Traumatic brain injury (TBI) remains a critical public health challenge. Although studies have found several prognostic factors for TBI, a useful early predictive tool for mortality has yet to be developed in the triage of the emergency room. This study aimed to use machine learning algorithms of artificial intelligence (AI) to develop predictive models for TBI patients in the emergency room triage. We retrospectively enrolled 18,249 adult TBI patients in the electronic medical records of three hospitals of Chi Mei Medical Group from January 2010 to December 2019, and undertook the 12 potentially predictive feature variables for predicting mortality during hospitalization. Six machine learning algorithms including logistical regression (LR) random forest (RF), support vector machines (SVM), LightGBM, XGBoost, and multilayer perceptron (MLP) were used to build the predictive model. The results showed that all six predictive models had high AUC from 0.851 to 0.925. Among these models, the LR-based model was the best model for mortality risk prediction with the highest AUC of 0.925; thus, we integrated the best model into the existed hospital information system for assisting clinical decision-making. These results revealed that the LR-based model was the best model to predict the mortality risk in patients with TBI in the emergency room. Since the developed prediction system can easily obtain the 12 feature variables during the initial triage, it can provide quick and early mortality prediction to clinicians for guiding deciding further treatment as well as helping explain the patient’s condition to family members.

Background: Numerous mortality prediction tools are currently available to assist patients with moderate to severe traumatic brain injury (TBI). However, an algorithm that utilizes various machine learning methods and employs diverse combinations of features to identify the most suitable predicting outcomes of brain injury patients in the intensive care unit (ICU) has not yet been well-established. Method: Between January 2016 and December 2021, we retrospectively collected data from the electronic medical records of Chi Mei Medical Center, comprising 2260 TBI patients admitted to the ICU. A total of 42 features were incorporated into the analysis using four different machine learning models, which were then segmented into various feature combinations. The predictive performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated using the Delong test. Result: The AUC for each model under different feature combinations ranged from 0.877 (logistic regression with 14 features) to 0.921 (random forest with 22 features). The Delong test indicated that the predictive performance of the machine learning models is better than that of traditional tools such as APACHE II and SOFA scores. Conclusion: Our machine learning training demonstrated that the predictive accuracy of the LightGBM is better than that of APACHE II and SOFA scores. These features are readily available on the first day of patient admission to the ICU. By integrating this model into the clinical platform, we can offer clinicians an immediate prognosis for the patient, thereby establishing a bridge for educating and communicating with family members.

Aims and Objectives: The goal of the present study was to explore anticipatory grief experiences among primary caregivers in home-based palliative care. We placed a special emphasis on coping methods adopted by individuals from Eastern cultures when facing death. Background: Most patients prefer spending their last days at home, which often exerts considerable physical and mental stress on their primary caregivers. Design: This study adopted a qualitative exploratory research design and used open-ended in-depth interviews to explore anticipatory grief experiences among primary caregivers of patients receiving palliative home care. Methods: In the study of the factors that Anticipatory Experience of Grief among Primary Caregivers in Palliative Home Care, we used the phenomenological approach and methods to glean answers. Posters and snowball sampling were used to individually recruit participants. In total, 16 primary hospice home caregivers participated. We used semi-structured interviews to collect data. The interviews were audio-recorded in a quiet location. Each interview was divided into two to three sessions, and each session lasted 60-90 minutes. For data analysis, field notes and audio recordings are transcribed into protocols. A thematic analysis method was used to analyze the context of the interviews. Sampling and analysis processes were repeated until theoretical saturation was achieved. Results: Four major themes emerged from the categorized interview data: meaning and manifestation as to the essence of life, the art of coping with death, realization and the gift of death, and adapting, coping with, and controlling emotions. Additionally, we observed, as with many Eastern cultures, a lunar calendar death in July and crying at the death of a relative is considered taboo. Conclusions: The coping methods adopted by individuals in Eastern cultures when facing death were summarized accordingly. These findings could serve as a reference to better understand the coping mechanisms these individuals use to address anticipatory grief when facing a family member’s impending death. Relevance to Clinical Practice: Potential relevance for the present findings include acceptance of Eastern cultural grief customs, early hospice and palliative care interventions for helping individuals adjust to their grief, case management and resource consolidation, providing 24-hour telephone consultation services, regular assessment visits, and developing localized teaching materials for life-and-death education.

Background: Traumatic brain injury (TBI) research often focuses on mortality rates or functional recovery, yet the critical need for long-term care among patients dependent on institutional or Respiratory Care Ward (RCW) support remains underexplored. This study aims to address this gap by employing machine learning techniques to develop and validate predictive models that analyze the prognosis of this patient population. Method: Retrospective data from electronic medical records at Chi Mei Medical Center, encompassing 2020 TBI patients admitted to the ICU between January 2016 and December 2021, were collected. A total of 44 features were included, utilizing four machine learning models and various feature combinations based on clinical significance and Spearman correlation coefficients. Predictive performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated with the DeLong test and SHAP (SHapley Additive exPlanations) analysis. Result: Notably, 236 patients (11.68%) were transferred to long-term care centers. XGBoost with 27 features achieved the highest AUC (0.823), followed by Random Forest with 11 features (0.817), and LightGBM with 44 features (0.813). The DeLong test revealed no significant differences among the best predictive models under various feature combinations. SHAP analysis illustrated a similar distribution of feature importance for the top 11 features in XGBoost, with 27 features, and Random Forest with 11 features. Conclusions: Random Forest, with an 11-feature combination, provided clinically meaningful predictive capability, offering early insights into long-term care trends for TBI patients. This model supports proactive planning for institutional or RCW resources, addressing a critical yet often overlooked aspect of TBI care.

We outlined a randomized controlled trial assessing the effect of a novel swallowing rehabilitation balloon on post-stroke dysphagia in home-based care. Stroke often causes dysphagia, and standard rehabilitation methods have limitations, prompting the need for innovative interventions. Stroke-induced dysphagia is challenging as it requires improved interventions beyond current limitations. The effect of the swallowing balloon’s impact on aspiration efficiency was assessed in and risk of the trial with stroke patients. Patient satisfaction, compliance, and cost-effectiveness were also evaluated. The outcome showed better swallowing and reduced risk of aspiration were achieved. This indicated the effectiveness of home-based balloon therapy. This novel approach enhances post-stroke dysphagia rehab, improves patients’ health, and reduces healthcare costs.