A5100373926- Sepsis Diagnosis and Treatment
- Machine Learning in Healthcare
- Topic Modeling
- Photovoltaic System Optimization Techniques
- Radiomics and Machine Learning in Medical Imaging
- Solar Radiation and Photovoltaics
- Advanced Graph Neural Networks
- Recommender Systems and Techniques
- Pancreatic and Hepatic Oncology Research
- COVID-19 diagnosis using AI
- Lung Cancer Treatments and Mutations
- Venous Thromboembolism Diagnosis and Management
- Hemodynamic Monitoring and Therapy
- Advanced Mathematical Modeling in Engineering
- Artificial Intelligence in Healthcare and Education
- Solidification and crystal growth phenomena
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Acute Myocardial Infarction Research
- Heparin-Induced Thrombocytopenia and Thrombosis
- Gallbladder and Bile Duct Disorders
- Lung Cancer Diagnosis and Treatment
- COVID-19 Clinical Research Studies
- Machine Learning and ELM
- Gaussian Processes and Bayesian Inference
- Stochastic Gradient Optimization Techniques
First People's Hospital of Chongqing
2024
Genecast (China)
2024
Wuhan University of Technology
2015-2023
Huaihua University
2023
Digital China Health (China)
2019-2022
Chinese People's Liberation Army
2022
Chinese PLA General Hospital
2022
Huazhong University of Science and Technology
2020-2022
Fujian Provincial Hospital
2022
Tongji Hospital
2022
ABSTRACT Background With the outbreak of coronavirus disease 2019 (COVID-19), a sudden case increase in late February 2020 led to deep concern globally. Italy, South Korea, Iran, France, Germany, Spain, US and Japan are probably countries with most severe outbreaks. Collecting epidemiological data predicting epidemic trends important for development measurement public intervention strategies. Epidemic prediction results yielded by different mathematical models inconsistent; therefore, we...
Erythropoietin (Epo) is known for its role in erythropoiesis and acts by binding to receptor (EpoR) on the surface of erythroid progenitors. EpoR activity follows site hematopoiesis from embryonic yolk sac fetal liver then adult spleen bone marrow. Expression has also been observed selected cells non-hematopoietic origin, such as mouse brain during mid-gestation, at levels comparable transcripts decrease development falling birth less than 1–3% level hematopoietic tissue. We have now...
Background There is currently a lack of model for predicting the occurrence venous thromboembolism (VTE) in patients with lung cancer. Machine learning (ML) techniques are being increasingly adapted use medical field because their capabilities intelligent analysis and scalability. This study aimed to develop validate ML models predict incidence VTE among cancer patients. Methods Data from Grade 3A hospital China without were included. Patient characteristics clinical predictors related...
The accuracy of photovoltaic (PV) power forecasting decreases drastically under cloudy weather due to the rapid, violent and irregular fluctuation solar irradiance. Therefore, improve PV forecasting, a detailed study on influence clouds in different movement evolution patterns irradiance is very necessary. classification recognition kinds are basic effect between cloud A Support Vector Machine (SVM) based model using high temporal spatial resolution sky images captured via total imager...
Abstract Background Although protective mechanical ventilation (MV) has been used in a variety of applications, lung injury may occur both patients with and without acute respiratory distress syndrome (ARDS). The purpose this study is to use machine learning identify clinical phenotypes for critically ill MV intensive care units (ICUs). Methods A retrospective cohort was conducted 5013 who had undergone treatment the Department Critical Care Medicine, Peking Union Medical College Hospital....
Background and aims Currently, there are still no definitive consensus in the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aimed to build a clinical decision support tool based on machine learning using Surveillance, Epidemiology, End Results (SEER) database data from Fifth Medical Center PLA General Hospital China.Methods 4,398 eligible patients SEER 504 hospital data, who presented with histologically proven iCCA, were enrolled for modeling by cross-validation learning....
We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering broad range of diseases, modalities, imaging devices, and demography. After pre-training, VisionFM provides to foster multiple artificial intelligence (AI) applications, such as disease screening diagnosis, prognosis, subclassification phenotype, systemic biomarker prediction, each application enhanced expert-level accuracy. The generalist outperformed ophthalmologists...
Heparin is one of the most commonly used medications in intensive care units. In clinical practice, use a weight-based heparin dosing nomogram standard practice for treatment thrombosis. Recently, machine learning techniques have dramatically improved ability computers to provide decision support and allowed possibility computer generated, algorithm-based recommendations.The objective this study was predict effects using methods optimize units based on predictions. Patient state predictions...
Most patients with advanced non-small cell lung cancer (NSCLC) have a poor prognosis. Predicting overall survival using clinical data would benefit by allowing providers to design an optimum treatment plan. We compared the performance of nomograms machine-learning models at predicting NSCLC patients. This comparison benefits development and selection during decision-making process for
Abstract Background The study focuses on PD‐L1 expression as an essential biomarker for gauging the response of EGFR/ALK wild‐type NSCLC patients to FDA‐approved immune checkpoint inhibitors (ICIs). It aims explore clinical, molecular, and microenvironment characteristics associated with in lung adenocarcinoma eligible ICI therapy. Methods In this retrospective study, tumor samples from 359 Chinese underwent comprehensive evaluations NGS‐targeted sequencing. investigation encompassed...
The objective of the present work was to use artificial neural network study quantitative structure-activity relationship (QSAR) protective effects N-p-tolyl/phenylsulfonyl L-amino acid thiolester derivatives on anoxic damage rat pheochromocytoma (PC12) cells. Five molecular parameters these target compounds, including heat formation, total energy, dipole moment, energy highest occupied orbital and lowest unoccupied orbital, were calculated with PM6 semi-empirical quantum mechanical method....
Text clustering is an important method for effectively organising, summarising, and navigating text information. However, in the absence of labels, data to be clustered cannot used train representation model based on deep learning. To address problem, algorithm learning proposed using transfer domain adaptation parameters update during cluster iteration. First, source perform pre-training classification model. This procedure acts as initialisation parameters. Then, discriminator added model,...
Abstract To systematically evaluate the effects of quality nursing interventions on surgical site wound infections (SSWI), length stay in hospital and postoperative complications patients with colorectal stomas. A search was conducted Embase, PubMed, Cochrane Library, Web Science, Wanfang China National Knowledge Infrastructure databases to retrieve publicly available data from construction database until September 2023 randomised controlled trials (RCTs) evaluating applying cancer (CRC)...
Background Unfractionated heparin is widely used in the intensive care unit as an anticoagulant. However, weight-based dosing has been shown to be suboptimal and may place patients at unnecessary risk during their stay. Objective In this study, we intended develop validate a machine learning–based model predict treatment outcomes provide dosage recommendations clinicians. Methods A shallow neural network was adopted retrospective cohort of from Multiparameter Intelligent Monitoring Intensive...
Abstract Background Regional citrate anticoagulation (RCA) is an important local method during bedside continuous renal replacement therapy. To improve patient safety and achieve computer assisted dose monitoring control, we took intensive care units patients into cohort aiming at developing a data-driven machine learning model to give early warning of citric acid overdose provide adjustment suggestions on pumping rate 10% calcium gluconate input for RCA treatment. Methods Patient age,...
Abstract Background Kidney cancer originates from the urinary tubule epithelial system of renal parenchyma, accounting for 20% all tumors. Approximately 70% cases are localized at diagnosis, and 30% metastatic. Most kidney cancers can be cured by surgery, but most metastatic patients relapse after surgery eventually die cancer. Therefore, accurately predicting patient survival identifying high‐risk will effectively guide interventions improve prognosis. Methods This study used data 12,394...
To investigate the differences between inhaled nitric oxide (iNO) treatment and conventional therapy in of postoperative hypoxemia obese patients with acute type A aortic dissection (ATAAD).ATAAD diagnosed treated emergency surgery our hospital from June 2017 to December 2019 were retrospectively analyzed. Patients divided into iNO group control group. Propensity score matching was used analyze clinical characteristics results two groups.A total 218 ATAAD BMI ≥ 25 surgery. Among them, 115...
Based on the study about basic idea of PageRank algorithm, combining with MapReduce distributed programming concepts, paper first proposed a parallel algorithm based adjacency list which is suitable for massive data processing.Then, after examining essential characteristics iteration hidden behind PageRank, it provided an acceleration model vector computing.Following, using such model, again brought forward power MapReduce.Finally, abundant experimental analyses, has been proved that both...
To improve the prediction accuracy of photovoltaic (PV) power generation, temperature PV modules and its is very important. A short-term step-wise model for module based on Support Vector Machine ( SVM) proposed in this paper. Firstly, primary impact factors are determined terms physical characteristics correlation coefficient between each factor temperature. Secondly, two kinds models, direct built to predict respectively. For model, SVM using historical data. one, models at first step then...