Postoperative infection is a strong negative predictor of OS in patients with OCSCC undergoing ablative surgery.īreast cancer is one of the most common diseases in women worldwide. Postoperative infection was a negative predictor of OS after adjusting for patient, antibiotic, pathologic, and operative factors the adjusted hazard ratio for OS was 2.54 (95% confidence interval, 1.27-5.09). The 5-year OS in patients with a postoperative infection (24.1%) was lower than those without (65.2% P <. Cox regression and propensity-score matching were used to adjust for confounders.įifty-four of 114 patients had a postoperative infection. Kaplan-Meier survival curves were used to compare overall survival (OS) in patients with postoperative infection. Postoperative infection was considered within 30 days after surgery. We ascertain the association of postoperative infection on survival in patients with locoregionally advanced oral cavity squamous cell carcinoma (OCSCC).Ī retrospective study of patients with stage III/IVA OCSCC undergoing curative-intent surgery was performed. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare. The thematic network structure is arranged thusly that its subjects are organized into two different areas, i) practices and techniques related to data mining in healthcare, and ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field’s evolution over time. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. ![]() Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes (‘NEURAL-NETWORKS’, ‘CANCER’, ‘ELETRONIC-HEALTH-RECORDS’, ‘DIABETES-MELLITUS’, ‘ALZHEIMER’S-DISEASE’, ‘BREAST-CANCER’, ‘DEPRESSION’, and ‘RANDOM-FOREST’) are depicted in a thematic network. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area. All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression ( accuracy = 80.57 percent ) and the greatest acquired from the random forest ( accuracy = 94.64 percent ). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K -nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). The dataset included nine predictor factors and one predictor variable that were linked to the patients’ survival status (alive or dead). The dataset included female patients diagnosed between 20 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute’s SEER Program’s November 2017 update, which offers population-based cancer statistics. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. ![]() Breast cancer is one of the most commonly diagnosed female disorders globally.
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