The attributes of hospital-based coronary artery diseases registries with a focus on key registry processes: A systematic review

Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models

BACKGROUND

The incidence and mortality rates of cardiovascular disease worldwide are a major concern in the healthcare industry. Precise prediction of cardiovascular disease is essential, and the use of machine learning and deep learning can aid in decision-making and enhance predictive abilities.

OBJECTIVE

The goal of this paper is to introduce a model for precise cardiovascular disease prediction by combining machine learning and deep learning.

METHOD

Two public heart disease classification datasets with 70,000 and 1190 records besides a locally collected dataset with 600 records were used in our experiments. Then, a model which makes use of both machine learning and deep learning was proposed in this paper. The proposed model employed CNN and LSTM, as the representatives of deep learning models, besides KNN and XGB, as the representatives of machine learning models. As each classifier defined the output classes, majority voting was then used as an ensemble learner to predict the final output class.

RESULT

The proposed model obtained the highest classification performance based on all evaluation metrics on all datasets, demonstrating its suitability and reliability in forecasting the probability of cardiovascular disease.

A Systematic Review of Health Management Mobile Applications in COVID-19 Pandemic: Features, Advantages, and Disadvantages

Introduction

With the increasing accessibility of smartphones, their use has been considered in healthcare services. Mobile applications have played a pivotal role in providing health services during COVID-19. This study is aimed at identifying the features, advantages, and disadvantages of health management mobile applications during COVID-19.

Methods

This systematic review was conducted in PubMed, Scopus, and Web of Science using the related keywords up to November 2021. The original articles in English about the health management mobile applications in COVID-19 were selected. The study selection was done by two researchers independently according to inclusion and exclusion criteria. Data extraction was done using a data extraction form, and the results were summarized and reported in related tables and figures.

Results

Finally, 12 articles were included based on the criteria. The benefits of mobile health applications for health management during COVID-19 were in four themes and 19 subthemes, and the most advantages of the application were in disease management and the possibility of recording information by users, digital tracking of calls, and data confidentiality. Furthermore, the disadvantages of them have been presented in two themes and 14 subthemes. The most common disadvantages are reduced adherence to daily symptom reports, personal interpretation of questions, and result bias.

Conclusion

The study results showed that mobile applications have been effective in controlling the prevalence of COVID-19 by identifying virus-infested environments, identifying and monitoring infected people, controlling social distancing, and maintaining quarantine. It is suggested that usability, ethical and security considerations, protection of personal information, and privacy of users be considered in application design and development.

Design and Development of a Hospital-Based Coronary Artery Disease (CAD) Registry in Iran

Background

The incidence of coronary artery disease (CAD), the leading cause of mortality in most developed and developing countries, is increasing. The adoption of hospital registries can improve care delivery and facilitate the management of CAD through better planning, as well as help with outcome assessment through more effective data management.

Objectives

The present study is aimed at designing a hospital-based CAD registry for managing CAD data.

Methods

This developmental study was conducted in three phases. Initially, sources related to CAD registries were reviewed, the results of which were published in two studies. In the next phase, the prerequisites and requisites of the software were determined through a qualitative study. In this phase, the registry dataset was determined by using a questionnaire. Finally, the developed conceptual model of the software was validated. The software was then developed based on the validated conceptual model.

Results

The registry data elements were classified into 13 main categories, including identification data, medical history, and risk factors. The dataset included 171 data elements, including data related to surgical and nonsurgical procedures. The conceptual model was approved by field experts, and the software was developed accordingly.

Conclusion

The steps followed in the present study for developing the CAD registry can be used as an appropriate approach for designing similar hospital-based registries. Considering the pivotal role of the registry in the management of CAD, the routine and systemic use of the registry is suggested in all healthcare centers.

Minimum Data Set for a Poisoning Registry: A Systematic Review

Poisoning, as a well-known medical condition, puts everyone at risk. As a data management tool, a registry plays an important role in monitoring the poisoned patients. Having a poisoning minimum data set is a major requirement for creating a poisoning registry. Therefore, the present systematic review was conducted in 2019 to identify the minimum data set for a poisoning registry. Searches were performed in four scientific databases, i.e., PubMed, Scopus, Web of Science, and Embase. The keywords used in the searches included minimum data set, "poison", and "registry". Two researchers independently evaluated the titles, abstracts, and texts of the papers. The data were collected from the related papers. Ultimately, the minimum data set was identified for the poisoning registry. Data elements extracted from the sources were classified into two general categories: administrative data and clinical data. Ninety-eight data elements in the administrative data category were subdivided into three sections: general data, admission data, and discharge data. One-hundred and thirty-one data elements in the clinical data category were subdivided into five sections: clinical observation data, clinical assessment data, past medical history data, diagnosis data, and treatment plan data. The minimum data set is a prerequisite for creating and using a poisoning registry and data system. It is suggested to evaluate and use the poisoning minimum data set in accordance with the national laws, needs, and standards based on the opinion of the local experts.