Electronic health record with computerized decision support tools for the purposes of a pediatric cardiovascular heart disease screening program in Crete

Chatzakis, Ilias; Vassilakis, Kostas; Lionis, Christos; Germanakis, Ioannis

doi:10.1016/j.cmpb.2018.03.009

Cited by 12 publications

(18 citation statements)

References 31 publications

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“…After 2 years ( n = 31,579 screened), greater proportions of 10–17-year-olds with a third incident hypertensive BP received a hypertension diagnosis (55% CDS, 21% control; P ≤ 0.001) [ 23 ]; however, follow-up to remeasure BP within 30 days did not differ between groups (14% CDS, 11% control). The Crete CV Screening tool, developed in Greece, provided EHR-integrated support for detecting high BP and CV disease risk factors in children [ 25 ]. The tool alerted staff to repeat an elevated BP measure, computed BP percentiles, and used a risk algorithm to support cardiologists in identifying a child needing cardiac work-up.…”

Section: Resultsmentioning

confidence: 99%

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Clinical Decision Support for the Diagnosis and Management of Adult and Pediatric Hypertension

Vuppala

Turer²

2020

Curr Hypertens Rep

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Purpose of Review To review literature from 2016 to 2019 on clinical decision support (CDS) for diagnosis and management of hypertension in children and adults. Recent Findings Ten studies described hypertension CDS systems. Novel advances included the integration of patient-collected blood pressure data, automated information retrieval and management support, and use of CDS in low-resource/developing-world settings and in pediatrics. Findings suggest that CDS increases hypertension detection/control, yet many children and adults with hypertension remain undetected or undercontrolled. CDS challenges included poor usability (from lack of health record integration, excessive data entry requests, and wireless connectivity challenges) and lack of clinician trust in blood pressure measures. Summary Hypertension CDS has improved but not closed gaps in the detection and control of hypertension in children and adults. The studies reviewed indicate that the usability of CDS and the system where CDS is deployed (e.g., commitment to highquality blood pressure measurement/infrastructure) may impact CDS's ability to increase hypertension detection and control.

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Section: Resultsmentioning

confidence: 99%

“…The Crete CV Screening tool, developed in Greece, provided EHR-integrated support for detecting high BP and CV disease risk factors in children [ 25 ]. The tool alerted staff to repeat an elevated BP measure, computed BP percentiles, and used a risk algorithm to support cardiologists in identifying a child needing cardiac work-up.…”

Section: Resultsmentioning

confidence: 99%

Clinical Decision Support for the Diagnosis and Management of Adult and Pediatric Hypertension

Vuppala

Turer²

2020

Curr Hypertens Rep

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“…Artificial intelligence solutions have been suggested to analyze and classify the EHR data for heart disease prediction [36], by designing standard classification models such as support vector machine (SVM), a priori algorithm, decision trees, and hybrid random forest model [37,38]. Heart failure prediction has been modeled by relying on machine learning techniques applied to EHR data and reached a high AUC score of 77% using Logistic regression with model selection based on Bayesian information criterion [39].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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“…However, a new area of research is emerging with great potential and, therefore, part of the research potential begins to be migrate to the healthcare services, as is evidenced by the articles of Rais et al (2018) or Ferreira et al (2020), which is bringing Portugal closer to Sweden. In Greece, the research interest is not much different from the Portuguese ones, particularly on the incidence in healthcare services (Chatzakis et al 2018;Grekousis and Liu 2019) and water management services (Alamanos et al 2018;Athanasiou et al 2018); however, we note a strong investment in renewable energy-of the 25 articles published in 2018, 10 of which are related to clean energy (Polemis and Spais 2020;Vlachokostas et al 2020).…”

Section: Quantitative Approachmentioning

confidence: 82%

“…Although we claim that the focus of AI is increasingly focused on the area of health services, it is not surprising that this research is also related to engineering and computer science, since the latter provide the necessary tools for the prevention, diagnosis and disease treatment. An example of this is the research of Chatzakis et al (2018), who had the objective to describe the development of an Electronic Health Record (EHR), with integrated computerized decision support system for the diagnosis of pediatric cardiovascular diseases.…”

Section: Quantitative Approachmentioning

confidence: 99%

Artificial Intelligence Research and Its Contributions to the European Union’s Political Governance: Comparative Study between Member States

2020

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In the last six decades, many advances have been made in the field of artificial intelligence (AI). Bearing in mind that AI technologies are influencing societies and political systems differently, it can be useful to understand what are the common issues between similar states in the European Union and how these political systems can collaborate with each other, seeking synergies, finding opportunities and saving costs. Therefore, we carried out an exploratory research among similar states of the European Union, in terms of scientific research in areas of AI technologies, namely: Portugal, Greece, Austria, Belgium and Sweden. A key finding of this research is that intelligent decision support systems (IDSS) are essential for the political decision-making process, since politics normally deals with complex and multifaceted decisions, which involve trade-offs between different stakeholders. As public health is becoming increasingly relevant in the field of the European Union, the IDSSs can provide relevant contributions, as it may allow sharing critical information and assist in the political decision-making process, especially in response to crisis situations.

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Electronic health record with computerized decision support tools for the purposes of a pediatric cardiovascular heart disease screening program in Crete

Cited by 12 publications

References 31 publications

Clinical Decision Support for the Diagnosis and Management of Adult and Pediatric Hypertension

Clinical Decision Support for the Diagnosis and Management of Adult and Pediatric Hypertension

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Artificial Intelligence Research and Its Contributions to the European Union’s Political Governance: Comparative Study between Member States

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