Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

Simões, Priscyla Waleska; Silva, Geraldo Doneda da; Moretti, G; Simon, Carla Sasso; Winnikow, Erik Paul; Nassar, Sílvia Modesto; Medeiros, Lídia Rosi; Rosa, Maria Inês da

doi:10.1590/0102-311x00205213

Cited by 5 publications

(5 citation statements)

References 34 publications

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“…NB has many restrictions in comparison with BN methods, despite its merits. Zheng et al (1999), Sajda (2006), Ameri et al (2008), Sadeghi (2012), Kowsalya et al (2015), Shelke (2015), Simoes et al (2015), Yamaguchi et al (2015) and Bazazeh et al (2016) designed the BN with feature analyzing and other improvement methods for breast cancer datasets and found its results better than other methods. Alzheimer's disease was experienced in the papers of Sun et al (2007) and Pinheiro et al (2008) with the same results.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient Bayesian network for differential diagnosis using experts' knowledge

Ershadi

Seifi

2020

IJICC

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PurposeThis study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods based on data, experts’ knowledge and both are considered in some cases. Besides, feature reduction and some clustering methods are used to improve their performance.Design/methodology/approachFirst, the performances of classification methods are evaluated for differential diagnosis of different diseases. Then, experts' knowledge is utilized to modify the Bayesian networks' structures. Analyses of the results show that using experts' knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification. A total of ten different diseases are used for testing, taken from the Machine Learning Repository datasets of the University of California at Irvine (UCI).FindingsThe proposed method improves both the computation time and accuracy of the classification methods used in this paper. Bayesian networks based on experts' knowledge achieve a maximum average accuracy of 87 percent, with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods.Practical implicationsThe proposed methodology can be applied to perform disease differential diagnosis analysis.Originality/valueThis study presents the usefulness of experts' knowledge in the diagnosis while proposing an adopted improvement method for classifications. Besides, the Bayesian network based on experts' knowledge is useful for different diseases neglected by previous papers.

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Section: Literature Reviewmentioning

confidence: 99%

An efficient Bayesian network for differential diagnosis using experts' knowledge

Ershadi

Seifi

2020

IJICC

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“…Moreover the recent representation of context is moving toward ontology due to its efficiency with data complexity and heterogeneity of sources. Despite its advantages, the ontology cannot handle the uncertainty and evaluate the current state [ 8 ].…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, there is no treatment for COPD, or rather there is no cure to reverse the damage done to the airways and lung function, but therapy can slow its progress, reduce complications, improve quality of life [ 3 ] and avoid exacerbation that is the main reason leading to rapidly worsening of health conditions [ 4 , 5 ]. Here we will focus on the exacerbation, which does not even have a clear definition because the true relationship between risk factors and the development of exacerbations are not fully understood [ 6 ]; each one has different signs and symptoms [ 7 ], even biomarkers cannot be relied upon to distinguish between COPD cohort at stable state and at exacerbation [ 8 ]. However, generally, exacerbation defined as involves impaired lung function, acute event, or sudden worsening of COPD symptoms likely to cause death [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, because the fluctuation and the diversity of exacerbation symptoms, the predicting of frequent exacerbations is needed to plug the uncertainty gap where logical processing (If-Else) does not work, and to select the relevant symptoms or attributes. In this context, Bayesian network has proven its efficiency to handle uncertainty in intelligent environments, more particularly those involving medical applications [ 8 ]. Bayesian network has a great reputation; it is used in many sensitive applications, like detecting brain tumors [ 9 ], predicting the risk of death among patients waiting for heart surgery [ 10 ], and identifying exacerbations of asthma patients [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network

Mcheick

Saleh

Ajami

et al. 2017

Sensors

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In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD) have been published. However, how to derive relevant attributes and early detection of COPD exacerbations remains a challenge. In this research work, we will use an efficient algorithm to select relevant attributes where there is no proper approach in this domain. Such algorithm predicts exacerbations with high accuracy by adding discretization process, and organizes the pertinent attributes in priority order based on their impact to facilitate the emergency medical treatment. In this paper, we propose an extension of our existing Helper Context-Aware Engine System (HCES) for COPD. This project uses Bayesian network algorithm to depict the dependency between the COPD symptoms (attributes) in order to overcome the insufficiency and the independency hypothesis of naïve Bayesian. In addition, the dependency in Bayesian network is realized using TAN algorithm rather than consulting pneumologists. All these combined algorithms (discretization, selection, dependency, and the ordering of the relevant attributes) constitute an effective prediction model, comparing to effective ones. Moreover, an investigation and comparison of different scenarios of these algorithms are also done to verify which sequence of steps of prediction model gives more accurate results. Finally, we designed and validated a computer-aided support application to integrate different steps of this model. The findings of our system HCES has shown promising results using Area Under Receiver Operating Characteristic (AUC = 81.5%).

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“…Assim, torna-se possível transferir o conhecimento capturado pelos modelos para especialistas do domínio. Modelos de ADs têm sido, portanto, concebidos para realização de tarefas computacionais de aplicabilidade crítica, como diagnósticos médicos [36].…”

Section: Como Resultado Modelos Inspirados Em Cérebros Biológicos Den...unclassified

Uma abordagem interpretável de aprendizado de máquina para avaliação retrospectiva do impacto da perda de sangue derivada de flebotomia em pacientes internados

Roza¹

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In medicine, the procedure of drawing blood from a patient for laboratory tests is called phlebotomy. Inpatients are submitted to phlebotomy protocols that include successive requests for tests, at rates that can reach daily extractions, in order to diagnose or monitor the evolution of their clinical conditions. However, frequent extractions expose the patient to high blood loss in a short period of time, which can trigger health problems, since the time between extractions can be insufficient for the adequate replacement of lost blood components. In order to evaluate the hypothesis of phlebotomy blood loss to be a relevant factor for the development or worsening of anemia and for an increase in hospital length of stay, this research conducted analyzes guided by retrospective clinical, laboratory and demographic data of 28,312 hospitalizations that occurred in the Hospital of Clinics at Unicamp (HC/Unicamp) between the years 2012 and 2016. In an initial phase, exploratory investigations were conducted in order to capture the main characteristics inherent to the dataset. Then, an interpretable Machine Learning (ML) approach was developed, using the Decision Tree-based techniques Gradient Boosting Machines and Random Forests for regression, binary classification and multiclass classification tasks. In order for the computational solutions produced to offer a degree of interpretability that is convenient for use in the medical-hospital setting, methods to extract interpretability of ML models were used to express the relationship between the different variables used, as well as listing the degree of their contributions to the models outputs. The results suggest a relationship between the amount of blood samples taken during hospitalization and length of stay, as well as the decrease in patients hemoglobin levels, a factor directly related to the development of anemia. The study conducted with unpublished data, to the best of our knowledge, is the first to jointly investigate relationships between phlebotomy, anemia and length of stay using ML. In addition, it deepens on existing knowledge about the problem and expands the correlated literature, since it takes into account the variables adjacent to the possibility of developing anemia in the course of hospitalization, such as blood transfusions and surgeries performed. It also offers HC/Unicamp subsidies for evaluating its phlebotomy processes and grounds for decision making.

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Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

Cited by 5 publications

References 34 publications

An efficient Bayesian network for differential diagnosis using experts' knowledge

An efficient Bayesian network for differential diagnosis using experts' knowledge

Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network

Uma abordagem interpretável de aprendizado de máquina para avaliação retrospectiva do impacto da perda de sangue derivada de flebotomia em pacientes internados

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