Natural language interface model for the evaluation of ergonomic routines in occupational health (ILENA)

Jutinico, Carlos Julio Moya; Herrera‐Viedma, Enrique; Burgos, Daniel; Crespo, Rubén González

doi:10.1007/s12652-018-0770-y

Cited by 19 publications

(3 citation statements)

References 14 publications

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“…To solve the detection problem, [25] combined ResNet and BERT, and to increase the performance of a single model, researchers looked at stepwise ensemble selection from model libraries. [26,[29][30] leveraged syntactic information and adopted a multi-task learning framework based on BERT to progress the reference line typical to sense grammatical errors.…”

Section: Related Workmentioning

confidence: 99%

Combining GCN and Transformer for Chinese Grammatical Error Detection

Zhang

2022

Journal of Internet Technology

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<p>This paper describes our system at a task: Chinese Grammatical Error Diagnosis (CGED). The task is held by the Natural Language Processing Techniques for Educational Applications (NLP-TEA) to encourage the development of automatic grammatical error diagnosis in Chinese learning since 2014. The goal of CGED is to diagnose four types of grammatical errors: word selection (S), redundant words (R), missing words (M), and disordered words (W). The automatic CGED system contains two parts including error detection and error correction and our system is designed to solve the error detection problem. Our system is built on three models: 1) a BERT-based model leveraging syntactic information; 2) a BERT-based model leveraging contextual embeddings; 3) a lexicon-based graph neural network leveraging lexical information. We also design an ensemble mechanism to improve the single model’s performance. Finally, our system achieves the highest F1 scores at detection level and identification level among all teams participating in the CGED 2020 task.</p> <p> </p>

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Section: Related Workmentioning

confidence: 99%

Combining GCN and Transformer for Chinese Grammatical Error Detection

Zhang

2022

Journal of Internet Technology

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“…Besides, these images will separate important from chronic DVT if the distortion configuration is monitored carefully. It is scientifically important to make a difference between important and enduring DVT (Jutinico et al, 2019). Heparin is used to treat an acute DVT patient and oral anti-coagulant with persistent DVT alone, as heparin is not used for its larger anti-coagulant and anti-inflammatory activity.…”

Section: Introduction: Overview Of Deep Venous Thrombosis In Patientsmentioning

confidence: 99%

Effect Analysis of Nursing Intervention on Lower Extremity Deep Venous Thrombosis in Patients

Zhang

2023

International Journal of Data Warehousing and Mining

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In the modern era, nursing intervention is an increased commitment to patient quality and protection that allows nurses to make evidence-based healthcare decisions. The challenging characteristic of patients such as high deep venous thrombosis (DVT) and respiratory embolisms (RE) are significant health conditions that lead to post-operative severe injury and death. In this article, hybrid machine learning (HML) is used for senile patients with lower extremity fractures during the perioperative time and the clinical effectiveness of early stages nursing protocol for deep venous thrombosis of patients and nurses. A three-dimensional shape model of the user interface is shown the examined vessels, which have compression measurements mapped to the surface as colors and virtual image plane representation of DVT. The measures of comprehension have been validated using HML model segmentation experts and contrasted with paired f-tests to reduce the incidence of lower extremity deep venous thrombosis in patients and nurses.

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“…As m-Health becomes popular, countries provide increased resources to this sector, benefiting society and communities become more health aware. Instead of expensive medical interventions and hospitalizations, this promotes health and quality of life (Jutinico et al, 2019).…”

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confidence: 99%

Mobile healthcare (m‐Health) based on artificial intelligence in healthcare 4.0

et al. 2022

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Healthcare 4.0 is about collecting huge amounts of data and getting it to work in applications, enabling healthcare management decisions well‐informed while providing for important gains in effectiveness and cost control. Diagnostics based on the digital footprint depend on wearable technology's ability to gather and extract essential patient data. Artificial intelligence (AI) technologies allow the analysis of real‐time observed data and continuously developing from data to understand the world surrounding them. To connect and access intelligent healthcare services, people, and devices at any time, a secure wireless mobile communication system is essential. This article suggests a mHealth‐based patient monitoring system (mHealth‐PMS) based on AI for healthcare 4.0. Mobile healthcare applications motorized by Convolutional Neural Network (CNN) have enabled people to triage their conditions and preemptive treatment decisions. Information collected has been analysed for substantiating cause, and alert and preventive messages have been immediately sent through the mobile application. The performance analysis has been executed, and the proposed mobile application‐based surveillance provided much‐enhanced reporting of information quickly on diseases, symptoms, factors, and more. The mHealth‐PMS strategy shows an accuracy ratio of 95.6%, monitoring ratio of 93.5%, data management ratio of 94.4%, data security ratio of 91.7%, data privacy ratio of 92.1%, prediction ratio of 95.3%, a cost‐effective ratio of 25.5% compared to the existing methods.

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Natural language interface model for the evaluation of ergonomic routines in occupational health (ILENA)

Cited by 19 publications

References 14 publications

Combining GCN and Transformer for Chinese Grammatical Error Detection

Combining GCN and Transformer for Chinese Grammatical Error Detection

Effect Analysis of Nursing Intervention on Lower Extremity Deep Venous Thrombosis in Patients

Mobile healthcare (m‐Health) based on artificial intelligence in healthcare 4.0

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