scholarly journals Thalamic but Not Subthalamic Neuromodulation Simplifies Word Use in Spontaneous Language

2021 ◽  
Vol 15 ◽  
Author(s):  
Hannes Ole Tiedt ◽  
Felicitas Ehlen ◽  
Michelle Wyrobnik ◽  
Fabian Klostermann
Keyword(s):  
Word Frequency ◽  
Language Processing ◽  
Word Use ◽  
Spontaneous Language ◽  
Thalamic Stimulation ◽  
Language Tests ◽  
Ventral Intermediate Nucleus ◽  
Open Class ◽  
The Impact ◽  
Stn Dbs

Several investigations have shown language impairments following electrode implantation surgery for Deep Brain Stimulation (DBS) in movement disorders. The impact of the actual stimulation, however, differs between DBS targets with further deterioration in formal language tests induced by thalamic DBS in contrast to subtle improvement observed in subthalamic DBS. Here, we studied speech samples from interviews with participants treated with DBS of the thalamic ventral intermediate nucleus (VIM) for essential tremor (ET), or the subthalamic nucleus (STN) for Parkinson’s disease (PD), and healthy volunteers (each n = 13). We analyzed word frequency and the use of open and closed class words. Active DBS increased word frequency in case of VIM, but not STN stimulation. Further, relative to controls, both DBS groups produced fewer open class words. Whereas VIM DBS further decreased the proportion of open class words, it was increased by STN DBS. Thus, VIM DBS favors the use of relatively common words in spontaneous language, compatible with the idea of lexical simplification under thalamic stimulation. The absence or even partial reversal of these effects in patients receiving STN DBS is of interest with respect to biolinguistic concepts suggesting dichotomous thalamic vs. basal ganglia roles in language processing.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

scholarly journals Evaluating Cultural Impact in Discursive Space through Digital Footprints

2021 ◽  
Vol 13 (7) ◽  
pp. 4043 ◽  
Author(s):  
Jesús López Baeza ◽  
Jens Bley ◽  
Kay Hartkopf ◽  
Martin Niggemann ◽  
James Arias ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Social Activity ◽  
Test Site ◽  
Cultural Discourse ◽  
Discursive Space ◽  
Topic Identification ◽  
Small Test ◽  
The Impact ◽  
The City

The research presented in this paper describes an evaluation of the impact of spatial interventions in public spaces, measured by social media data. This contribution aims at observing the way a spatial intervention in an urban location can affect what people talk about on social media. The test site for our research is Domplatz in the center of Hamburg, Germany. In recent years, several actions have taken place there, intending to attract social activity and spotlight the square as a landmark of cultural discourse in the city of Hamburg. To evaluate the impact of this strategy, textual data from the social networks Twitter and Instagram (i.e., tweets and image captions) are collected and analyzed using Natural Language Processing intelligence. These analyses identify and track the cultural topic or “people talking about culture” in the city of Hamburg. We observe the evolution of the cultural topic, and its potential correspondence in levels of activity, with certain intervention actions carried out in Domplatz. Two analytic methods of topic clustering and tracking are tested. The results show a successful topic identification and tracking with both methods, the second one being more accurate. This means that it is possible to isolate and observe the evolution of the city’s cultural discourse using NLP. However, it is shown that the effects of spatial interventions in our small test square have a limited local scale, rather than a city-wide relevance.

Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Mental Health Problems ◽  
Well Being ◽  
Research Activity ◽  
Health Crisis ◽  
Key Topics ◽  
The Uk ◽  
The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

Motor Symptom Asymmetry Predicts Non-motor Outcome and Quality of Life Following STN DBS in Parkinson's Disease

2021 ◽  
Author(s):  
Julie Péron ◽  
Philippe Voruz ◽  
Jordan Pierce ◽  
Kévin Ahrweiller ◽  
Claire Haegelen ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Symptom ◽  
Control Group ◽  
Motor Symptoms ◽  
Healthy Control ◽  
The Impact ◽  
Stn Dbs

Abstract Risk factors for long-term non-motor disorders and quality of life following subthalamic nucleus deep-brain stimulation (STN DBS) have not yet been fully identified. In the present study, we investigated the impact of motor symptom asymmetry in Parkinson’s disease.Data were extracted for 52 patients with Parkinson’s disease (half with left-sided motor symptoms and half with right-sided ones) who underwent bilateral STN and a matched healthy control group. Performances for cognitive tests and neuropsychiatric and quality-of-life questionnaires at 12 months post-DBS were compared with a pre-DBS baseline. Results indicated a deterioration in cognitive performance post-DBS in patients with left-sided motor symptoms. Performances of patients with right-sided motor symptoms were maintained, except for a verbal executive task. These differential effects had an impact on patients’ quality of life. The results highlight the existence of two distinct cognitive profiles of Parkinson’s disease, depending on motor symptom asymmetry. This asymmetry is a potential risk factor for non-motor adverse effects following STN DBS.

Abstract P253: Proton Pump Inhibitor Use is Positively Associated with Incidence of Cardiovascular Disease

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Elizabeth J Bell ◽  
Jennifer L St. Sauver ◽  
Veronique L Roger ◽  
Nicholas B Larson ◽  
Hongfang Liu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Cardiovascular Disease ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Language Processing ◽  
Proton Pump ◽  
Hazard Ratio ◽  
Time Varying ◽  
Increased Risk ◽  
The Impact

Introduction: Proton pump inhibitors (PPIs) are used by an estimated 29 million Americans. PPIs increase the levels of asymmetrical dimethylarginine, a known risk factor for cardiovascular disease (CVD). Data from a select population of patients with CVD suggest that PPI use is associated with an increased risk of stroke, heart failure, and coronary heart disease. The impact of PPI use on incident CVD is largely unknown in the general population. Hypothesis: We hypothesized that PPI users have a higher risk of incident total CVD, coronary heart disease, stroke, and heart failure compared to nonusers. To demonstrate specificity of association, we additionally hypothesized that there is not an association between use of H 2 -blockers - another commonly used class of medications with similar indications as PPIs - and CVD. Methods: We used the Rochester Epidemiology Project’s medical records-linkage system to identify all residents of Olmsted County, MN on our baseline date of January 1, 2004 (N=140217). We excluded persons who did not grant permission for their records to be used for research, were <18 years old, had a history of CVD, had missing data for any variable included in our model, or had evidence of PPI use within the previous year.We followed our final cohort (N=58175) for up to 12 years. The administrative censoring date for CVD was 1/20/2014, for coronary heart disease was 8/3/2016, for stroke was 9/9/2016, and for heart failure was 1/20/2014. Time-varying PPI ever-use was ascertained using 1) natural language processing to capture unstructured text from the electronic health record, and 2) outpatient prescriptions. An incident CVD event was defined as the first occurrence of 1) validated heart failure, 2) validated coronary heart disease, or 3) stroke, defined using diagnostic codes only. As a secondary analysis, we calculated the association between time-varying H 2 -blocker ever-use and CVD among persons not using H 2 -blockers at baseline. Results: After adjustment for age, sex, race, education, hypertension, hyperlipidemia, diabetes, and body-mass-index, PPI use was associated with an approximately 50% higher risk of CVD (hazard ratio [95% CI]: 1.51 [1.37-1.67]; 2187 CVD events), stroke (hazard ratio [95% CI]: 1.49 [1.35-1.65]; 1928 stroke events), and heart failure (hazard ratio [95% CI]: 1.56 [1.23-1.97]; 353 heart failure events) compared to nonusers. Users of PPIs had a 35% greater risk of coronary heart disease than nonusers (95% CI: 1.13-1.61; 626 coronary heart disease events). Use of H 2 -blockers was also associated with a higher risk of CVD (adjusted hazard ratio [95% CI]: 1.23 [1.08-1.41]; 2331 CVD events). Conclusions: PPI use is associated with a higher risk of CVD, coronary heart disease, stroke and heart failure. Use of a drug with no known cardiac toxicity - H 2 -blockers - was also associated with a greater risk of CVD, warranting further study.

The national impact of the COVID-19 pandemic on U.S. prostate cancer community care.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 5061-5061
Author(s):  
Matthew R. Cooperberg ◽  
Paul Brendel ◽  
Daniel J. Lee ◽  
Rahul Doraiswami ◽  
Hariesh Rajasekar ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Language Processing ◽  
Care Delivery ◽  
Specific Antigen ◽  
Risk Groups ◽  
Low Risk ◽  
T Stage ◽  
Risk Stratum ◽  
The Impact

5061 Background: We used data from a specialty-wide, community-based urology registry to determine trends in outpatient prostate cancer (PCa) care during the COVID-19 pandemic. Methods: 3,165 (̃ 25%) of US urology providers, representing 48 states and territories, participate in the American Urological Association Quality (AQUA) Registry, which collects data via automated extraction from electronic health record systems. We analyzed trends in PCa care delivery from 156 practices contributing data in 2019 and 2020. Risk stratification was based on prostate-specific antigen (PSA) at diagnosis, biopsy Gleason, and clinical T-stage, and we used a natural language processing algorithm to determine Gleason and T-stage from unstructured clinical notes. The primary outcome was mean weekly visit volume by PCa patients per practice (visits defined as all MD and mid-level visits, telehealth and face-to-face), and we compared each week in 2020 through week 44 (November 1) to the corresponding week in 2019. Results: There were 267,691 PCa patients in AQUA who received care between 2019 and 2020. From mid-March to early November, 2020 (week 10 – week 44) the magnitude of the decline and recovery varied by risk stratum, with the steepest drops for low-risk PCa (Table). For 2020, overall mean visits per day (averaged weekly) were similar to 2019 for the first 9 weeks (̃25). Visits declined to week 14 (18.19; a 31% drop from 2019), recovered to 2019 levels by week 23, and declined steadily to 11.89 (a 58% drop from 2019) as of week 44, the cut off of this analysis. Conclusions: Access to care for men with PCa was sharply curtailed by the COVID-19 pandemic, and while the impact was less for men with high-risk disease compared to those with low-risk disease, visits even for high-risk individuals were down nearly one-third and continued to fall through November. This study provides real-world evidence on the magnitude of decline in PCa care across risk groups. The impact of this decline on cancer outcomes should be followed closely.[Table: see text]

scholarly journals Measures of speech rhythm and the role of corpus-based word frequency: a multifactorial comparison of Spanish(-English) speakers

2011 ◽  
Vol 11 (2) ◽  
pp. 1 ◽  
Author(s):  
Michael J. Harris ◽  
Stefan Th. Gries
Keyword(s):  
Word Frequency ◽  
Binary Logistic Regression ◽  
Spanish Speakers ◽  
English Speakers ◽  
Vowel Duration ◽  
Mexican Spanish ◽  
Speech Rhythm ◽  
Multiple Metrics ◽  
Main Effects ◽  
The Impact

In this study, we address various measures that have been employed to distinguish between syllable and stress- timed languages. This study differs from all previous ones by (i) exploring and comparing multiple metrics within a quantitative and multifactorial perspective and by (ii) also documenting the impact of corpus-based word frequency. We begin with the basic distinctions of speech rhythms, dealing with the differences between syllable-timed languages and stress-timed languages and several methods that have been used to attempt to distinguish between the two. We then describe how these metrics were used in the current study comparing the speech rhythms of Mexican Spanish speakers and bilingual English/Spanish speakers (speakers born to Mexican parents in California). More specifically, we evaluate how well various metrics of vowel duration variability as well as the so far understudied factor of corpus-based frequency allow to classify speakers as monolingual or bilingual. A binary logistic regression identifies several main effects and interactions. Most importantly, our results call the utility of a particular rhythm metric, the PVI, into question and indicate that corpus data in the form of lemma frequencies interact with two metrics of durational variability, suggesting that durational variability metrics should ideally be studied in conjunction with corpus-based frequency data.

scholarly journals MELHISSA: a multilingual entity linking architecture for historical press articles

2021 ◽  
Author(s):  
Elvys Linhares Pontes ◽  
Luis Adrián Cabrera-Diego ◽  
Jose G. Moreno ◽  
Emanuela Boros ◽  
Ahmed Hamdi ◽  
...  
Keyword(s):  
Language Processing ◽  
Digital Libraries ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Historical Documents ◽  
Entity Linking ◽  
Named Entities ◽  
European Languages ◽  
Meta Information ◽  
The Impact

AbstractDigital libraries have a key role in cultural heritage as they provide access to our culture and history by indexing books and historical documents (newspapers and letters). Digital libraries use natural language processing (NLP) tools to process these documents and enrich them with meta-information, such as named entities. Despite recent advances in these NLP models, most of them are built for specific languages and contemporary documents that are not optimized for handling historical material that may for instance contain language variations and optical character recognition (OCR) errors. In this work, we focused on the entity linking (EL) task that is fundamental to the indexation of documents in digital libraries. We developed a Multilingual Entity Linking architecture for HIstorical preSS Articles that is composed of multilingual analysis, OCR correction, and filter analysis to alleviate the impact of historical documents in the EL task. The source code is publicly available. Experimentation has been done over two historical documents covering five European languages (English, Finnish, French, German, and Swedish). Results have shown that our system improved the global performance for all languages and datasets by achieving an F-score@1 of up to 0.681 and an F-score@5 of up to 0.787.

scholarly journals Neural markers of speech comprehension: measuring EEG tracking of linguistic speech representations, controlling the speech acoustics

2021 ◽  
Author(s):  
Marlies Gillis ◽  
Jonas Vanthornhout ◽  
Jonathan Z Simon ◽  
Tom Francart ◽  
Christian Brodbeck
Keyword(s):  
Word Frequency ◽  
Language Processing ◽  
Speech Intelligibility ◽  
Acoustic Properties ◽  
Speech Comprehension ◽  
Speech Acoustics ◽  
Brain Responses ◽  
Cortical Responses ◽  
Linguistic Representations ◽  
Acoustic Processing

When listening to speech, brain responses time-lock to acoustic events in the stimulus. Recent studies have also reported that cortical responses track linguistic representations of speech. However, tracking of these representations is often described without controlling for acoustic properties. Therefore, the response to these linguistic representations might reflect unaccounted acoustic processing rather than language processing. Here we tested several recently proposed linguistic representations, using audiobook speech, while controlling for acoustic and other linguistic representations. Indeed, some of these linguistic representations were not significantly tracked after controlling for acoustic properties. However, phoneme surprisal, cohort entropy, word surprisal and word frequency were significantly tracked over and beyond acoustic properties. Additionally, these linguistic representations are tracked similarly across different stories, spoken by different readers. Together, this suggests that these representations characterize processing of the linguistic content of speech and might allow a behaviour-free evaluation of the speech intelligibility.

scholarly journals Association Between Patient Survival and Clinician Variability in Treatment Rates for Aortic Valve Stenosis

2021 ◽  
Author(s):  
J. Matthew Brennan ◽  
Angela Lowenstern ◽  
Paige Sheridan ◽  
Isabel J. Boero ◽  
Vinod H. Thourani ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Language Processing ◽  
Severe Aortic Stenosis ◽  
The United States ◽  
Risk Models ◽  
Transcatheter Aortic Valve ◽  
Competing Risk Models ◽  
The Impact ◽  
Potential Challenge

Background Patients with symptomatic severe aortic stenosis (ssAS) have a high mortality risk and compromised quality of life. Surgical/transcatheter aortic valve replacement (AVR) is a Class I recommendation, but it is unclear if this recommendation is uniformly applied. We determined the impact of managing cardiologists on the likelihood of ssAS treatment. Methods and Results Using natural language processing of Optum electronic health records, we identified 26 438 patients with newly diagnosed ssAS (2011–2016). Multilevel, multivariable Fine‐Gray competing risk models clustered by cardiologists were used to determine the impact of cardiologists on the likelihood of 1‐year AVR treatment. Within 1 year of diagnosis, 35.6% of patients with ssAS received an AVR; however, rates varied widely among managing cardiologists (0%, lowest quartile; 100%, highest quartile [median, 29.6%; 25th–75th percentiles, 13.3%–47.0%]). The odds of receiving AVR varied >2‐fold depending on the cardiologist (median odds ratio for AVR, 2.25; 95% CI, 2.14–2.36). Compared with patients with ssAS of cardiologists with the highest treatment rates, those treated by cardiologists with the lowest AVR rates experienced significantly higher 1‐year mortality (lowest quartile, adjusted hazard ratio, 1.22, 95% CI, 1.13–1.33). Conclusions Overall AVR rates for ssAS were low, highlighting a potential challenge for ssAS management in the United States. Cardiologist AVR use varied substantially; patients treated by cardiologists with lower AVR rates had higher mortality rates than those treated by cardiologists with higher AVR rates.

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