A5018982357- COVID-19 diagnosis using AI
- COVID-19 Clinical Research Studies
- Machine Learning in Healthcare
- COVID-19 epidemiological studies
- Topic Modeling
- Functional Brain Connectivity Studies
- Domain Adaptation and Few-Shot Learning
- Infrared Target Detection Methodologies
- 3D Surveying and Cultural Heritage
- Neural dynamics and brain function
- Phonocardiography and Auscultation Techniques
- AI in cancer detection
- Remote Sensing and LiDAR Applications
- Music and Audio Processing
- COVID-19 and healthcare impacts
- Image Enhancement Techniques
- Drug-Induced Adverse Reactions
- Human Mobility and Location-Based Analysis
- SARS-CoV-2 detection and testing
- SARS-CoV-2 and COVID-19 Research
- Anomaly Detection Techniques and Applications
- Data Quality and Management
- Dermatological and COVID-19 studies
- EEG and Brain-Computer Interfaces
- Autoimmune Bullous Skin Diseases
Third Hospital of Hebei Medical University
2023
Hebei Medical University
2023
Xian Center for Disease Control and Prevention
2022
Jiangsu University
2020-2021
First People's Hospital of Kunshan
2020-2021
Xidian University
2012-2020
Huazhong University of Science and Technology
2020
The purpose of this study was to observe the imaging characteristics novel coronavirus pneumonia.Sixty-three confirmed patients were enrolled from December 30, 2019 January 31, 2020. High-resolution CT (HRCT) chest performed. number affected lobes, ground glass nodules (GGO), patchy/punctate opacities, patchy consolidation, fibrous stripes and irregular solid in each patient's image recorded. Additionally, we performed follow-up these patients.CT images 63 collected. M/F ratio: 33/30. mean...
Background Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of disease is essential to achieve optimal outcomes for as well reduce risk overloading health care system. Currently, severe nonsevere types are differentiated by only a few features, which do not comprehensively characterize complicated pathological, physiological, immunological responses SARS-CoV-2 infection in different types. In addition, these type-defining features may be...
Background Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there a lack of comprehensive understanding in various features and appropriate analytical approaches enabling the early detection effective diagnosis COVID-19. Objective We aimed to combine low-dimensional lab testing data, as well high-dimensional computed tomography (CT) imaging accurately differentiate between healthy...
Limited by the noise, missing data and varying sampling density of point clouds, planar primitives are prone to be lost during plane segmentation, leading topology errors when reconstructing complex building models. In this paper, a pipeline recover broken (TopoLAP) is proposed reconstruct level details 3 (LoD3) Firstly, segmented from incomplete clouds feature lines detected both images. Secondly, structural contours each segment reconstructed subset selection intersections these lines....
Effectively and efficiently diagnosing COVID-19 patients with accurate clinical type is essential to achieve optimal outcomes for the as well reducing risk of overloading healthcare system. Currently, severe non-severe types are differentiated by only a few features, which do not comprehensively characterize complicated pathological, physiological, immunological responses SARS-CoV-2 invasion in different types. In this study, we recruited 214 confirmed 148 type, from Wuhan, China. The...
The COVID-19 is sweeping the world with deadly consequences. Its contagious nature and clinical similarity to other pneumonias make separating subjects contracted non-COVID-19 viral pneumonia a priority challenge. However, testing has been greatly limited by availability cost of existing methods, even in developed countries like US. Intrigued wide routine blood tests, we propose leverage them for using power machine learning. Two proven-robust learning model families, random forests (RFs)...
Abstract Effectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal outcomes. Currently, there a lack of comprehensive understanding various biomedical features and appropriate technical approaches to accurately detecting patients. In this study, we recruited 214 confirmed in non-severe (NS) 148 severe (S) type, 198 non-infected healthy (H) participants 129 non-COVID viral pneumonia (V) The participants’ information (23 features), lab testing results...
Collaborative filtering technology is currently the most successful and widely used in recommendation system. It has achieved rapid development theoretical research practice. selects information similarity relationships based on user’s history collects others that are same as hobbies. User’s evaluation to generate recommendations. The main inadequate combination of context mining new points interest context-aware process. On basis traditional technology, view characteristics music...
Background clutter can obscure or mimic the desired target and confuse operator. Thus, similarity between background is a typical feature of clutter. A metric based on human contrast sensitivity function proposed. It measures in frequency domain, after differences that cannot be seen by visual system have been "thrown away" visible weighted according to system. The correlation experimental probability detection predicted are presented. Experiment results show...
Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted necessity for entity systems capture global coherence. However, there are two common weaknesses previous models. First, most them calculate pairwise scores between all candidate and select relevant group as final result. In this process, consistency among wrong well that right ones involved, which may introduce noise data increase model complexity. Second,...
Inferring patterns of synchronous brain activity from a heterogeneous sample electroencephalograms (EEG) is scientifically and methodologically challenging. While it intuitively statistically appealing to rely on readings more than one individual in order highlight recurrent activation, pooling information across subjects presents non-trivial methodological problems. We discuss some the scientific issues associated with understanding synchronized neuronal propose framework for statistical...
<sec> <title>BACKGROUND</title> Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there a lack of comprehensive understanding in various features and appropriate analytical approaches enabling the early detection effective diagnosis COVID-19. </sec> <title>OBJECTIVE</title> We aimed to combine low-dimensional lab testing data, as well high-dimensional computed tomography (CT)...
<sec> <title>BACKGROUND</title> Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of disease is essential to achieve optimal outcomes for as well reduce risk overloading health care system. Currently, severe nonsevere types are differentiated by only a few features, which do not comprehensively characterize complicated pathological, physiological, immunological responses SARS-CoV-2 infection in different types. In addition, these type-defining...
Background Non-pharmaceutical interventions (NPIs) against COVID-19 may prevent the spread of other infectious diseases. Our purpose was to assess effects NPIs on diarrhea in Xi'an, China. Methods Based surveillance data diarrhea, and different periods emergence responses for Xi'an from 2011 2021, we applied Bayesian structural time series model interrupted evaluate epidemiological characteristics causative pathogens diarrhea. Findings A total 102,051 cases were reported 2021. The results...