CIVILICA We Respect the Science
(ناشر تخصصی کنفرانسهای کشور / شماره مجوز انتشارات از وزارت فرهنگ و ارشاد اسلامی: ۸۹۷۱)

Predicting Patient Length of Stay in a Neurosurgical Intensive Care Unit of a Large Teaching Hospital

عنوان مقاله: Predicting Patient Length of Stay in a Neurosurgical Intensive Care Unit of a Large Teaching Hospital
شناسه ملی مقاله: JR_SBMU-6-2_007
منتشر شده در در سال 1400
مشخصات نویسندگان مقاله:

Behrouz Alizadeh Savareh - National Agency for Strategic Research in Medical Education, Tehran, Iran
Ahmad Alibabaei - Anesthesiology and Critical Care Department, Critical Care Quality Improvement Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran- Virtual School of Medical Education and Management, Shahid Beheshti Unive
Soleiman Ahmady - National Agency for Strategic Research in Medical Education, Tehran, Iran- Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Majid Mokhtari - Department of Pulmonary and Critical Care Medicine, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Mohammadreza Hajiesmaeili - Anesthesiology and Critical Care Department, Critical Care Quality Improvement Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Saeedeh Nateghinia - Anesthesiology and Critical Care Department, Critical Care Quality Improvement Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

خلاصه مقاله:
Background: The intensive care unit (ICU) has the highest mortality and admission rates compared to other wards. Therefore, to increase the performance of hospital services, it is very important to evaluate indicators such as mortality and length of stay of patients in ICU. The present study aimed to investigate the neural network analysis method and Particle Swarm Optimization - Support Vector Machine to predict the length of stay in the neurosurgical intensive care unit.Materials and Methods: This descriptive research deals with data mining and modeling of intensive care unit processes, leading to a practical example of the application of health systems engineering knowledge, using MATLAB software. Data of ۱۲۰۰ patients admitted during the years ۲۰۱۷ to ۲۰۱۹ in the intensive care unit of neurosurgery. Then we evaluated all data with SVM + PSA and NCA.Results: Identifying the important features and using them has gradually reduced the LOS prediction error from ۴۰% to ۷%. Using the NCA technique makes better results for predicting ICU LOS.Conclusion: PSO + SMV in addition to NCA is a good predictor of ICU LOS screening in patients after neurosurgery and can provide more accurate prognostic factors.

کلمات کلیدی:
Neuro-ICU, Length of Stay, PSO, SVM, feature selection

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1547208/