Chin-Choon Yeh

Background and Objectives: Major trauma ranks among the leading causes of mortality and handicap in both developing and developed countries, consuming substantial healthcare resources. Its unpredictable nature and diverse clinical presentations often lead to rapid and challenging-to-predict changes in patient conditions. An increasing number of models have been developed to address this challenge. Given our access to extensive and relatively comprehensive data, we seek assistance in making a meaningful contribution to this topic. This study aims to leverage artificial intelligence (AI)/machine learning (ML) to forecast potential adverse effects in major trauma patients. Methods: This retrospective analysis considered major trauma patient admitted to Chi Mei Medical Center from 1 January 2010 to 31 December 2019. Results: A total of 5521 major trauma patients were analyzed. Among five AI models tested, XGBoost showed the best performance (AUC 0.748), outperforming traditional clinical scores such as ISS and GCS. The model was deployed as a web-based application integrated into the hospital information system. Preliminary clinical use demonstrated improved efficiency, interpretability through SHAP analysis, and positive user feedback from healthcare professionals. Conclusions: This study presents a predictive model for estimating recovery probabilities in severe burn patients, effectively integrated into the hospital information system (HIS) without complex computations. Clinical use has shown improved efficiency and quality. Future efforts will expand predictions to include complications and treatment outcomes, aiming for broader applications as technology advances.

Background and Objectives: Burn injuries range from minor medical issues to severe, life-threatening conditions. The severity and location of the burn dictate its treatment; while minor burns might be treatable at home, severe burns necessitate medical intervention, sometimes in specialized burn centers with extended follow-up care. This study aims to leverage artificial intelligence (AI)/machine learning (ML) to forecast potential adverse effects in burn patients. Methods: This retrospective analysis considered burn patients admitted to Chi Mei Medical Center from 2010 to 2019. The study employed 14 features, comprising supplementary information like prior comorbidities and laboratory results, for building models for predicting graft surgery, a prolonged hospital stay, and overall adverse effects. Overall, 70% of the data set trained the AI models, with the remaining 30% reserved for testing. Three ML algorithms of random forest, LightGBM, and logistic regression were employed with evaluation metrics of accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Results: In this research, out of 224 patients assessed, the random forest model yielded the highest AUC for predictions related to prolonged hospital stays (>14 days) at 81.1%, followed by the XGBoost (79.9%) and LightGBM (79.5%) models. Besides, the random forest model of the need for a skin graft showed the highest AUC (78.8%), while the random forest model and XGBoost model of the occurrence of adverse complications both demonstrated the highest AUC (87.2%) as well. Based on the best models with the highest AUC values, an AI prediction system is designed and integrated into hospital information systems to assist physicians in the decision-making process. Conclusions: AI techniques showcased exceptional capabilities for predicting a prolonged hospital stay, the need for a skin graft, and the occurrence of overall adverse complications for burn patients. The insights from our study fuel optimism for the inception of a novel predictive model that can seamlessly meld with hospital information systems, enhancing clinical decisions and bolstering physician–patient dialogues.

The epidemiologic profile of ethnic Chinese patients with Dupuytren’s disease is unknown. We therefore investigated the epidemiology of Dupuytren’s disease using Taiwan’s National Health Insurance Research Database. Patients who filed claims for treatment for Dupuytren’s disease between January 2000 and December 2011 were identified in the database. Age- and gender-specific incidences were estimated by dividing the incidence number by population data. We identified 1,078 patients with Dupuytren’s disease (681 men, 397 women; male/female ratio: 1:1.72). The annual incidence rate ranged from 0.39-0.63/105 for men and 0.14-0.44/105 for women. A trend analysis revealed a rising trend in the annual incidence from 2001 to 2011 (p = 0.0199). The prevalence rate increased steadily from 0.46/105 in 2000 to 4.52/105 in 2011 (p = 0.0186). The mean age at onset was significantly higher in men than in women (60.7 ± 18.4 vs. 53.7 ± 15.5 years). Peak age at onset for men was 70–79 (28.1%) and for women was 50–59 (33.5%). Men > 60 years old had higher incidence rates than did women (incidence rate ratios: 2.0, 4.5, and 6.6 for those 60–69, 70–79, and ≥ 80, respectively). Hypertension (29.6%), diabetes mellitus (21.9%), hyperlipidemia (14.8%), ischemic heart disease (10.5%), and chronic obstructive pulmonary disease (8.0%) were the most common comorbidities. The incidence and prevalence of Dupuytren’s disease and the male/female ratio were significantly lower in ethnic Chinese than in Western ethnic groups. Moreover, the age at onset was significantly lower in ethnic Chinese women. However, the incidences of three comorbidities (hypertension, diabetes mellitus, and hyperlipidemia) were similar to those in other ethnicities.