Purpose: This review aims to summarize and evaluate the most accurate machine-learning algorithm used to predict ischemic heart disease. Methods: This systematic review was performed following PRISMA guidelines. A comprehensive search was carried out using multiple databases such as Science Direct, PubMed\ MEDLINE, CINAHL, and IEEE explore. Results: Thirteen articles published between 2017 to 2021 were eligible for inclusion. Three themes were extracted: the commonly used algorithm to predict ischemic heart disease, the accuracy of algorithms to predict ischemic heart disease, and the clinical outcomes to improve the quality of care. All methods have utilized supervised and unsupervised machine-learning. Conclusion: Applying machine-learning is expected to assist clinicians in interpreting patients’ data and implementing optimal algorithms for their datasets. Furthermore, machine-learning can build evidence-based that support health care providers to manage individual situations who need invasive procedures such as catheterizations.
Purpose This study aims to assess the quality of life (QOL) and the related factors in patients post-stroke in Jordan. Design/methodology/approach Prospective, the cross-sectional study recruited 100 participants with stroke from three public hospitals from December 1, 2021 to February 1, 2022. Patients with stroke were interviewed to fill the stroke-specific quality of life questionnaire. Findings Forty-five per cent of the participants were male. More than half of the participants (53%) were married, and the average age of the participants was 63.6 (SD =3.8). Most of the participants had an ischemic stroke (86%) with an affected left side (65%). The overall QOL of the participants was leveling at (M = 123.5, SD = 45.2), which is a moderate level. It was found statistical significance differences among participants according to gender, type of stroke, affected side and presence of comorbidities (Table 1). Research limitations/implications There were some limitations in this study. First, this study was based on mild to moderate Jordanian stroke survivors and did not include critically ill stroke survivors; the QOL critically ill stroke survivors may differ, which could affect the generalizability of data among all stroke survivors. Second, this study is prospective, and this type of study is prone to bias that could influence the reliability of the results. It is recommended to conduct a mixed-method study to reveal an in-depth understanding of the associated factors with QOL, to ensure reliability and to reflect a better view of the Jordanian population. Practical implications To sum up, there is a reduction in the level of QOL among stroke survivors; hence, it is crucial to focus on detecting factors contributing to reducing the QOL and taking individual differences between sexes, type and location of the stroke, and comorbidities into consideration to develop a treatment plan that enhances the QOL and well-being for survivors of stroke. Social implications Taking individual differences between sexes, type and location of the stroke and comorbidities into consideration to develop a treatment plan that enhances the QOL and well-being of survivors of stroke. Originality/value The findings of this study bring a strong insight toward assessing the main factors indicating a decrease QOL among stroke survivors.
Background: Diabetes is an endocrine chronic condition with a high prevalence rate among the population that needs a complex management process. However, many advanced health care technologies were evolving to help patients to achieve their centered care and self-management using real-time proactive techniques through interactive systems to detect early complications and prevent them. the purpose of the current review is to assess the findings of literature reviews of the main interventions that used a real-time partially automated interactive systems to interpret patient’s data including biological information, exercise, and dietary content calculated from a message sent by the patient and respond with actionable findings, helping patients to achieve diabetes self-management. Methods: PubMed\ MEDLINE, CINAHL, Google Scholar, and Research Gate were used to search the literature for studies published between the periods 2015 to 2021. Results: Eleven articles were included in the literature review. The retrieved studies approved the significant effect of achieving diabetic self-management by utilizing Information Technology [IT] with the Natural Language Processing [NLP] methods by sending a real-time, partially automated interactive system to interpret patient's biological information, physical activity, and dietary content calculated using a message sent by patients to achieve their self-management. Conclusion: Improved blood glucose levels, glycemic control, better readings of blood pressure, and lifestyle improvement including dietary intake and physical activity were offered using continuous real-time massages to improve their health outcomes.
Objectives Big data has revolutionized nursing and health care and raised concerns. This research aims to help nurses understand big data sets to provide better patient care. Methods This study used big data in nursing to improve patient care. Big data in nursing has sparked a global revolution and raised concerns, but few studies have focused on helping nurses understand big data to provide the best patient care. This systematic review was conducted based on PRISMA guidelines. PubMed, MEDLINE, CINAHL, Google Scholar, and ResearchGate were used for 2010–2020 studies. Results The most common use of big data in nursing was investigated in eight papers between 2015 and 2018. All research showed improvements in patient outcomes and healthcare delivery when big data was used in the medical-surgical, emergency department, critical care unit, community, systems biology, and leadership applications. Big data is not taught to nurses. Conclusions Big data applications in nursing and health care improve early intervention and decision-making. Big data provides a comprehensive view of a patient’s status and social determinants of health, allowing treatment using all metaparadigms and avoiding a singular focus. Big data can help prepare nurses and improve patient outcomes by improving quality, safety, and outcomes.
Background This study aims to assess healthy lifestyle behaviors among undergraduate students and determine the association between electronic health literacy with lifestyle behavior among undergraduate Jordanian university students. Methods A descriptive cross-sectional design was used. The study recruited 404 participants utilizing undergraduate students from public and private universities. The e-Health literacy scale was used to assess the level of health information literacy among university students. Results Data were collected from 404 participants who reported very good health status, the majority of the participants were female 57.2% with an average age of 19.3 years. The results showed that participants had good health behavior in terms of exercise, taking breakfast, smoking status, and sleeping status. The results have shown an inadequate level of e-Health literacy 16.61 (SD = 4.10) out of 40. The vast majority of students, in terms of their attitudes toward the Internet, thought that Internet health information was very useful/useful (95.8%). Also, they thought that online health information was very important /important (97.3%). The results showed that students who were attending public universities had higher e-Health literacy scores rather than those who were attending private universities, t (402) = 1.81, p = .014. The mean e-Health literacy score for nonmedical students was higher than those for medical students ( p = .022). Conclusion The study's findings provide important insights into the health behaviors and electronic health literacy of undergraduate students in Jordanian universities, and offer valuable guidance for future health education programs and policies aimed at promoting healthy lifestyles in this population.
Purpose This study aims to review the lived experience of patients suffering from stroke and describe their perception of palliative care needs. Design/methodology/approach A literature review search was conducted. Web of Sciences, SAGE, CINAHL, PubMed and Jordanian Database for Nursing Research databases were used to search the literature. Findings The findings of 37 articles were address palliative care approaches for patients with stroke, lived experiences of patients suffering from stroke and the experience, barriers and facilitators related to health-care service for stroke survivors. Originality/value This review indicated the importance of recognizing palliative care needs among patients suffering from stroke to improve post-stroke recovery. This study recommends further research, especially in low- and middle-income countries, to understand patients’ experiences and recognize the main palliative care needs that can be incorporated into interventions designed to improve the quality of life among them.
Background Many current studies have claimed that the actual risk of heart disease among women is equal to that in men. Using a large machine learning algorithm (MLA) data set to predict mortality in women, data mining techniques have been used to identify significant aspects of variables that help in identifying the primary causes of mortality within this target category of the population. Objective This study aims to predict mortality caused by heart disease among women, using an artificial intelligence technique–based MLA. Methods A retrospective design was used to retrieve big data from the electronic health records of 2028 women with heart disease. Data were collected for Jordanian women who were admitted to public health hospitals from 2015 to the end of 2021. We checked the extracted data for noise, consistency issues, and missing values. After categorizing, organizing, and cleaning the extracted data, the redundant data were eliminated. Results Out of 9 artificial intelligence models, the Chi-squared Automatic Interaction Detection model had the highest accuracy (93.25%) and area under the curve (0.825) among the build models. The participants were 62.6 (SD 15.4) years old on average. Angina pectoris was the most frequent diagnosis in the women's extracted files (n=1,264,000, 62.3%), followed by congestive heart failure (n=764,000, 37.7%). Age, systolic blood pressure readings with a cutoff value of >187 mm Hg, medical diagnosis (women diagnosed with congestive heart failure were at a higher risk of death [n=31, 16.58%]), pulse pressure with a cutoff value of 98 mm Hg, and oxygen saturation (measured using pulse oximetry) with a cutoff value of 93% were the main predictors for death among women. Conclusions To predict the outcomes in this study, we used big data that were extracted from the clinical variables from the electronic health records. The Chi-squared Automatic Interaction Detection model—an MLA—confirmed the precise identification of the key predictors of cardiovascular mortality among women and can be used as a practical tool for clinical prediction.
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