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Background: Trauma is a serious medical and economic problem worldwide, and patients with trauma injuries have a poor survival rate following cardiac arrest. This study aimed to create a prediction model specific to prehospital trauma care and to achieve greater accuracy with techniques of machine learning.Methods: This retrospective observational study investigated data of patients who had blunt trauma injuries due to traffic accident and fall trauma from January 1, 2018, to December 31, 2019, using the National Emergency Medical Services Information System, which stores emergency medical service activity records nationwide in the United States. Random forest was used to develop a machine learning model. Results:Per the prediction model, the area under the curve of the predictive model was 0.95 and negative predictive value was 0.99. The feature importance of the predictive model was the highest for the AVPU scale (an acronym from "Alert, Verbal, Pain, Unresponsive"), followed by oxygen saturation (SpO2). Among patients who were progressing to cardiac arrest, the cutoff value was 89% for SpO2 in unalert patients.Conclusions: Patients whose conditions did not progress to cardiac arrest could be identified with high accuracy by machine learning model techniques.

The illegal dumping of aluminum and plastic into cities and marine areas leads to negative impacts on the ecosystem and contributes to increased environmental pollution. Although volunteer trash pickup activities have increased in recent years, they require significant effort, time, and money. Therefore, we propose automated trash pickup robot, which incorporates autonomous movement and trash pickup arms. Although these functions have been actively developed, relatively little research has focused on trash detection. As such, we have developed a trash detection function by using deep learning models to improve the accuracy. First, we created a new trash dataset that classifies four types of trash with high illegal dumping volumes (cans, plastic bottles, cardboard, and cigarette butts). Next, we developed a new you only look once (YOLO)-based model with low parameters and computations. We trained the model on a created dataset and a dataset consisting of marine trash created during previous research. In consequence, the proposed models achieve the same detection accuracy as the existing models on both datasets, with fewer parameters and computations. Furthermore, the proposed models accelerate the edge device’s frame rate.

Background: In Japan, increasing the number of ambulance requests, the case with the use of respiratory assistance devices in prehospital care by paramedics is also increasing. When patient experiences respiratory failure, the first responders frequently select a respiratory assist device (RAD) such as Bag Valve Mask (BVM), Jackson Rees (JR), or BVM with Gas Supply Valve Ⓡ (BVM+GSV). This is based on both evaluation and experience as there is no study indicating which RAD is the best choice at the pre-hospital emergency site. This study clarified the precautions when using BVM, JR, and BVM+GSV in pre-hospital emergency medical care with healthy volunteers. Methods: Twenty healthy adults were fitted with a RAD while breathing spontaneously, and changes in vital signs and ETCO2 were observed.Results: The level of ETCO2 became elevated after each RAD was attached. The value was significantly higher in the JR group than in the others.Conclusions: The study showed that even in the presence of spontaneous breathing, ETCO2 increased markedly with the application of respiratory assist devices that are used in pre-hospital care for conditions such as hypoxemia and ventilatory disturbance. The increase in ETCO2 was particularly significant in the

Background: All people have rights to take first aids equally because they have risk for sudden cardiac arrest at any time. Japan employs mainly two inclusive strategies for rapid response first aid. The first is public services such as fire trucks and ambulance response. The second is increasing the number of first responders. However, many residents are geographically unaware whether public services in their areas provide quick responses during emergencies, such as cardiac arrest. For this reason, they lack knowledge the necessity of mutual aid, which enables nearby neighbors who have undergone proper training to respond and provide first aid. Thus, the study aims to identify geographically specific areas where mutual aid is essential for rapid response first aid.Methods: The study targeted 20 cities in Japan designated by government ordinance and to simulate response areas reachable by public service. The driving conditions with 3 min were simulated with the speed limits, which are obeyed by the Japanese Road Traffic Act. Also, the populations covered in the areas was calculated in each targeted city.Results: The simulated map appears to render easy recognition of weak areas that may benefit from rapid response and may necessitate mutual aid. The maximum, minimum, and median population coverage rates are 65%, 22%, and 38.5%, respectively.Conclusion: The study indicates that mutual aid for rapid response is essential to most of the targeted areas in the case of sudden cardiac arrest. Moreover, mutual aid can be implemented strategically by geographic visualization and numerical values for equal rapid response first aid.