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Your search keyword '"Bani Hani, Salam"' showing total 24 results
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24 results on '"Bani Hani, Salam"'

9. Cardiovascular disease-related mortality in young adults: Artificial Intelligence prediction Background: Many Cardiovascular disease (CVD) patients die young. Assessing CVD risks is essential to creating prediction models that accurately predict CVD mortality overall and in subgroups like young adults. Machine Learning Algorithms (MLA), a subfield of Artificial Intelligence (AI), must be used to detect heart diseases early to prevent further coronary artery damage and save lives. Objectives: To predict death versus a life outcome among young adults (18-45 years) with CVD using the Chi-squared Automatic Interaction Detector (CHAID) algorithms, an artificial intelligence (AI) technique. Methods: A retrospective design for retrieving larg (Preprint)

Author

Ahmad, Muayyad, primary and Bani Hani, Salam, additional

Published
2024
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23. Preoperative anxiety, postoperative pain tolerance and analgesia consumption: A prospective cohort study

Author

Qaddumi, Jamal, Arda, Ali Mohammad, Alkhawaldeh, Abdullah, ALBashtawy, Mohammed, Abdalrahim, Asem, ALBashtawy, Sa’d, Al Omari, Omar, Bashtawi, Mahmoud, Masa’deh, Rami, ALBashtawy, Zaid, Mohammad, Khitam Ibrahem, ALBashtawy, Bayan, Aljezawi, Ma’en, Khatatbeh, Haitham, Ta’an, Wafa’a, Suliman, Mohammad, Al Dameery, Khloud, and Bani Hani, Salam

Abstract

Background: Anxiety affects the patient’s perception of postoperative pain and causes a significant increase in the consumption of analgesia postoperatively.Objective: This study assesses the relationship between preoperative anxiety, postoperative pain and postoperative pethidine consumption.Methods: A prospective cohort design was used. Data were collected from 100 patients who were undergoing a laparoscopic cholecystectomy at St Joseph Hospital, Jerusalem. Pain-controlled analgesia with pethidine was utilised to manage pain throughout the postoperative period. The visual analogue scale scores and pethidine consumption of all patients were recorded.Findings: Participants’ mean level of pain was higher in the preoperative period (mean visual analogue scale = 1.3) compared with their mean level of pain in the postoperative period (mean visual analogue scale = 0.5). There is a statistically significant difference between the participants’ level of anxiety and postoperative pain level (p < 0.001). Gender, weight, level of education and smoking were predictors of developing preoperative anxiety. Also, gender, smoking and medication were statistically significant predictors of developing postoperative pain. Furthermore, gender, medical history and medication were statistically significant predictors of pethidine consumption postoperatively.Conclusions: The preoperative anxiety reduction intervention should be promoted and implemented as routine care for all surgical patients.

Published
2024
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24. Predicting mortality amongst Jordanian men with heart attacks using the chi-square automatic interaction detection model.

Author

Bani Hani S and Ahmad M

Subjects
Humans, Male, Jordan, Retrospective Studies, Middle Aged, Aged, Algorithms, Adult, Risk Factors, Chi-Square Distribution, Machine Learning, Myocardial Infarction mortality, Myocardial Infarction diagnosis
Abstract

Background: One of the most complicated cardiovascular diseases in the world is heart attack. Since men are the most likely to develop cardiac diseases, accurate prediction of these conditions can help save lives in this population. This study proposed the Chi-Squared Automated Interactive Detection (CHAID) model as a prediction algorithm to forecast death versus life among men who might experience heart attacks. Methods: Data were extracted from the electronic health solution system in Jordan using a retrospective, predictive study. Between 2015 and 2021, information on men admitted to public hospitals in Jordan was gathered. Results: The CHAID algorithm had a higher accuracy of 93.72% and an area under the curve of 0.792, making it the best top model created to predict mortality among Jordanian men. It was discovered that among Jordanian men, governorates, age, pulse oximetry, medical diagnosis, pulse pressure, heart rate, systolic blood pressure, and pulse pressure were the most significant predicted risk factors of mortality from heart attack. Conclusion: With heart attack complaints as the primary risk factors that were predicted using machine learning algorithms like the CHAID model, demographic characteristics and hemodynamic readings were presented., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Published
2024
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