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Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches
2020
BMC Medical Informatics and Decision Making
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Random forest (RF) and convolutional neural network (CNN) approaches were applied to different disease entities. ...
Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases. ...
We hereby state that the study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki as reflected in a priori approval by the institution's human research committee. ...
doi:10.1186/s12911-020-01277-w
pmid:32993636
pmcid:PMC7526202
fatcat:aejcpcfybbhxfbmxcyfh3gw5aq
Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease
2021
Frontiers in Bioengineering and Biotechnology
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Convolutional neural networks are advanced image processing algorithms that are currently in existence. ...
Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora ...
(SVMs), neural network, Naïve Bayes (NB), and random forest (RF). ...
doi:10.3389/fbioe.2021.635764
fatcat:os5pwjcowffilorbhfwlnthi4a
Application of artificial intelligence to the diagnosis and therapy of colorectal cancer
2020
American Journal of Cancer Research
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Recently, AI has been widely applied in the medical field. The effective combination of AI and big data can provide convenient and efficient medical services for patients. ...
processing, and machine learning. ...
Acknowledgements This study was supported by the National Natural Science Foundation of China (81903032), ...
pmid:33294256
pmcid:PMC7716173
fatcat:ahdp6zyb2fbmfkg5rz5mkpfj7i
The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future
2020
Medicina
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Recent research has tended toward computerized, automatic, and real-time detection of lesions, which are approaches that offer utility in daily practice. ...
early detection of neoplastic diseases, implementation of the best treatment strategy, and optimization of patient prognosis. ...
network; CNN-convolutional neural network; R-CNN-regional-based convolutional neural network; SSD-Single-Shot MultiBox Detector; FCN-fully convolutional network; DT-decision tree; ARR-average recall rate ...
doi:10.3390/medicina56070364
pmid:32708343
fatcat:kp67to5sdfd6tox742id2yogj4
Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach
2020
Frontiers in Pharmacology
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Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. ...
We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. ...
This project was supported by the Shanghai Science and Technology Committee (18411953100) ...
doi:10.3389/fphar.2020.572372
pmid:33132910
pmcid:PMC7562716
fatcat:wyyu6c3i4zc65fmd2kjfrdhwky
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment
2021
Frontiers in Microbiology
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In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes ...
The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit ...
process of ML methods currently used in microbiome research during action workshops. ...
doi:10.3389/fmicb.2021.634511
pmid:33737920
pmcid:PMC7962872
fatcat:wbun4lkwwjen5ccdy4zb7mnz3q
Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era
2021
Current Oncology
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CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. ...
Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine. ...
CNN, convolutional neural network. ...
doi:10.3390/curroncol28030149
pmid:33922402
fatcat:cz65gjzmmzccpgywyu2jxrjmhm
Artificial Intelligence in Translational Medicine
2021
International Journal of Translational Medicine
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Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical ...
Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting ...
ML/DL approaches suitable in the drug discovery field include RF, Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Graph Convolutional Neural Networks (GCNN), Convolutional Neural Networks ...
doi:10.3390/ijtm1030016
fatcat:c6g6ld26gjg6jbkcddauo44qvu
A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis
[article]
2021
arXiv
pre-print
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In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models. ...
Among these methods, random field models play an indispensable role in improving the analysis performance. ...
N2019003) and the "China Scholarship Council" (No. 2018GBJ001757). We thank Miss Zixian Li and Mr. Guoxian Li for their importantsupport and discussion in this work. ...
arXiv:2009.13721v3
fatcat:q46wb3rhwjcode3b46h6v2lhoa
CARS 2020—Computer Assisted Radiology and Surgery Proceedings of the 34th International Congress and Exhibition, Munich, Germany, June 23–27, 2020
2020
International Journal of Computer Assisted Radiology and Surgery
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Preface Analogue and Digital CARS 2020 Congress The overarching purpose of the scholarly publication and communication process of IJCARS in the context of the CARS congress could be defined as: "To enable ...
stimulate complimentary thoughts and actions within the given domain of discourse by all parties involved in the scientific/medical communication process". ...
We thank NVIDIA for the Titan X hardware grant that allowed us to process the images in a faster way. ]. ...
doi:10.1007/s11548-020-02171-6
pmid:32514840
fatcat:lyhdb2zfpjcqbf4mmbunddwroq
Abstracts
2021
Basic & Clinical Pharmacology & Toxicology
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Conclusion: Emergency nursing model can play a significant role in emergency of critically ill patients. ...
and promotion in clinical nursing. ...
Methods: In the study, a multi-feature augmentation convolutional neural network (Aug-CNN) model is proposed. The model has robust performance. ...
doi:10.1111/bcpt.13588
pmid:34041856
fatcat:wobg4mjgunfilgvzqsj5l4an7m
LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction
2021
Applied Sciences
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LSLSD can effectively capture different levels of semantic information within and between event sentences in the electronic medical record (EMR) corpus. ...
This paper proposes a diagnosis and treatment event extraction method in the Chinese language, fusing long short-level semantic dependency of the corpus, LSLSD, for solving these problems. ...
Acknowledgments: We would like to thank two doctors from a First-class Hospital at Grade III in Shanghai, who manually checked and corrected the data set that is labeled by our semi-automatic annotation ...
doi:10.3390/app11167237
fatcat:2tdudo36hrbyxcrggcbd2z54ei
Panel and Study Groups
2013
Neuropsychopharmacology
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We have had the opportunity to study both genetic variation and expression in genes in this pathway (GAD1, GAD2, SLC12A2 (NKCC1) and SLC12A5 (KCC2) across human brain development in hippocampus and dorsolateral ...
Methods: We have studied over 238 normal human brains in both DLPFC and hippocampus ranging in age from week 14 in the fetus to 80 years of age, using Illumina microchip arrays, qRT-PCR and RNA Seq. ...
higher order traits, but that can help link networks at different scales (eg, molecular and imaging) across cohorts. ...
doi:10.1038/npp.2013.278
fatcat:expu2u3f3reypazj7tt7jhybfa
Abstracts from the Human Genome Meeting 2018
2018
Human Genomics
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by GSDC and KREONET in KISTI. ...
by GSDC and KREONET in KISTI. ...
by a random forest model. ...
doi:10.1186/s40246-018-0138-6
fatcat:nl6xhsuchzgbxik4pjwjvbbkie
ACNP 58th Annual Meeting: Poster Session III
2019
Neuropsychopharmacology
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These data show that JOTROL can address several ADrelevant targets in aged transgenic mice, supporting a multipronged approach. ...
To evaluate this possibility, we utilize network analytic approaches to characterize pediatric anxiety as a network of symptom domains. ...
Conclusions: In this project, we showed the feasibility and potentials of achieving better tissue segmentation with a convolutional neural network for the infant MRI scans. ...
doi:10.1038/s41386-019-0547-9
pmid:31801974
pmcid:PMC6957926
fatcat:dd7d43ysfvc5bbbstfl73szya4
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