Learning Bayesian networks for clinical time series analysis

Heijden, Maarten van der; Velikova, Marina; Lucas, Peter

doi:10.1016/j.jbi.2013.12.007

Cited by 57 publications

(37 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It turns out that it is possible to predict at least part of the exacerbations a day in advance with the best model resulting in an AUC of 0.82. [20] The results from the pilot studies with respect to the usability goal revealed that the patients' impression of the system after using it is fairly positive. This early feedback from actual COPD patients is important for these kinds of systems because acceptance is often a concern.…”

Section: Home Monitoring Of Copd Patientsmentioning

confidence: 99%

14 Supporting active patient self-care

Heijden¹,

Velikova²,

Lucas³

2015

Information Technology for Patient Empowerment in Healthcare

Self Cite

View full text Add to dashboard Cite

We are currently confronted with a trend of increased pressure on health care, with associated increasing financial costs, due to an aging society and the expected increase in the prevalence of disability and chronic disease. Finding measures for cost reduction, without sacrificing quality of care, is a significant healthcare challenge. Computing technology offers promising solutions in this respect. In this article, we review contributions made by mobile computing technology in supporting the care process. In particular, we consider mobile decision support with a view to enable patient self-management , transferring part of the clinical care from healthcare professionals to the patient. Mobile computing can play an important role in giving patients an active, decisive role in managing their own disease.

show abstract

Section: Home Monitoring Of Copd Patientsmentioning

confidence: 99%

14 Supporting active patient self-care

Heijden¹,

Velikova²,

Lucas³

2015

Information Technology for Patient Empowerment in Healthcare

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on their content, the selected papers were tabled into predictive models used in CD identification (Table 1) and pathologies with model usage, along with their strengths and limitations ( Table 2). There is currently no complete AI system that can able to detect a couple of abnormalities overall through the human body [38].…”

Section: Quality Evaluationmentioning

confidence: 99%

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Battineni

Sagaro

Chinatalapudi

et al. 2020

JPM

221

View full text Add to dashboard Cite

This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.

show abstract

“…Bayesian Networks models (or Bayesian Networks, shortly BNs) are a class of multivariate statistical models that allow for the representation of probabilistic relationships among a set of variables by means of a graph [37,50,47,34]. Bayesian Networks have been extensively applied in a variety of fields, from genomic and gene expression analysis [21,54,57] to official statistics [59,51], ecology [44,28], fault diagnosis [26], healthcare [36,62] and climate data applications [16,45,64].…”

Section: Bayesian Networkmentioning

confidence: 99%

Modeling “Equitable and Sustainable Well-being” (BES) Using Bayesian Networks: A Case Study of the Italian Regions

Onori

Lasinio

2020

Soc Indic Res

View full text Add to dashboard Cite

Measurement of well-being has been a highly debated topic since the end of the last century. While some specific aspects are still open issues, a multidimensional approach as well as the construction of shared and well-rooted systems of indicators are now accepted as the main route to measure this complex phenomenon. A meaningful effort, in this direction, is that of the Italian "Equitable and Sustainable Well-being" (BES) system of indicators, developed by the Italian National Institute of Statistics (ISTAT) and the National Council for Economics and Labour (CNEL). The BES framework comprises a number of atomic indicators measured yearly at regional level and reflecting the different domains of well-being (e.g. Health, Education, Work & Life Balance, Environment,...). In this work we aim at dealing with the multidimensionality of the BES system of indicators and try to answer three main research questions: I) What is the structure of the relationships among the BES atomic indicators; II) What is the structure of the relationships among the BES domains; III) To what extent the structure of the relationships reflects the current BES theoretical framework. We address these questions by implementing Bayesian Networks (BNs), a widely accepted class of multivariate statistical models, particularly suitable for handling reasoning with uncertainty. Implementation of a BN results in a set of nodes and a set of conditional independence statements that provide an effective tool to explore associations in a system of variables. In this work, we also suggest two strategies for encoding prior knowledge in the BN estimating algorithm so that the BES theoretical framework can be represented into the network.

show abstract

Learning Bayesian networks for clinical time series analysis

Cited by 57 publications

References 27 publications

14 Supporting active patient self-care

14 Supporting active patient self-care

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Modeling “Equitable and Sustainable Well-being” (BES) Using Bayesian Networks: A Case Study of the Italian Regions

Contact Info

Product

Resources

About