دورية أكاديمية
The classification algorithms to support the management of the patient with femur fracture.
| العنوان: | The classification algorithms to support the management of the patient with femur fracture. |
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| المؤلفون: | Scala A; Department of Public Health, University of Naples 'Federico II', Naples, Italy., Trunfio TA; Department of Advanced Biomedical Sciences, University of Naples 'Federico II', Naples, Italy. teresa.trunfio@gmail.com., Improta G; Department of Public Health, University of Naples 'Federico II', Naples, Italy.; Interdepartmental Research Center on Management and Innovation in Healthcare, University of Naples 'Federico II', Naples, Italy. |
| المصدر: | BMC medical research methodology [BMC Med Res Methodol] 2024 Jul 16; Vol. 24 (1), pp. 150. Date of Electronic Publication: 2024 Jul 16. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288 (Electronic) Linking ISSN: 14712288 NLM ISO Abbreviation: BMC Med Res Methodol Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London : BioMed Central, [2001- |
| مواضيع طبية MeSH: | Algorithms* , Femoral Neck Fractures*/surgery , Femoral Neck Fractures*/therapy , Femoral Neck Fractures*/classification, Humans ; Female ; Male ; Aged ; Femoral Fractures/surgery ; Femoral Fractures/classification ; Femoral Fractures/therapy ; Length of Stay/statistics & numerical data ; Artificial Intelligence ; Middle Aged ; Aged, 80 and over ; Risk Factors |
| مستخلص: | Effectiveness in health care is a specific characteristic of each intervention and outcome evaluated. Especially with regard to surgical interventions, organization, structure and processes play a key role in determining this parameter. In addition, health care services by definition operate in a context of limited resources, so rationalization of service organization becomes the primary goal for health care management. This aspect becomes even more relevant for those surgical services for which there are high volumes. Therefore, in order to support and optimize the management of patients undergoing surgical procedures, the data analysis could play a significant role. To this end, in this study used different classification algorithms for characterizing the process of patients undergoing surgery for a femoral neck fracture. The models showed significant accuracy with values of 81%, and parameters such as Anaemia and Gender proved to be determined risk factors for the patient's length of stay. The predictive power of the implemented model is assessed and discussed in view of its capability to support the management and optimisation of the hospitalisation process for femoral neck fracture, and is compared with different model in order to identify the most promising algorithms. In the end, the support of artificial intelligence algorithms laying the basis for building more accurate decision-support tools for healthcare practitioners. (© 2024. The Author(s).) |
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| فهرسة مساهمة: | Keywords: Biomedical data analysis; Classification algorithms; Femur fracture; Machine learning; Modelling |
| تواريخ الأحداث: | Date Created: 20240716 Date Completed: 20240716 Latest Revision: 20240719 |
| رمز التحديث: | 20240719 |
| مُعرف محوري في PubMed: | PMC11251118 |
| DOI: | 10.1186/s12874-024-02276-5 |
| PMID: | 39014322 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1471-2288 |
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| DOI: | 10.1186/s12874-024-02276-5 |