Web System Prototype based on speech recognition to construct medical reports in Brazilian Portuguese

Toledo, Thiago Ferreira de; Lee, Huei; Spolaôr, Newton; Coy, Cláudio Saddy Rodrigues; Wu, Feng

doi:10.1016/j.ijmedinf.2018.10.010

Cited by 8 publications

(3 citation statements)

References 5 publications

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Section: Resultsmentioning

confidence: 99%

“…Second, NLP is also used in clinical/biomedical text mining for medical report construction [ 58 ], Web-based real-time case finding [ 36 ], and Web blog analysis [ 13 ]. Specifically, a Web system [ 58 ] developed based on the Google Web Speech API and Microsoft Bing Speech API generated medical reports via automatic speech recognition. A Web-based NLP diabetes case-finding method [ 36 ] developed based on structured and unstructured electronic medical records was proven effective in “identifying uncodified diabetes cases in real-time, leading to a significant improvement in diabetes case finding and a complete ascertainment of diagnoses of diabetes mellitus (p. 1)”.…”

Section: Resultsmentioning

confidence: 99%

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Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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“…SVRT can also be used to manage video figures, including human postures and skeletons [3,4]. Several studies have found that current speech recognition technology has high levels of efficacy, accuracy and usability with respect to medical records [5,6].…”

Section: Introductionmentioning

confidence: 99%

How do people think about the implementation of speech and video recognition technology in emergency medical practice?

et al. 2022

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Background Recently, speech and video information recognition technology (SVRT) has developed rapidly. Introducing SVRT into the emergency medical practice process may lead to improvements in health care. The purpose of this study was to evaluate the level of acceptance of SVRT among patients, caregivers and emergency medical staff. Methods Structured questionnaires were developed for the patient or caregiver group and the emergency medical staff group. The survey was performed in one tertiary academic hospital emergency department. Questions were optimized for each specific group, and responses were provided mostly using Likert 5-scales. Additional multivariable logistic regression analyses for the whole cohort and subgroups were conducted to calculate odds ratios (OR) and confidence intervals (CI) to examine the association between individual characteristics and SVRT acceptance. Results Of 264 participants, respondents demonstrated a positive attitude and acceptance toward SVRT and artificial intelligence (AI) in future; 179 (67.8%) for video recordings, and 190 (72.0%) for speech recordings. A multivariable logistic regression model revealed that several factors were associated with acceptance of SVRT in emergency medical practice: belief in health care improvement by signal analysis technology (OR, 95% CIs: 2.48 (1.15–5.42)) and AI (OR, 95% CIs: 1.70 (0.91–3.17)), reliability of AI application in emergency medicine (OR, 95% CIs: 2.36 (1.28–4.35)) and the security of personal information (OR, 95% CIs: 1.98 (1.10–3.63)). Conclusion A high level of acceptance toward SVRT has been shown in patients or caregivers, and it also appears to be associated with positive attitudes toward new technology, AI and security of personal information.

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