Emergency department presentations of patients with alcohol use disorders in an Australian regional health district.

Background: This study aimed to investigate the longitudinal changes in emergency department (ED) presentations incurred by patients with alcohol use disorders. Methods: A retrospective quantitative analysis was conducted on patients' ED presentations between December 2011 and January 2019 in an Australian regional health district. The health district has five EDs serving rural, regional, and metropolitan areas. Patients with alcohol use disorders were divided into two groups for comparison: those who had interactions with the community-based Drug and Alcohol (D&A) services and those who did not. Results: A total of 2,519 individual patients with alcohol use disorders made 21,715 ED presentations. Among these patients, 75.4% did not have interactions with the community-based D&A services. Compared with those who had, these patients were older, more likely to be diagnosed with abdominal pain (26.9% vs 12.0%, p < 0.001) and chest pain (16.2% vs 8.6%, p < 0.001), and had longer mean length of ED stay (7 hours and 41.7 minutes vs 6 hours and 25.6 minutes, p < 0.001). For the patients who had interactions with the community-based D&A services, their 28-day re-presentation rates decreased from 55.5% (2013-14) to 45.1% (2017-18); however, were higher than that of those who had no interactions (41.1% to 32.8%). Overall, 21.9%-24.5% of the patients were frequent ED presenters (i.e., ≥4 visits per year). Frequent ED presenters were proportionately higher among the patients who had interactions with the community-based D&A services, consistently over the relevant years. Although patients with alcohol use disorders frequently presented to EDs, their alcohol use disorders were only identified in 8.9% of their presentations. Conclusions: Patients with alcohol use disorders were often unidentified in EDs. Those who did not have interactions with the community-based D&A services were less likely to be diagnosed with alcohol use disorders when presenting to EDs.

Predicting lung cancer survival based on clinical data using machine learning: A review.

Machine learning has gained popularity in predicting survival time in the medical field. This review examines studies utilizing machine learning and data-mining techniques to predict lung cancer survival using clinical data. A systematic literature review searched MEDLINE, Scopus, and Google Scholar databases, following reporting guidelines and using the COVIDENCE system. Studies published from 2000 to 2023 employing machine learning for lung cancer survival prediction were included. Risk of bias assessment used the prediction model risk of bias assessment tool. Thirty studies were reviewed, with 13 (43.3%) using the surveillance, epidemiology, and end results database. Missing data handling was addressed in 12 (40%) studies, primarily through data transformation and conversion. Feature selection algorithms were used in 19 (63.3%) studies, with age, sex, and N stage being the most chosen features. Random forest was the predominant machine learning model, used in 17 (56.6%) studies. While the number of lung cancer survival prediction studies is limited, the use of machine learning models based on clinical data has grown since 2012. Consideration of diverse patient cohorts and data pre-processing are crucial. Notably, most studies did not account for missing data, normalization, scaling, or standardized data, potentially introducing bias. Therefore, a comprehensive study on lung cancer survival prediction using clinical data is needed, addressing these challenges.

Predicting Readmission Following Hospital Treatment for Patients with Alcohol Related Diagnoses in an Australian Regional Health District.

This study aims to investigate the prediction of hospital readmission of alcohol use disorder patients within 28 days of discharge and compare the performance of six machine learning methods i.e., random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM.