A5056505424- Machine Learning in Healthcare
- Biomedical Text Mining and Ontologies
- Topic Modeling
- Artificial Intelligence in Healthcare
- Business Process Modeling and Analysis
- Clinical practice guidelines implementation
- Semantic Web and Ontologies
- Rough Sets and Fuzzy Logic
- Electronic Health Records Systems
- Advanced Causal Inference Techniques
- Dementia and Cognitive Impairment Research
- Advanced Thermodynamic Systems and Engines
- Artificial Intelligence in Healthcare and Education
- Antiplatelet Therapy and Cardiovascular Diseases
- Statistical Methods in Clinical Trials
- Heart Failure Treatment and Management
- Natural Language Processing Techniques
- ECG Monitoring and Analysis
- Data Mining Algorithms and Applications
- Statistical Methods and Inference
- Genetic Associations and Epidemiology
- AI in cancer detection
- Service-Oriented Architecture and Web Services
- Digital Mental Health Interventions
- Medical Coding and Health Information
Zhejiang University
2016-2025
Zhejiang University of Science and Technology
2022-2025
Xinjiang University
2025
National Engineering Research Center of Electromagnetic Radiation Control Materials
2022
Ministry of Education of the People's Republic of China
2010-2020
Xinjiang Medical University
2015-2017
First People's Hospital of Foshan
2017
Tsinghua University
2013
Eindhoven University of Technology
2010
Computer-aided polyp detection in gastric gastroscopy has been the subject of research over past few decades. However, despite significant advances, automatic real time is still an unsolved problem. In this paper, we report on a convolutional neural network (CNN) for that constructed based Single Shot MultiBox Detector (SSD) architecture and which call SSD Gastric Polyps (SSD-GPNet). To take full advantages feature maps' information from pyramid to acquire higher accuracy, re-use abandoned...
In recent years, there has been a dramatic rise in interest retrosynthesis prediction with artificial intelligence (AI) techniques. Unlike conventional performed by chemists and rule-based expert systems, AI-driven automatically learns chemistry knowledge from off-the-shelf experimental datasets to predict reactions routes. This provides an opportunity address many challenges, including heavy reliance on extensive expertise, the sub-optimality of routes, prohibitive computational cost....
Brain age gap (BAG), the deviation between estimated brain and chronological age, is a promising marker of health. However, genetic architecture reliable targets for aging remains poorly understood. In this study, we estimate magnetic resonance imaging (MRI)–based using deep learning models trained on UK Biobank validated with three external datasets. A genome-wide association study BAG identified two unreported loci seven previously reported loci. By integrating Mendelian Randomization (MR)...
Acute coronary syndrome (ACS), as a common and severe cardiovascular disease, is leading cause of death the principal serious long-term disability globally. Clinical risk prediction ACS important for early intervention treatment. Existing scoring models are based mainly on small set hand-picked factors often dichotomize predictive variables to simplify score calculation.This study develops regularized stacked denoising autoencoder (SDAE) model stratify clinical risks patients from large...
Abstract Background The interpretability of results predicted by the machine learning models is vital, especially in critical fields like healthcare. With increasingly adoption electronic healthcare records (EHR) medical organizations last decade, which accumulated abundant patient data, neural networks or deep techniques are gradually being applied to clinical tasks utilizing huge potential EHR data. However, typical black-boxes, not transparent and prediction outcomes difficult interpret....
We describe the setup of testing regions for China Earthquake Forecast Testing Center and provide preliminary forecast results in scope Collaboratory Study Predictability (CSEP) project. investigate spatiotemporal variations completeness magnitude M c by using frequency‐magnitude distribution Networks (CENC) catalog. find three periods significantly different histories: (I) 1 January 1970–30 September 2001, (II) October 2001–30 2008, (III) 2008–31 August 2011. mapping provides median values...
Clinical pathways leave traces, described as event sequences with regard to a mixture of various latent treatment behaviors. Measuring similarities between patient traces can profitably be exploited further basis for providing insights into the pathways, and complementing existing techniques clinical pathway analysis (CPA), which mainly focus on looking at aggregated data seen from an external perspective. Most methods measure via computing relative distance their sequences. However, typical...
Introduction This study explores the role of digital intelligence technology (DIT) in indirectly enhancing curriculum ideological effectiveness sports colleges through improvements teaching and student engagement. The research provides insights into how integration can influence political education outcomes. Methods A nationwide sample 804 faculty respondents was analyzed using a chain mediation model grounded Technology Acceptance Model, Self-Determination Theory, Educational Ecosystem...
Abstract Previous research has established Type 2 Diabetes Mellitus as a significant risk factor for various disorders, adversely impacting human health. While evidence increasingly links type diabetes to cognitive impairment and brain understanding the causal effects of its preclinical stage on health is yet be fully known. This knowledge gap hinders advancements in screening preventing neurological psychiatric diseases. To address this gap, we employed robust machine learning algorithm...
Healthcare process leaves patient treatment trajectory (PTT), described as a sequence of interdependent clinical events affiliated with large volume longitudinal therapy and information. Predicting the future event in PTT, vital essential task for providing insights into entire trajectory, can serve an efficient proactive altering service health delivery. However, it is challenging because there are long-term dependencies between events, which irregularly distributed along temporal axis...
Importance Although digital cognitive behavioral therapy for insomnia (dCBT-I) has been studied in many randomized clinical trials and is recommended as a first-line treatment option, few studies have systematically examined its effectiveness, engagement, durability, adaptability settings. Objective To evaluate the of dCBT-I. Design, Setting, Participants This retrospective cohort study was conducted using longitudinal data collected via mobile app named Good Sleep 365 between November 14,...