DBOS: A Dialog-Based Object Query System for Hospital Nurses

Chu, Edward T.-H.; Huang, Zi-Zhe

doi:10.3390/s20226639

Cited by 6 publications

(5 citation statements)

References 27 publications

(31 reference statements)

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“…A successful example is to provide relevant information inside clinical departments [126]. Chu et al developed a Question-and-Answer (QA) system for hospital staff to inform the location of mobile medical equipment (electrocardiography machines), moving around a hospital [127].…”

Section: Future Nlp and Conclusionmentioning

confidence: 99%

Natural Language Processing: from Bedside to Everywhere

et al. 2022

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Objectives: Owing to the rapid progress of natural language processing (NLP), the role of NLP in the medical field has radically gained considerable attention from both NLP and medical informatics. Although numerous medical NLP papers are published annually, there is still a gap between basic NLP research and practical product development. This gap raises questions, such as what has medical NLP achieved in each medical field, and what is the burden for the practical use of NLP? This paper aims to clarify the above questions. Methods: We explore the literature on potential NLP products/services applied to various medical/clinical/healthcare areas. Results: This paper introduces clinical applications (bedside applications), in which we introduce the use of NLP for each clinical department, internal medicine, pre-surgery, post-surgery, oncology, radiology, pathology, psychiatry, rehabilitation, obstetrics, and gynecology. Also, we clarify technical problems to be addressed for encouraging bedside applications based on NLP. Conclusions: These results contribute to discussions regarding potentially feasible NLP applications and highlight research gaps for future studies.

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Section: Future Nlp and Conclusionmentioning

confidence: 99%

Natural Language Processing: from Bedside to Everywhere

et al. 2022

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“…"), it needs to be transformed into the intermediate language {'object': '温度'('temperature'), 'location': '卧室'('bedroom')}. Then fill the sketch with the key-value parameter, complete the parameter mapping and fill it into the parameter list, as shown in the above-structured instruction can be converted to [5,7], as shown in the blue dashed line. And fill the parameters cnlNum and viewID into the HTTP address of the SCADA system, and realize real-time data query by calling the HTML monitoring data interface of address link.…”

Section: A Tf-idf+cos_sim Based Semantic Parsing Algorithm 1) Semantic Parsing Flow Of Tf-idf+cos_simmentioning

confidence: 99%

“…)", it needs to be transformed into the intermediate language {'object': '温度', 254 'location': '卧室 '}. Then fill the sketch with the key-value parameter, complete the parameter mapping and fill it into 255 the parameter list, as shown in the above-structured instruction can be converted to [5,7], as shown in the blue dashed 256 Step…”

Section: B Semantic Parsing Algorithm Based On Dependency Syntactic Analysis 1) the Semantic Parsing Process Of Dependent Syntactic Analymentioning

confidence: 99%

“…The rule-based approach uses information such as keywords to form rules to detect user intent. Chu, E.T.H et al [7] proposed a dialoguebased object query system, which is combined with cosine similarity and TF-IDF to determine user intent. Regarding intent classification based on query click logs, it is mostly used for business scenarios such as search engine, and user intention can be obtained by clicking log.…”

Section: Introductionmentioning

confidence: 99%

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SCADA-NLI: A Natural Language Query and Control Interface for Distributed Systems

Shen

et al. 2021

IEEE Access

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Human-Computer natural language interaction is helpful to reduce the operation and maintenance cost of the SCADA system, and it is necessary to solve the complex natural language interface problem that supports data query and real-time control. According to the complexity of natural language instructions, a hierarchical classification of semantic parsing algorithm is adopted. Firstly, the KWECS method is used to classify the intent of natural language instruction, then the TF-IDF keyword extraction algorithm combined with the cosine similarity is used to structure the key-value of the classified natural language instructions which was used into SCADA control intermediate language and then formally converted into actual control or query instruction. If the analysis fails, the complex control and query instruction analysis are carried out according to the classification results, structuring instruction parsing based on dependency parsing and SQL natural language parsing based on deep learning are adopted respectively to implement real-time control interface and database query interface. Our experimental results show that the proposed hierarchical classification of natural language comprehensive query and control interface can better solve the problem of human-computer natural language interaction in the SCADA system, and the accuracy of intent recognition reaches 96.5%. In more detail, the accuracy, precision, recall, and F-score of instruction parsing reach 88.47%, 90.21%, 89.48%, and 89.72% respectively. Especially, it provides more convenient interactive means for industrial and agricultural information management and control.INDEX TERMS Natural language interface, intent classification, semantic parsing, scada systems, humancomputer interaction.

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“…There are two types of text compactions that are most popular. First, extractive techniques extract important sentences from document texts and combine them into a summary [5], [6]. Usually, the resulting sentences are arranged the same as the sentences in the original document.…”

Section: Introductionmentioning

confidence: 99%

Extractive text summarization for scientific journal articles using long short-term memory and gated recurrent units

Fitrianah

Jauhari

2022

Bulletin EEI

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Along with the increasing number of scientific publications, many scientific communities must read the entire text to get the essence of information from a journal article. This will be quite inconvenient if the scientific journal article is quite long and there are more than one journals. Motivated by this problem, encourages the need for a method of text summarization that can automatically, concisely, and accurately summarize a scientific article document. The purpose of this research is to create an extractive text summarization by doing feature engineering to extract the semantic information from the original text. Comparing the long short-term memory algorithm and gated recurrent units and were used to get the most relevant sentences to be served as a summary. The results showed that both algorithms yielded relatively similar accuracy results, with gated recurrent units at 98.40% and long short-term memory at 98.68%. The evaluation method with matrix recall-oriented understudy for gisting evaluation (ROUGE) is used to evaluate the summary results. The summary results produced by the LSTM model compared to the summary results using the latent semantic analysis (LSA) method were then obtained recall values at ROUGE-1, ROUGE-2, and ROUGE-L respectively were 76.25%, 59.49%, and 72.72%.

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DBOS: A Dialog-Based Object Query System for Hospital Nurses

Cited by 6 publications

References 27 publications

Natural Language Processing: from Bedside to Everywhere

Natural Language Processing: from Bedside to Everywhere

SCADA-NLI: A Natural Language Query and Control Interface for Distributed Systems

Extractive text summarization for scientific journal articles using long short-term memory and gated recurrent units

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