Mapping risk of ischemic heart disease using machine learning in a Brazilian state

Bergamini, Marcela; Iora, Pedro Henrique; Rocha, Thiago Augusto Hernandes; Tchuisseu, Yolande Pokam; Dutra, Amanda de Carvalho; Scheidt, João Felipe Hermann Costa; Nihei, Oscar Kenji; Carvalho, Maria Dalva de Barros; Staton, Catherine A.; Vissoci, João Ricardo Nickenig; Andrade, Luciano de

doi:10.1371/journal.pone.0243558

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…In the first step, the capacity of each center is determined, while the second step consists of summing up the capacities within the buffer taking into account the overlapped health service area. Thus, the accessibility is the available amount of cardiologists and referral centers chemical and mechanical per local population added up within 60 km of each health service [25,26,27]. The result is an accessibility index to cardiologists, chemical reperfusion referral centers and mechanical reperfusion centers for each municipality.…”

Section: Accessibility To Cardiologists' Office and Referral Centers mentioning

confidence: 99%

The Impact of Socioeconomic Factors, Coverage and Access to Health on Heart Ischemic Disease Mortality in a Brazilian Southern State: A Geospatial Analysis

et al. 2021

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Background: No other disease has killed more than ischemic heart disease (IHD) for the past few years globally. Despite the advances in cardiology, the response time for starting treatment still leads patients to death because of the lack of healthcare coverage and access to referral centers. Objectives: To analyze the spatial disparities related to IHD mortality in the Parana state, Brazil. Methods: An ecological study using secondary data from Brazilian Health Informatics Department between 2013-2017 was performed to verify the IHD mortality. An spatial analysis was performed using the Global Moran and Local Indicators of Spatial Association (LISA) to verify the spatial dependency of IHD mortality. Lastly, multivariate spatial regression models were also developed using Ordinary Least Squares and Geographically Weighted Regression (GWR) to identify socioeconomic indicators (aging, income, and illiteracy rates), exam coverage (catheterization, angioplasty, and revascularization rates), and access to health (access index to cardiologists and chemical reperfusion centers) significantly correlated with IHD mortality. The chosen model was based on p < 0.05, highest adjusted R² and lowest Akaike Information Criterion. Results: A total of 22,920 individuals died from IHD between 2013-2017. The spatial analysis confirmed a positive spatial autocorrelation global between IDH mortality rates (Moran's I: 0.633, p < 0.01). The LISA analysis identified six high-high pattern clusters composed by 66 municipalities (16.5%). GWR presented the best model (Adjusted R²: 0.72) showing that accessibility to cardiologists and chemical reperfusion centers, and revascularization and angioplasty rates differentially affect the IHD mortality rates geographically. Aging and illiteracy rate presented positive correlation with IHD mortality rate, while income ratio presented negative correlation (p < 0.05). Conclusion: Regions of vulnerability were unveiled by the spatial analysis where sociodemographic, exam coverage and accessibility to health variables impacted differently the IHD mortality rates in Paraná state, Brazil. Dutra et al: The Impact of Socioeconomic Factors, Coverage and Access to Health on Heart Ischemic Disease Mortality in a Brazilian Southern State Art. 5, page 2 of 15 Highlights • The increase in ischemic heart disease mortality rates is related to geographical disparities. • The IHD mortality is differentially associated to socioeconomic factors, exam coverage, and access to health. • Higher accessibility to chemical reperfusion centers did not necessarily improve patient outcomes in some regions of the state. • Clusters of high mortality rate are placed in regions with low amount of cardiologists, income and schooling.

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Section: Accessibility To Cardiologists' Office and Referral Centers mentioning

confidence: 99%

The Impact of Socioeconomic Factors, Coverage and Access to Health on Heart Ischemic Disease Mortality in a Brazilian Southern State: A Geospatial Analysis

et al. 2021

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“…The cross-validations were performed 100 times. The test and train datasets were divided in 10 parts/each (for each of 10 validations, nine parts were used to train and one for internal validation) and this process was repeated 10 times, according to cross-validation methodology described by Bergamini et al [ 46 ], adapted by the authors.…”

Section: Methodsmentioning

confidence: 99%

Applicability of machine learning technique in the screening of patients with mild traumatic brain injury

Terabe,

Massago,

Iora

et al. 2023

PLoS ONE

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Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional University Hospital of Maringá, Paraná state, Brazil. This is an observational, descriptive, cross-sectional, and retrospective study using ML technique to develop a protocol that predicts which patients with an initial diagnosis of mild TBI should be recommended for a head CT. Among the tested models, he linear extreme gradient boosting was the best algorithm, with the highest sensitivity (0.70 ± 0.06). Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.

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“…MLAs and other large-scale data analytical techniques must take place as an effective and factual method to predict heart diseases such as CVD, ischemic heart disease (IHD), and CHF [12]. MLAs that can create a variety of prediction models to accurately assess the presence or absence of risk factors that contribute to the development of CVD can be used to manage these enormous data sets, also known as big data or large-scale data [13,14]. Based on various populations and subgroups, several prediction models were created using the well-established risk factors and antecedents of CVD [9].…”

Section: Introductionmentioning

confidence: 99%

Prediction of mortality in young adults with cardiovascular disease using artificial intelligence

Ahmad,

Bani Hani,

Abu-Abeeleh

et al. 2024

ELECTRON J GEN MED

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Background: Young mortality is prevalent among patients with cardiovascular disease (CVD). To develop prediction models for CVD mortality in young adults, it is crucial to assess CVD risks. Early detection of cardiac disorders using machine learning algorithms, a branch of artificial intelligence (AI) is crucial for preventing more damage to coronary arteries and saving lives. Aims: To predict mortality versus a life outcome among young adults (18-45 years) with CVD using AI technique known as Chi-squared automatic interaction detector (CHAID) algorithms. Methods: A large-scale dataset was extracted from the electronic health records of 809 young adult patients diagnosed with CVD using a retrospective design. Information was retrieved regarding young adults from Jordan who were admitted to public health institutions between 2015 and the end of 2021. Results: CHAID algorithms were chosen among seven prediction models based on accuracy and area under curve to predict mortality vs life in young individuals (18-45 years old) with CVD. The mortality prediction algorithms started with pulse pressure, then diastolic blood pressure, then ischemic heart disease, and last geographical location. Conclusions: CHAID model used in our study indicated how the death rate was classified and distributed among a variety of parameters. As a result, we may argue that AI model could provide additional information on how many aspects are articulated in connection to CVD patient fatality situations.

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Mapping risk of ischemic heart disease using machine learning in a Brazilian state

Cited by 8 publications

References 31 publications

The Impact of Socioeconomic Factors, Coverage and Access to Health on Heart Ischemic Disease Mortality in a Brazilian Southern State: A Geospatial Analysis

The Impact of Socioeconomic Factors, Coverage and Access to Health on Heart Ischemic Disease Mortality in a Brazilian Southern State: A Geospatial Analysis

Applicability of machine learning technique in the screening of patients with mild traumatic brain injury

Prediction of mortality in young adults with cardiovascular disease using artificial intelligence

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