Search

Objective: An objective and convenient primary triage procedure is needed for prioritizing patients who need help in mass casualty incident (MCI) situations, where there is a lack of medical staff and available resources. This study aimed to develop an automated remote decision-making algorithm that remotely categorize a patient's emergency level using clinical parameters that can be measured with a wearable device., Approach: The algorithm was developed according to the following procedures. First, we used the National Trauma Data Bank data set, a large open trauma patient data set assembled by the American College of Surgeons (ACS). In addition, we performed pre-processing to exclude data when the vital sign or consciousness indicator value was missing or physiologically in an abnormal range. Second, we selected the T-RTS method, which classifies emergency levels into four classes (Delayed, Urgent, Immediate and Dead), as the primary outcome. Third, three machine learning methods widely used in the medical field, logistic regression, random forest, and deep neural network (DNN), were applied to build the algorithm. Finally, each method was evaluated using quantitative performance indicators including the macro-averaged f1 score, macro-averaged mean absolute error (MMAE), and the area under the receiver operating characteristic curve (AUC)., Main Results: For total sets, the logistic regression had a macro-averaged f1 score of 0.673, an MMAE of 0.387 and an AUC value of 0.844 (95% CI, 0.843-0.845), while the random forest and DNN had macro-averaged f1 scores of 0.783 and 0.784, MMAEs of 0.297 and 0.298 and AUC values of 0.882 (95% CI, 0.881-0.883) and 0.883(95% CI, 0.881-0.884), respectively., Significance: In a comprehensive analysis of these results, our algorithm demonstrated a viable approach that could be practically adopted in an MCI. In addition, it can be employed to transfer patients and to redistribute available resources according to their priorities.

The fault detection and classification (FDC) modeling proposed in this study is a research approach that is intended to improve the performance of plasma process models by leveraging optical emission spectroscopy (OES) data containing plasma information (PI) and enhancing model interpretability using explainable artificial intelligence (XAI) algorithms. Status variable identification data that included normal and abnormal states of bias power, pressure, SF6 gas flow, and O2 gas flow were collected during a silicon etching process with SF6, O2 gas plasma. Additional variables were derived from the OES data and included additional PI, such as O and F radicals, which were computed using actinometry, and electron temperature and electron density computed using the line ratio method. By building a high-performance FDC model and interpreting its results using XAI algorithms, we propose solutions to the limitations of the FDC model in semiconductor plasma processes. [ABSTRACT FROM AUTHOR]

Pressure ulcers (PUs) are a prevalent skin disease affecting patients with impaired mobility and in high-risk groups. These ulcers increase patients' suffering, medical expenses, and burden on medical staff. This study introduces a clinical decision support system and verifies it for predicting real-time PU occurrences within the intensive care unit (ICU) by using MIMIC-IV and in-house ICU data. We develop various machine learning (ML) and deep learning (DL) models for predicting PU occurrences in real time using the MIMIC-IV and validate using the MIMIC-IV and Kangwon National University Hospital (KNUH) dataset. To address the challenge of missing values in time series, we propose a novel recurrent neural network model, GRU-D++. This model outperformed other experimental models by achieving the area under the receiver operating characteristic curve (AUROC) of 0.945 for the on-time prediction and AUROC of 0.912 for 48h in-advance prediction. Furthermore, in the external validation with the KNUH dataset, the fine-tuned GRU-D++ model demonstrated superior performances, achieving an AUROC of 0.898 for on-time prediction and an AUROC of 0.897 for 48h in-advance prediction. The proposed GRU-D++, designed to consider temporal information and missing values, stands out for its predictive accuracy. Our findings suggest that this model can significantly alleviate the workload of medical staff and prevent the worsening of patient conditions by enabling timely interventions for PUs in the ICU. [ABSTRACT FROM AUTHOR]

Research papers are a repository of information on the various elements that make up science and technology R&D activities. Generating knowledge maps based on research papers enables identification of specific areas of scientific and technical research as well as understanding of the flow of knowledge between those areas. Recently, as the number of electronic publishing and informatics archives along with the amount of accumulated knowledge related to science and technology has proliferated, the need to utilize the meta-knowledge obtainable from research papers has increased. Therefore, this study devised a model based on meta-knowledge (i.e., text information including citations, abstracts, area codes) for prediction of future growth potential using deep learning algorithms and investigated the applicability of the various forms of meta-knowledge to the prediction of future growth potential. It also proposes how to select the promising technology clusters based on the proposed model. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation forecasting (PVGF) is an attractive research topic for efficient energy management in smart city. In addition, the long short-term memory recurrent neural network (LSTM/RNN) has been actively utilized for predicting various time series tasks in recent years due to its outstanding ability to learn the feature of sequential time-series data. Although the existing forecasting models were obtained from learning the sequential PVGF data, it is observed that irregular factors made adverse effects on the forecasting results of very-short-term PVGF tasks, thus, the entire forecasting performance was deteriorated. In this regard, multiscale LSTM-based deep learning which is capable for forecasting very-short-term PVGF is proposed for efficient management. The model concatenates on two different scaled LSTM modules to overcome the deterioration that is originated from the irregular factors. Lastly, experimental results present the proposed framework can assist to forecast the tendency of PVGF amount steadily. [ABSTRACT FROM AUTHOR]

In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical personnel. However, when relying on a small number of medical personnel, the ability to increase survivability is limited. Therefore, developing a classification model for survival prediction that can quickly and precisely triage via wearable devices without medical personnel is important. In this study, we designed a consciousness index to substitute the factor by manpower and improved the classification accuracy by applying a machine learning algorithm. First, logistic regression analysis using vital signs and a consciousness index capable of remote monitoring through wearable devices confirmed the high efficiency of the consciousness index. We then developed a classification model with high accuracy which corresponds to existing injury severity scoring systems through the machine learning algorithms. We extracted 460,865 cases which met our criteria for developing the survival prediction from the national sample project in the national trauma databank which contains 408,316 cases of blunt injury and 52,549 cases of penetrating injury. Among the dataset, 17,918 (3.9%) cases died while the other survived. The AUCs with 95% confidence intervals (CIs) for the different models with the proposed simplified consciousness score as follows: RTS (as baseline), 0.78 (95% CI = 0.775 to 0.785); logistic regression, 0.87 (95% CI = 0.862 to 0.870); random forest, 0.87 (95% CI = 0.862 to 0.872); deep neural network, 0.89 (95% CI = 0.882 to 0.890). As a result, we confirmed the possibility of remote triage using a wearable device. It is expected that the time required for triage can be effectively reduced by using the developed classification model of survival prediction. [ABSTRACT FROM AUTHOR]

In many applications in the industry, tabular data are the most commonly used data type. Tabular data have the advantage of being easy to understand and interpret. Machine learning algorithms are mainly used to deduce a mapping function that maps input data to output in tabular data. Machine learning methods for dealing with tabular data are classified into two categories: similarity-based approach and feature-based approach. The feature-based approach explores a mapping function by clarifying the relationships between features, while the similarity-based approach uses similarities of observations to explore a mapping function. Both aspects have their pros and cons. Feature-based models are easy to understand and intuitive to use and deploy but generally cannot utilize the relationships between observations. Similarity-based models are most suited for exploiting the relationships among observations, but their availability is usually limited. In order to take advantage of both aspects, we propose an algorithm to combine feature-based and similarity-based approaches using a graph neural network. Additionally, to complement the shortcomings of cross-entropy loss, the proposed method applies supervised contrastive learning. Through supervised contrastive learning, the proposed method boosts the efficiency of learning the generalized representation for each class. Experimental results show that the proposed method provides more precise results for classification tasks, implying that it may improve the generalization capability. [ABSTRACT FROM AUTHOR]