Pulse-line intersection method with unboxed artificial intelligence for hesitant pulse wave classification

Chen, Hsing‐Chung; Damarjati, Cahya; Putra, Karisma Trinanda; Chen, Han-MI; Hsieh, Ching-Liang; Lin, Hung‐Jen; Wu, Mei-Yao; Chen, Chin‐Sheng

doi:10.1016/j.ipm.2021.102855

Cited by 16 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to trust a particular result, biomedical scientists need to understand how it was obtained. This problem is being addressed by the XAI (eXplainable Artificial Intelligence) discipline, which offers a variety of ways to provide some level of explanation to deep learning AI solutions (Chen et al, 2022; Muddamsetty et al, 2022; Yang et al, 2022). NUMERATE is being updated to include XAI methods.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

et al. 2022

View full text Add to dashboard Cite

In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning and attention mechanisms to study the interactions between different biomedical data to detect and diagnose diseases. We conduct extensive testing on biomedical data. The results show the benefits of using deep learning technologies in the field of artificial intelligence of medical things to diagnose diseases in the healthcare decision‐making process. For example, the disease detection rate using the proposed methodology achieves 92%, which is greatly improved compared to the higher‐level disease detection models.

show abstract

Section: Discussion and Future Directionsmentioning

confidence: 99%

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Explainable AI collectively refers to methods that can exploit the interpretability of a given decision-making process, such as traditional and modern ML models. An enormous potential is shown from this branch of AI study that can unbox the modern ML 'black-box' model [34]. LIME is powerful because it provides accessibility and simplicity [35].…”

Section: Explainable Ai Modelmentioning

confidence: 99%

Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence

et al. 2022

Self Cite

View full text Add to dashboard Cite

Early prediction of students’ learning performance and analysis of student behavior in a virtual learning environment (VLE) are crucial to minimize the high failure rate in online courses during the COVID-19 pandemic. Nevertheless, traditional machine learning models fail to predict student performance in the early weeks due to the lack of students’ activities’ data in a week-wise timely manner (i.e., spatiotemporal feature issues). Furthermore, the imbalanced data distribution in the VLE impacts the prediction model performance. Thus, there are severe challenges in handling spatiotemporal features, imbalanced data sets, and a lack of explainability for enhancing the confidence of the prediction system. Therefore, an intelligent framework for explainable student performance prediction (ESPP) is proposed in this study in order to provide the interpretability of the prediction results. First, this framework utilized a time-series weekly student activity data set and dealt with the VLE imbalanced data distribution using a hybrid data sampling method. Then, a combination of convolutional neural network (CNN) and long short-term memory (LSTM) was employed to extract the spatiotemporal features and develop the early prediction deep learning (DL) model. Finally, the DL model was explained by visualizing and analyzing typical predictions, students’ activities’ maps, and feature importance. The numerical results of cross-validation showed that the proposed new DL model (i.e., the combined CNN-LSTM and ConvLSTM), in the early prediction cases, performed better than the baseline models of LSTM, support vector machine (SVM), and logistic regression (LR) models.

show abstract

“…Many meaningful tongue and pulse features for the diagnosis of various diseases have been discovered using large amounts of complicated clinical data via AI-based data mining methods. For instance, the multistep approach (4), Genetic Algorithm_Extreme Gradient Boosting (GA_XGBT) model (5), random forest (6), and convolutional neural network (MIMT-CNN) (7) have been widely applied in diabetic tongue research, while AdaBoost (8), Support Vector Machine (SVM) (9), and logistic regression (10) have been applied in pulse research. The use of AI to understand the clinical data of diseases can help to objectively and efficiently improve the accuracy and precision of diagnosis (2).…”

Section: Introductionmentioning

confidence: 99%

Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study

Tian¹,

Wang²,

Sun³

et al. 2023

Ann Transl Med

View full text Add to dashboard Cite

Background: With the development of technology and the renewal of traditional Chinese medicine (TCM) diagnostic equipment, artificial intelligence (AI) has been widely applied in TCM. Numerous articles employing this technology have been published. This study aimed to outline the knowledge and themes trends of the four TCM diagnostic methods to help researchers quickly master the hotspots and trends in this field. Four TCM diagnostic methods is a TCM diagnostic method through inspection, listening, smelling, inquiring and palpation, the purpose of which is to collect the patient's medical history, symptoms and signs. Then, it provides an analytical basis for later disease diagnosis and treatment plans. Methods: Publications related to AI-based research on the four TCM diagnostic methods were selected from the Web of Science Core Collection, without any restriction on the year of publication. VOSviewer and Citespace were primarily used to create graphical bibliometric maps in this field. Results: China was the most productive country in this field, and Evidence-Based Complementary and Alternative Medicine published the largest number of related papers, and the Shanghai University of Traditional Chinese Medicine is the dominant research organization. The Chengdu University of Traditional Chinese Medicine had the highest average number of citations. Jinhong Guo was the most influential author and Artificial Intelligence in Medicine was the most authoritative journal. Six clusters separated by keywords association showed the range of AI-based research on the four TCM diagnostic methods. The hotspots of AI-based research on the four TCM diagnostic methods included the classification and diagnosis of tongue images in patients with diabetes and machine learning for TCM symptom differentiation.Conclusions: This study demonstrated that AI-based research on the four TCM diagnostic methods is currently in the initial stage of rapid development and has bright prospects. Cross-country and regional cooperation should be strengthened in the future. It is foreseeable that more related research outputs will rely on the interdisciplinarity of TCM and the development of neural networks models.

show abstract

Pulse-line intersection method with unboxed artificial intelligence for hesitant pulse wave classification

Cited by 16 publications

References 32 publications

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence

Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study

Contact Info

Product

Resources

About