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Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches

Yuanren Tong, Keming Lu, Yingyun Yang, Ji Li, Yucong Lin, Dong Wu, Aiming Yang, Yue Li, Sheng Yu, Jiaming Qian
2020 BMC Medical Informatics and Decision Making  
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

Guihua Chen, Jun Shen
2021 Frontiers in Bioengineering and Biotechnology  
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

Yutong Wang, Xiaoyun He, Hui Nie, Jianhua Zhou, Pengfei Cao, Chunlin Ou
2020 American Journal of Cancer Research  
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

Daniela Cornelia Lazăr, Mihaela Flavia Avram, Alexandra Corina Faur, Adrian Goldiş, Ioan Romoşan, Sorina Tăban, Mărioara Cornianu
2020 Medicina  
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

Bowei Ma, Yucheng Guo, Weian Hu, Fei Yuan, Zhenggang Zhu, Yingyan Yu, Hao Zou
2020 Frontiers in Pharmacology  
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

Laura Judith Marcos-Zambrano, Kanita Karaduzovic-Hadziabdic, Tatjana Loncar Turukalo, Piotr Przymus, Vladimir Trajkovik, Oliver Aasmets, Magali Berland, Aleksandra Gruca, Jasminka Hasic, Karel Hron, Thomas Klammsteiner, Mikhail Kolev (+17 others)
2021 Frontiers in Microbiology  
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

Athanasia Mitsala, Christos Tsalikidis, Michail Pitiakoudis, Constantinos Simopoulos, Alexandra K Tsaroucha
2021 Current Oncology  
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

Simone Brogi, Vincenzo Calderone
2021 International Journal of Translational Medicine  
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]

Yixin Li, Chen Li, Xiaoyan Li, Kai Wang, Md Mamunur Rahaman, Changhao Sun, Hao Chen, Xinran Wu, Hong Zhang, Qian Wang
2021 arXiv   pre-print
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  
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


2021 Basic & Clinical Pharmacology & Toxicology  
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

Pengjun Zhai, Chen Wang, Yu Fang
2021 Applied Sciences  
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  
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  
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  
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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