Tracking group identity through natural language within groups.
Ontology highlight
ABSTRACT:
SUBMITTER: Ashokkumar A
PROVIDER: S-EPMC9229362 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:OBJECTIVE:To describe differences in patient experiences of hospital care by preferred language within racial/ethnic groups. DATA SOURCE:2014-2015 HCAHPS survey data. STUDY DESIGN:We compared six composite measures for seven languages (English, Spanish, Russian, Portuguese, Chinese, Vietnamese, and Other) within applicable subsets of five racial/ethnic groups (Hispanics, Asian/Pacific Islanders, American Indian/Alaska Natives, Blacks, and Whites). We measured patient-mix adjusted overall, between- and within-hospital differences in patient experience by language, using linear regression. DATA COLLECTION METHODS:Surveys from 5 480 308 patients discharged from 4517 hospitals 2014-2015. PRINCIPAL FINDINGS:Within each racial/ethnic group, mean reported experiences for non-English-preferring patients were almost always worse than their English-preferring counterparts. Language differences were largest and most consistent for Care Coordination. Within-hospital differences by language were often larger than between-hospital differences and were largest for Care Coordination. Where between-hospital differences existed, non-English-preferring patients usually attended hospitals whose average patient experience scores for all patients were lower than the average scores for the hospitals of their English-preferring counterparts. CONCLUSIONS:Efforts should be made to increase access to better hospitals for language minorities and improve care coordination and other facets of patient experience in hospitals with high proportions of non-English-preferring patients, focusing on cultural competence and language-appropriate services.
| S-EPMC6341216 | biostudies-literature
Project description:BackgroundThe processes leading to the diversity of over 7000 present-day languages have been the subject of scholarly interest for centuries. Several factors have been suggested to contribute to the spatial segregation of speaker populations and the subsequent linguistic divergence. However, their formal testing and the quantification of their relative roles is still missing. We focussed here on the early stages of the linguistic divergence process, that is, the divergence of dialects, with a special focus on the ecological settings of the speaker populations. We adopted conceptual and statistical approaches from biological microevolution and parallelled intra-lingual variation with genetic variation within a species. We modelled the roles of geographical distance, differences in environmental and cultural conditions and in administrative history on linguistic divergence at two different levels: between municipal dialects (cf. in biology, between individuals) and between dialect groups (cf. in biology, between populations).ResultsWe found that geographical distance and administrative history were important in separating municipal dialects. However, environmental and cultural differences contributed markedly to the divergence of dialect groups. In biology, increase in genetic differences between populations together with environmental differences may suggest genetic differentiation of populations through adaptation to the local environment. However, our interpretation of this result is not that language itself adapts to the environment. Instead, it is based on Homo sapiens being affected by its environment, and its capability to adapt culturally to various environmental conditions. The differences in cultural adaptations arising from environmental heterogeneity could have acted as nonphysical barriers and limited the contacts and communication between groups. As a result, linguistic differentiation may emerge over time in those speaker populations which are, at least partially, separated.ConclusionsGiven that the dialects of isolated speaker populations may eventually evolve into different languages, our result suggests that cultural adaptation to local environment and the associated isolation of speaker populations have contributed to the emergence of the global patterns of linguistic diversity.
| S-EPMC6122686 | biostudies-literature
Project description:Group living animals often engage in corporate territorial defence. Territorial group vocalizations can provide information about group identity, size and composition. Neighbouring groups may use this information to avoid unfavourable direct conflicts. Giant otters are highly social and territorial animals with an elaborate vocal repertoire. They produce long-range screams when they are alert or excited, i.e. in an alarm, isolation or begging context. Long-range screams are not only produced by one individual at a time ('single screams') but also by multiple group members simultaneously, resulting in a highly conspicuous 'group chorus'. Wild giant otters regularly produce group choruses during interactions with predators, when they detect intruders in their territory or before group reunions after separation. Since single screams and especially group choruses probably contribute to the groups' corporate territorial defence, we hypothesized that group identity is encoded in single screams and group choruses. We analysed vocalizations from five wild and three captive giant otter groups and found statistical evidence for a group signature in group choruses. Results for single screams were less conclusive, which might have been caused by the comparatively lower sample size. We suggest that giant otters may gain information on group identity by listening to group choruses. Group identity likely constitutes important social information for giant otters since territory boundaries of neighbouring groups can overlap and direct inter-group conflicts are severe. Therefore, group chorusing may contribute to the mutual avoidance of members from different groups.
| S-EPMC5638252 | biostudies-literature
Project description:Analysis of transcript abundance estimates as a function of demographic, psychometric, and language use features.
2017-12-05 | GSE87656 | GEO
Project description:The spatial structure of genetic diversity underlying social variation is a critical determinant of how cooperation and conflict evolve. Here we investigated whether natural social groups of the cooperative soil bacterium Myxococcus xanthus harbor internal genetic and phenotypic variation and thus the potential for social conflict between interacting cells. Ten M. xanthus fruiting bodies isolated from soil were surveyed for variation in multiple social phenotypes and genetic loci, and patterns of diversity within and across fruiting body groups were examined. Eight of the 10 fruiting bodies were found to be internally diverse, with four exhibiting significant variation in social swarming phenotypes and five harboring large variation in the number of spores produced by member clones in pure culture. However, genetic variation within fruiting bodies was much lower than across fruiting bodies, suggesting that migration across even spatially proximate groups is limited relative to mutational generation of persisting endemic diversity. Our results simultaneously highlight the potential for social conflict within Myxococcus social groups and the possibility of social coevolution among diverse related lineages that are clustered in space and cotransmitted across generations.
| S-EPMC3131823 | biostudies-literature
Project description:When different objects switch identities in the multiple identity tracking (MIT) task, viewers need to rebind objects' identity and location, which requires attention. This rebinding helps people identify the regions targets are in (where they need to focus their attention) and inhibit unimportant regions (where distractors are). This study investigated the processing of attentional tracking after identity switching in an adapted MIT task. This experiment used three identity-switching conditions: a target-switching condition (where the target objects switched identities), a distractor-switching condition (where the distractor objects switched identities), and a no-switching condition. Compared to the distractor-switching condition, the target-switching condition elicited greater activation in the frontal eye fields (FEF), intraparietal sulcus (IPS), and visual cortex. Compared to the no-switching condition, the target-switching condition elicited greater activation in the FEF, inferior frontal gyrus (pars orbitalis) (IFG-Orb), IPS, visual cortex, middle temporal lobule, and anterior cingulate cortex. Finally, the distractor-switching condition showed greater activation in the IFG-Orb compared to the no-switching condition. These results suggest that, in the target-switching condition, the FEF and IPS (the dorsal attention network) might be involved in goal-driven attention to targets during attentional tracking. In addition, in the distractor-switching condition, the activation of the IFG-Orb may indicate salient change that pulls attention away automatically.
| S-EPMC4689547 | biostudies-literature
Project description:BackgroundIn the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone.ObjectiveThe objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention.MethodsBeginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out "reported speech" (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020.ResultsInterannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen κ). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state-level geolocations.ConclusionsWe have made the 13,714 tweets identified in this study, along with each tweet's time stamp and US state-level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19.
| S-EPMC7834613 | biostudies-literature
Project description:ContextIdentifying tranquil areas is important for landscape planning and policy-making. Research demonstrated discrepancies between modelled potential tranquil areas and where people experience tranquillity based on field surveys. Because surveys are resource-intensive, user-generated text data offers potential for extracting where people experience tranquillity.ObjectivesWe explore and model the relationship between landscape ecological measures and experienced tranquillity extracted from user-generated text descriptions.MethodsGeoreferenced, user-generated landscape descriptions from Geograph.UK were filtered using keywords related to tranquillity. We stratify resulting tranquil locations according to dominant land cover and quantify the influence of landscape characteristics including diversity and naturalness on explaining the presence of tranquillity. Finally, we apply natural language processing to identify terms linked to tranquillity keywords and compare the similarity of these terms across land cover classes.ResultsEvaluation of potential keywords yielded six keywords associated with experienced tranquillity, resulting in 15,350 extracted tranquillity descriptions. The two most common land cover classes associated with tranquillity were arable and horticulture, and improved grassland, followed by urban and suburban. In the logistic regression model across all land cover classes, freshwater, elevation and naturalness were positive predictors of tranquillity. Built-up area was a negative predictor. Descriptions of tranquillity were most similar between improved grassland and arable and horticulture, and most dissimilar between arable and horticulture and urban.ConclusionsThis study highlights the potential of applying natural language processing to extract experienced tranquillity from text, and demonstrates links between landscape ecological measures and tranquillity as a perceived landscape quality.Supplementary informationThe online version of this article (10.1007/s10980-020-01181-8) contains supplementary material, which is available to authorized users.
| S-EPMC8550761 | biostudies-literature
Project description:ImportanceCase reports that externalize expert diagnostic reasoning are utilized for clinical reasoning instruction but are difficult to search based on symptoms, final diagnosis, or differential diagnosis construction. Computational approaches that uncover how experienced diagnosticians analyze the medical information in a case as they formulate a differential diagnosis can guide educational uses of case reports.ObjectiveTo develop a "reasoning-encoded" case database for advanced clinical reasoning instruction by applying natural language processing (NLP), a sub-field of artificial intelligence, to a large case report library.DesignWe collected 2525 cases from the New England Journal of Medicine (NEJM) Clinical Pathological Conference (CPC) from 1965 to 2020 and used NLP to analyze the medical terminology in each case to derive unbiased (not prespecified) categories of analysis used by the clinical discussant. We then analyzed and mapped the degree of category overlap between cases.ResultsOur NLP algorithms identified clinically relevant categories that reflected the relationships between medical terms (which included symptoms, signs, test results, pathophysiology, and diagnoses). NLP extracted 43,291 symptoms across 2525 cases and physician-annotated 6532 diagnoses (both primary and related diagnoses). Our unsupervised learning computational approach identified 12 categories of medical terms that characterized the differential diagnosis discussions within individual cases. We used these categories to derive a measure of differential diagnosis similarity between cases and developed a website ( universeofcpc.com ) to allow visualization and exploration of 55 years of NEJM CPC case series.ConclusionsApplying NLP to curated instances of diagnostic reasoning can provide insight into how expert clinicians correlate and coordinate disease categories and processes when creating a differential diagnosis. Our reasoning-encoded CPC case database can be used by clinician-educators to design a case-based curriculum and by physicians to direct their lifelong learning efforts.
| S-EPMC9849536 | biostudies-literature
Project description:ObjectiveTo address thyroid cancer overdiagnosis, we aim to develop a natural language processing (NLP) algorithm to determine the appropriateness of thyroid ultrasounds (TUS).Patients and methodsBetween 2017 and 2021, we identified 18,000 TUS patients at Mayo Clinic and selected 628 for chart review to create a ground truth dataset based on consensus. We developed a rule-based NLP pipeline to identify TUS as appropriate TUS (aTUS) or inappropriate TUS (iTUS) using patients' clinical notes and additional meta information. In addition, we designed an abbreviated NLP pipeline (aNLP) solely focusing on labels from TUS order requisitions to facilitate deployment at other health care systems. Our dataset was split into a training set of 468 (75%) and a test set of 160 (25%), using the former for rule development and the latter for performance evaluation.ResultsThere were 449 (95.9%) patients identified as aTUS and 19 (4.06%) as iTUS in the training set; there are 155 (96.88%) patients identified as aTUS and 5 (3.12%) were iTUS in the test set. In the training set, the pipeline achieved a sensitivity of 0.99, specificity of 0.95, and positive predictive value of 1.0 for detecting aTUS. The testing cohort revealed a sensitivity of 0.96, specificity of 0.80, and positive predictive value of 0.99. Similar performance metrics were observed in the aNLP pipeline.ConclusionThe NLP models can accurately identify the appropriateness of a thyroid ultrasound from clinical documentation and order requisition information, a critical initial step toward evaluating the drivers and outcomes of TUS use and subsequent thyroid cancer overdiagnosis.
| S-EPMC10947349 | biostudies-literature