scholarly journals CANDICE A1: Measuring language lateralisation with different language tasks: A systematic review

2017 ◽  
Author(s):  
Abigail Bradshaw ◽  
Paul Andrew Thompson ◽  
Alexander Wilson ◽  
Dorothy Vera Margaret Bishop ◽  
Zoe Victoria Joan Woodhead
Keyword(s):  
Systematic Review ◽  
Language Processing ◽  
Hemispheric Specialisation ◽  
Methodological Considerations

This is a preprint of a systematic review that aims to summarise research using different language tasks to measure laterality for different components of language processing. Implications of this research for models of hemispheric specialisation for language are discussed, as well as the need to consider how different aspects of language processing can show different patterns of lateralisation both across and within individuals. A protocol for this review can be found at https://osf.io/hyvc4/. This work was published in two peer-reviewed publications:Bradshaw AR, Bishop DVM, Woodhead ZVJ (2017). Methodological considerations in assessment of language lateralisation with fMRI: a systematic review. PeerJ 5:e3557 https://doi.org/10.7717/peerj.3557.Bradshaw AR, Thompson PA, Wilson AC, Bishop DV, Woodhead ZVJ (2017). Measuring language lateralisation with different language tasks: A systematic review. PeerJ 5:e3929 https://doi.org/10.7717/peerj.3929.

scholarly journals Is motor inhibition involved in the processing of sentential negation? An assessment via the Stop-Signal Task

2021 ◽  
Author(s):  
Martina Montalti ◽  
Marta Calbi ◽  
Valentina Cuccio ◽  
Maria Alessandra Umiltà ◽  
Vittorio Gallese
Keyword(s):  
Reaction Time ◽  
Language Processing ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Motor Inhibition ◽  
Good Tool ◽  
Scientific Debate ◽  
Sentential Negation ◽  
Stop Signal Reaction Time ◽  
Methodological Considerations

AbstractIn the last decades, the embodied approach to cognition and language gained momentum in the scientific debate, leading to evidence in different aspects of language processing. However, while the bodily grounding of concrete concepts seems to be relatively not controversial, abstract aspects, like the negation logical operator, are still today one of the main challenges for this research paradigm. In this framework, the present study has a twofold aim: (1) to assess whether mechanisms for motor inhibition underpin the processing of sentential negation, thus, providing evidence for a bodily grounding of this logic operator, (2) to determine whether the Stop-Signal Task, which has been used to investigate motor inhibition, could represent a good tool to explore this issue. Twenty-three participants were recruited in this experiment. Ten hand-action-related sentences, both in affirmative and negative polarity, were presented on a screen. Participants were instructed to respond as quickly and accurately as possible to the direction of the Go Stimulus (an arrow) and to withhold their response when they heard a sound following the arrow. This paradigm allows estimating the Stop Signal Reaction Time (SSRT), a covert reaction time underlying the inhibitory process. Our results show that the SSRT measured after reading negative sentences are longer than after reading affirmative ones, highlighting the recruitment of inhibitory mechanisms while processing negative sentences. Furthermore, our methodological considerations suggest that the Stop-Signal Task is a good paradigm to assess motor inhibition’s role in the processing of sentence negation.

scholarly journals Machine Learning in Medical Emergencies: a Systematic Review and Analysis

2021 ◽  
Vol 45 (10) ◽  
Author(s):  
Inés Robles Mendo ◽  
Gonçalo Marques ◽  
Isabel de la Torre Díez ◽  
Miguel López-Coronado ◽  
Francisco Martín-Rodríguez
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Language Processing ◽  
Emergency Services ◽  
Clinical Decision ◽  
Quality Of Healthcare ◽  
The Other ◽  
Other Hand ◽  
Increasing Demand ◽  
Commercial Applications

AbstractDespite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

scholarly journals Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

2021 ◽  
Vol 28 (1) ◽  
pp. e100262
Author(s):  
Mustafa Khanbhai ◽  
Patrick Anyadi ◽  
Joshua Symons ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Patient Experience ◽  
Language Processing ◽  
Performance Metrics ◽  
Free Text ◽  
Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

Systematic review of current natural language processing methods and applications in cardiology

Heart ◽  
2021 ◽  
pp. heartjnl-2021-319769
Author(s):  
Meghan Reading Turchioe ◽  
Alexander Volodarskiy ◽  
Jyotishman Pathak ◽  
Drew N Wright ◽  
James Enlou Tcheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Care ◽  
Real World Data ◽  
Clinical Text ◽  
Clinical Notes ◽  
Artery Disease ◽  
Automated Methods

Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clinical care that may be used to improve outcomes and understanding of disease in cardiology. The purpose of this systematic review is to provide an understanding of NLP, review how it has been used to date within cardiology and illustrate the opportunities that this approach provides for both research and clinical care. We systematically searched six scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, PubMed and Scopus) for studies published in 2015–2020 describing the development or application of NLP methods for clinical text focused on cardiac disease. Studies not published in English, lacking a description of NLP methods, non-cardiac focused and duplicates were excluded. Two independent reviewers extracted general study information, clinical details and NLP details and appraised quality using a checklist of quality indicators for NLP studies. We identified 37 studies developing and applying NLP in heart failure, imaging, coronary artery disease, electrophysiology, general cardiology and valvular heart disease. Most studies used NLP to identify patients with a specific diagnosis and extract disease severity using rule-based NLP methods. Some used NLP algorithms to predict clinical outcomes. A major limitation is the inability to aggregate findings across studies due to vastly different NLP methods, evaluation and reporting. This review reveals numerous opportunities for future NLP work in cardiology with more diverse patient samples, cardiac diseases, datasets, methods and applications.

A systematic review of unsupervised approaches to grammar induction

2020 ◽  
pp. 1-43
Author(s):  
Vigneshwaran Muralidaran ◽  
Irena Spasić ◽  
Dawn Knight
Keyword(s):  
Systematic Review ◽  
Computational Linguistics ◽  
Language Processing ◽  
Experimental Studies ◽  
Formal System ◽  
Grammar Induction ◽  
Learning Evaluation ◽  
Analysis Theory ◽  
Research Questions ◽  
Implementation Methodology

Abstract This study systematically reviews existing approaches to unsupervised grammar induction in terms of their theoretical underpinnings, practical implementations and evaluation. Our motivation is to identify the influence of functional-cognitive schools of grammar on language processing models in computational linguistics. This is an effort to fill any gap between the theoretical school and the computational processing models of grammar induction. Specifically, the review aims to answer the following research questions: Which types of grammar theories have been the subjects of grammar induction? Which methods have been employed to support grammar induction? Which features have been used by these methods for learning? How were these methods evaluated? Finally, in terms of performance, how do these methods compare to one another? Forty-three studies were identified for systematic review out of which 33 described original implementations of grammar induction; three provided surveys and seven focused on theories and experiments related to acquisition and processing of grammar in humans. The data extracted from the 33 implementations were stratified into 7 different aspects of analysis: theory of grammar; output representation; how grammatical productivity is processed; how grammatical productivity is represented; features used for learning; evaluation strategy and implementation methodology. In most of the implementations considered, grammar was treated as a generative-formal system, autonomous and independent of meaning. The parser decoding was done in a non-incremental, head-driven fashion by assuming that all words are available for the parsing model and the output representation of the grammar learnt was hierarchical, typically a dependency or a constituency tree. However, the theoretical and experimental studies considered suggest that a usage-based, incremental, sequential system of grammar is more appropriate than the formal, non-incremental, hierarchical view of grammar. This gap between the theoretical as well as experimental studies on one hand and the computational implementations on the other hand should be addressed to enable further progress in computational grammar induction research.

scholarly journals Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

2020 ◽  
Vol 78 (4) ◽  
pp. 1547-1574
Author(s):  
Sofia de la Fuente Garcia ◽  
Craig W. Ritchie ◽  
Saturnino Luz
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Processing ◽  
Future Research ◽  
Original Research ◽  
Speech And Language ◽  
Eligibility Criteria ◽  
Speech And Language Processing

Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

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