto de cinco características de qualidade: • Eficácia (ou efetividade) - esta característica representa o nível de precisão e completude com que os usuários alcançam os objetivos específicos, durante a utilização do sistema ou produto de software; • Eficiência - sua medição representa o nível de eficácia alcançada em relação aos recursos consumidos para o alcance das metas; • Satisfação - trata do quanto as necessidades do usuário são satisfeitas dentro de um determinado contexto de uso do sistema ou produto de software. Esta característica é composta pelas subcaracterísticas Utilidade, Confiança, Prazer e Conforto do usuário em relação ao sistema; • Livre de risco - trata do grau em que a qualidade de um sistema ou produto permite mitigar ou evitar riscos potenciais à vida humana, à situação econômica, à saúde ou ao meio ambiente, sendo estas suas três subcaracterísticas; • Cobertura de contexto - trata do uso do sistema em todos os contextos específicos e/ou em contextos além dos inicialmente identificados, sendo composta pelas subcaracterísticas completude de contexto e flexibilidade do sistema. Assim, para se medir a qualidade de um CDSS deve-se considerar tanto o contexto de utilização quanto a escolha da característica e subcaracterística que melhor condizem ao propósito da avaliação [20]. De acordo com Harrison et al. [21], Eficácia, Eficiência e Satisfação são considerados os principais critérios a serem avaliados para refletir a qualidade de uso. Tais características de qualidades em uso refletem o atendimento das necessidades e expectativas dos usuários dos sistemas, em especial ao usuário primário ou final, uma vez que estão diretamente relacionadas com a experiência do usuário. O modelo de qualidade em uso fornece uma contribuição poderosa para a prática de avaliar um sistema e determinar sua qualidade. Como contribuição, propusemos um modelo de processo para avaliação de qualidade em uso de um CDSS através da medição, a priori, de duas características de qualidade - satisfação e eficiência. Acreditamos que tais características são importantes na avaliação de um CDSS devido estreita relação destas com a experiência do usuário-final e a usabilidade do sistema. Assim, quando mensuradas, tais características podem corroborar com a qualidade do CDSS e mitigar a não utilização e não aceitação desse tipo de software. Nosso modelo proposto é definido por cinco (5) fases, a saber: 1) Identificação de cenário e contexto de uso do sistema, 2) seleção das medidas, métricas e métodos para mensurar as características, 3) a medição da qualidade, 4) a análise dos valores encontrados na medição e 5) a apresentação dos resultados obtidos. O resultado da aplicação do modelo de processo traduz-se em um conjunto de informações que nortearão um melhoramento do software, caso a medição das características fique abaixo de um padrão pré-definido pelos atores envolvidos no processo de medição do sistema. Por outro lado, se a medição for positiva, isso vem ratificar a qualidade do sistema e ações poderão ser tomadas para disseminar esse bom resultado, buscando a adesão de mais utilizadores. Como forma de validação do modelo proposto, após sua utilização para identificação de cenários e contexto-de-uso possíveis de serem mensurados, foi apresentado um CDSS da área oncológica a profissionais de saúde, estudantes de medicina e profissionais da área de qualidade de software que, ao final de sua utilização, responderam a um inquérito com o objetivo de avaliar o sistema. A aplicação se deu de forma online, dado a necessidade de mantermos o distanciamento social e o de cumprirmos as orientações sanitárias. As respostas serviram como fonte de dados para a medição das características de qualidadeem- uso do sistema. Os resultados da aplicação revelou que nosso modelo de processo de avaliação é válido, relevante e de fácil utilização para identificar as características importantes em um sistema, bem como suas medições por meio das funções matemáticas do modelo ISO/IEC 25022. Outras contribuições do nosso trabalho, temos 1) no âmbito acadêmico, um estudo significativo na área de qualidade de software, com foco em suas características, especialmente na qualidade em uso. Uma guideline para a coleta e mensuração dessas características foi construída em nosso modelo de processo; 2) na área de desenvolvimento de software, os profissionais podem contar com um processo simples e adaptável, aplicável a outros tipos de sistema, para mensuração da qualidade em uso de seus produtos.The research has been partially funded by the FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 and Operação Centro 01-0145-FEDER-000019 – C4 – Centro de Competências em Cloud Computing, co-financed by the Programa Operacional Regional do Centro (CENTRO 2020), through the Sistema de Apoio à Investigação Científica e Tecnológica – Programas Integrados de IC&DT. I would also like to acknowledge the contribution of the COST Action IC1303: AAPELE—Archi- tectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226; SHELD-ON—Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology)