Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Computational medicine, present and the future: obstetrics and gynecology perspective.

Medicine is, in its essence, decision making under uncertainty; the decisions are made about tests to be performed and treatments to be administered. Traditionally, the uncertainty in decision making was handled using expertise collected by individual providers and, more recently, systematic appraisal of research in the form of evidence-based medicine. The traditional approach has been used successfully in medicine for a very long time. However, it has substantial limitations because of the complexity of the system of the human body and healthcare. The complex systems are a network of highly coupled components intensely interacting with each other. These interactions give those systems redundancy and thus robustness to failure and, at the same time, equifinality, that is, many different causative pathways leading to the same outcome. The equifinality of the complex systems of the human body and healthcare system demand the individualization of medical care, medicine, and medical decision making. Computational models excel in modeling complex systems and, consequently, enabling individualization of medical decision making and medicine. Computational models are theory- or knowledge-based models, data-driven models, or models that combine both approaches. Data are essential, although to a different degree, for computational models to successfully represent complex systems. The individualized decision making, made possible by the computational modeling of complex systems, has the potential to revolutionize the entire spectrum of medicine from individual patient care to policymaking. This approach allows applying tests and treatments to individuals who receive a net benefit from them, for whom benefits outweigh the risk, rather than treating all individuals in a population because, on average, the population benefits. Thus, the computational modeling-enabled individualization of medical decision making has the potential to both improve health outcomes and decrease the costs of healthcare

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

Prenatal exposures and exposomics of asthma

This review examines the causal investigation of preclinical development of childhood asthma using exposomic tools. We examine the current state of knowledge regarding early-life exposure to non-biogenic indoor air pollution and the developmental modulation of the immune system. We examine how metabolomics technologies could aid not only in the biomarker identification of a particular asthma phenotype, but also the mechanisms underlying the immunopathologic process. Within such a framework, we propose alternate components of exposomic investigation of asthma in which, the exposome represents a reiterative investigative process of targeted biomarker identification, validation through computational systems biology and physical sampling of environmental medi

A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exists in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption of BNs; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review empowers researchers and clinicians with an analytical framework and findings that will enable understanding of the need to address the problems of restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice

Mobile clinical decision support systems and applications: a literature and commercial review

The final publication is available at Springer via http://dx.doi.org/10.1007/s10916-013-0004-y[EN] Background: The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. Objective: The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Methods: Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Results: 92 relevant papers and 192 commercial apps were found. 44 papers were focused only on mobile clinical decision support systems. 171 apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. Conclusions: The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.This research has been partially supported by Ministerio de Economía y Competitividad, Spain. This research has been partially supported by the ICT-248765 EU-FP7 Project. 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Construct validity of the suboptimal health status questionnaire-25 in a Ghanaian population

Background The Suboptimal Health Status Questionnaire-25 (SHS-Q-25) developed to measure Suboptimal Health Status has been used worldwide, but its construct validity has only been tested in the Chinese population. Applying Structural Equation Modelling, we investigate aspects of the construct validity of the SHS-Q-25 to determine the interactions between SHS subscales in a Ghanaian population. Methods The study involved healthy Ghanaian participants (n = 263; aged 20–80 years; 63% female), who responded to the SHSQ-25. In an exploratory factor and parallel analysis, the study extracted a new domain structure and compared to the established five-domain structure of SHSQ-25. A confirmatory factor analysis (CFA) was conducted and the fit of the model further discussed. Invariance analysis was carried out to establish the consistency of the instrument across multi-groups. Results The extracted domains were reliable with Cronbach’s ɑ of 0.846, 0.820 and 0.864 respectively, for fatigue, immune-cardiovascular and cognitive. The CFA revealed that the model fit indices were excellent (RMSEA = 0.049 \u3c 0.08, CFI = 0.903 \u3e 0.9, GFI = 0.880 \u3c 0.9, TLI = 0.907 \u3e 0.9). The fit indices for the three-domain model were statistically superior to the five-domain model. There were, however, issues of insufficient discriminant validity as some average variance extracts were smaller than the corresponding maximum shared variance. The three-domain model was invariant for all constrained aspects of the structural model across age, which is an important risk factor for most chronic diseases. Conclusion The validity tests suggest that the SHS-Q25 can measure SHS in a Ghanaian population. It can be recommended as a screening tool to early detect chronic diseases especially in developing countries where access to facilities is diminished

Temporality of Risk Factors and the Gender Differential Related to Autism Spectrum Disorder Diagnosis

Autism spectrum disorders (ASD) constitute life-long neurodevelopmental conditions. Globally, ASD risk for males remains 2 to 4 times greater than for females. Critical exposure mechanisms, their timing on ASD risk, and associations with the ASD gender differential remain elusive. The purpose of this study was to describe the relationship between preconception, pregnancy, recalled lactation practice, and infant traits, on ASD risk and the gender differential of ASD. A recently published temporal framework was adapted to study effects of maternal smoking and vitamin use, and recalled lactation practice on offspring ASD diagnosis with adjustment for preconception health and infant breathing traits. A retrospective case-control analysis using 733 child data records from U.S. autism registry characterized child gender-stratified relationships of 9 study variables. Logistic regression results showed prior maternal smoking, male gender, and maternal recollection of lactation practices were associated with offspring ASD diagnosis. Exposure factors associated with ASD did not differ by child gender or maternal vitamin use. Infant respiratory distress at birth was a covariate and collinearly related to obstetric risks. Maternal smoking was antecedent to respiratory distress and lactation practice. Study limitations included incomplete responses without repeated measures for recalled lactation practice and maternal diet variables. The implications for positive social change include a better understanding of reproductive, preconception, and prenatal risk factors of ASD. The study results have implications for reproductive health, smoking cessation programs, family planning, and prenatal care for women of reproductive age

The economics of diagnostic test: the cost-effectiveness of screening test for gestational diabetes mellitus in Scotland

Gestational diabetes mellitus (GDM) is the most common medical complication during pregnancy and is defined as carbohydrate intolerance with varying levels of severity, with the onset or first recognition occurring during pregnancy. A variety of different tests and guidelines have been used to screen for GDM over the past decade and due to this the prevalence’s for GDM that are reported in studies tend to vary considerably. However, in Scotland there has been controversy over the method for the screening and the diagnosis of GDM, reflecting the lack of consensus for the diagnosis of this condition. This thesis therefore undertakes a cost-effectiveness analysis that compares four screening test strategies that use various combinations of screening and diagnostic tests with a strategy that involves no screening. The first objective was to explore the economic approach to evaluating diagnostic testing in GDM. In consultation with experts and informed by comparable diagnostic testing models, the thesis adapted a conceptual model to the practice of GDM detection. The thesis found that the most appropriate model makes use of a combination of tests as either “negative dominant strategy” (NDS) or “positive dominant strategy” (PDS), allowing the clinician to consider test results in terms of the differences in false negative (FN) and false positive (FP) test results, as combining the tests in terms of NDS and PDS involves a tread-off between sensitivity and specificity. A key input parameter of the model was identified to be the disease prevalence. However, due to limited known evidence in Scotland, the second objective was to assess the evidence on this key parameter. A systematic review was conducted to evaluate the prevalence of GDM, considering not just screening test characteristics, but also population characteristics. The review explores the possibility that variations in over half of the studies can be explained by ethnicity and diagnostic screening strategies and whether 75g or 100g oral glucose tolerance test (OGTT) methods of testing for GDM can cause variations in the results. Mothers with risk factors are prone to testing positive which in turn leads to higher prevalence rates compared to other populations and screening ethnic groups that have a high risk of developing GDM can indeed result in high prevalence estimates, ranging from 8.5% to 12.8%. Decision making in healthcare over the past decade has increasingly been based on considerations of cost effectiveness, including national guidelines for GDM screening. The third objective was to summarize and appraise the present economic evaluation literature. Thus, a systematic review of economic evaluations, as outlined in the various cost and cost-effectiveness studies that have been published in recent years, was performed to critically appraise the current analytical methods used to measure the cost-effectiveness of screening tests in order to develop a standardised economic model. Costs associated with the screening and management of GDM vary widely by country, ranging approximately from £2.42 to £50.9 per case detection, and are dependent on the tests used, the screening approach, how the costs are calculated and the prevalence of GDM in the population. The review of the CEA studies has significant implications for future research and policy making and as such long term consequences are appropriate outcomes for the CEA of screening tests for GDM in order to capture all long term adverse health outcomes for GDM. Therefore, the economic evaluation for GDM should account for the effectiveness of postpartum screening for type 2 DM. The aforementioned conceptual model and the results of the systematic reviews of diabetes prevalence and GDM screening cost-effectiveness have been synthesised into a model to estimate the cost-effectiveness of screening for GDM based on four screening guidelines that include 1) SIGN 2001 (random plasma glucose followed by 75g OGTT) , 2) NICE 2008 (risk factors screening followed by fasting plasma glucose and 75g OGTT) , 3) Consensus 2010 (75g OGTT) and 4) SIGN 2010 (risk factors screening followed by 75g OGTT) versus 5) no screening. This probabilistic model was used to estimate and compare the costs and quality adjusted life years (QALYs) of screening tests for GDM. Three independent decision trees, for case detections, short term complications in the first year and long term complications over the lifetime, explored and considered the combinations of screening and diagnostic tests in terms of NDS and PDS. Independent decision trees would allow policy makers to focus on each part of the model separately. The primary outcomes of the analysis were the incremental cost per case identification, one year QALYs for short term complications and lifetime QALYs for type 2 diabetes mellitus for long term complications. Case identification was insufficient for policy makers because it fails to take account of the consequence of false positive and false negative results of tests. For short term complications, the incremental cost-effectiveness ratio was £46,760 per QALY for the two-step approach with SIGN 2001 (NDS) using 75g OGTT to confirm any positive random plasma glucose (RPG) before treatment compared with no screening. At willingness to pay £30,000/QALY, this strategy has 64% probability of being cost-effective for short term complications. The cost effectiveness of screening tests for GDM, to prevent short term complications, is dependent on the probability of GDM being undiagnosed. Additionally, treatments during gestation are important as they reduce additional costs that may be required to treat serious adverse complications. PDS screening where all pregnant women with one or more high risk factors are requested to undertake 75g OGTT diagnostic test, proposed by SIGN 2010, is the most cost-effective strategy in long term complications. SIGN 2010 (PDS) has higher QALY (80.9736) and is less expensive (£4,088) than the other strategies and dominates the other screening test strategies for long term complications. At a threshold of £30,000/QALY, the CEA illustrates that the probability that SIGN 2010 (PDS) will be cost-effective is approximately 55.8%. The cost effectiveness of screening tests for GDM, to prevent long term complications, is dependent on the probability of GDM being over diagnosed. If mothers have received previous diagnoses of GDM, this should trigger regular screening for type 2 DM so that it is discovered early on, before the onset of symptoms or the development of complications associated with type 2 DM. Postpartum screening and the subsequent treatment of GDM presents an important opportunity to reduce type 2 DM. This thesis provides the first economic model of a screening test using independent decision trees, split by NDS and PDS. By using NDS and PDS, decision makers can interpret the combination of test results. This better presents the consequences of false positive and false negatives and a trade-off between sensitive and specificity. The thesis finds novel value of applying this methodology to GDM screening. By using independent decision trees for NDS and PDS, the model was able to identify long term complications as the most important factors affecting the results of screening test strategies. Currently, all guidelines for GDM screening tests which also included two Scottish guidelines SIGN 2001 and SIGN 2010 are performed as NDS with regard to both screening tests and postpartum screening. Thus, in Scotland, policy makers or clinicians should consider SIGN 2010 with PDS for screening tests for GDM in order to prevent long term complications. Lastly, this work is also the first to apply the expected value of information (EVPI) to the area of GDM screening. The population EVPPI of £784,042 for long term complications shows that there is greater uncertainty with respect to long term complications and that collecting information on long term complications is likely to be worthwhile. Thus, it captures the long time horizons in screening programmes required for decisions about the value of further research and the expected payoff of conducting further research to resolve the model uncertainties