Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning

Criticality and clinical department prediction of ED patients using machine learning based on heterogeneous medical data

PROBLEM

Emergency triage faces multiple challenges, including limited medical resources and inadequate manual triage nurses, which cause incorrect triage, overcrowding in the emergency department (ED), and long patient waiting time.

OBJECTIVE

This paper aims to propose and validate an accurate and efficient artificial intelligence-based method for effectively ED triage and alleviating the pressure on medical resources.

METHODS

We propose two novel machine learning models, TransNet and TextRNN, for predicting patient severity levels and clinical departments using heterogeneous medical data in ED triage. Our models employ a parallel structure for feature extraction and incorporate an attention mechanism to extract essential information from the fused features, enabling accurate predictions. The models analyze the triage data (2020-2022) from the ED of Beijing University People's Hospital, incorporating variables (demographics, triage vital signs, and chief complaints) to identify patient severity levels and clinical departments. We performed data cleaning, categorization, and encoding first. Then, we divided the available data into a training set (56%), a validation set (24%), and a test set (20%) by random sampling. Finally, our models underwent 5-fold cross-validation and were compared with other state-of-the-art models.

RESULTS

We comprehensively evaluated the proposed models against various Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Traditional Machine Learning (TML), and Transformer-based (TF) models, achieving excellent performance in predicting triage outcomes. Specifically, TextRNN achieved a prediction success rate of 86.23% [85.86-86.70] for severity levels and 94.30% [94.00-94.46] for clinical departments among 161,198 ED visits. Moreover, TransNet demonstrated higher sensitivities of 84.08% and 90.05% for severity levels and clinical departments, respectively, with specificities of 76.48% and 95.16%. The accuracy of our model is 0.87%, 0.18%, 4.29%, and 1.96%, higher than that of the above four family models on average. Furthermore, our method significantly reduced under-triage by 12.06% and over-triage by 17.92% compared to manual triage.

CONCLUSIONS

Experimental results demonstrated that the proposed models fuse heterogeneous medical data in the triage process, successfully predicting patients' triage outcomes. Our models can improve triage efficiency, reduce the under/over-triage rate, and provide physicians with valuable decision-making support.

Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring

Despite the declining COVID-19 cases, global healthcare systems still face significant challenges due to ongoing infections, especially among fully vaccinated individuals, including adolescents and young adults (AYA). To tackle this issue, cost-effective alternatives utilizing technologies like Artificial Intelligence (AI) and wearable devices have emerged for disease screening, diagnosis, and monitoring. However, many AI solutions in this context heavily rely on supervised learning techniques, which pose challenges such as human labeling reliability and time-consuming data annotation. In this study, we propose an innovative unsupervised framework that leverages smartwatch data to detect and monitor COVID-19 infections. We utilize longitudinal data, including heart rate (HR), heart rate variability (HRV), and physical activity measured via step count, collected through the continuous monitoring of volunteers. Our goal is to offer effective and affordable solutions for COVID-19 detection and monitoring. Our unsupervised framework employs interpretable clusters of normal and abnormal measures, facilitating disease progression detection. Additionally, we enhance result interpretation by leveraging the language model Davinci GPT-3 to gain deeper insights into the underlying data patterns and relationships. Our results demonstrate the effectiveness of unsupervised learning, achieving a Silhouette score of 0.55. Furthermore, validation using supervised learning techniques yields high accuracy (0.884 ± 0.005), precision (0.80 ± 0.112), and recall (0.817 ± 0.037). These promising findings indicate the potential of unsupervised techniques for identifying inflammatory markers, contributing to the development of efficient and reliable COVID-19 detection and monitoring methods. Our study shows the capabilities of AI and wearables, reflecting the pursuit of low-cost, accessible solutions for addressing health challenges related to inflammatory diseases, thereby opening new avenues for scalable and widely applicable health monitoring solutions.

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment

Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence's role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients' mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.

Machine learning applications for COVID-19 outbreak management

Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.