Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data.

BACKGROUND: Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health-related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors. OBJECTIVE: The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users' tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors. METHODS: A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted. RESULTS: A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users' opinions and feelings. CONCLUSIONS: Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers' ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions.

Crises information dissemination through social media in the UK and Saudi Arabia: A linguistic perspective.

This study investigates health-promoting messages in British and Saudi officials' social-media discourse during the Coronavirus Disease 2019 (COVID-19) Pandemic. Taking discourse as a constructivist conception, we examined the crisis-response strategies employed by these officials on social media, and the role of such strategies in promoting healthy behaviors and compliance with health regulations. The study presents a corpus-assisted discourse analysis of the tweets of a Saudi health official and a British health official that focuses on keyness, speech acts, and metaphor. We found that both officials utilized clear communication and persuasive rhetorical tactics to convey the procedures suggested by the World Health Organization. However, there were some differences in how the two officials used speech acts and metaphors to achieve their goals. The British official used empathy as the primary communication strategy, while the Saudi official emphasized health literacy. The British official also used conflict-based metaphors such as war and gaming, whereas the Saudi official used metaphors that reflected life as a journey interrupted by the pandemic. Despite these differences, both officials utilized directive speech acts to tell audiences the procedures they should follow to achieve the desired conclusion of healing patients and ending the pandemic. In addition, rhetorical questions and assertions were used to direct people to perform certain behaviors favored. Interestingly, the discourse used by both officials contained characteristics of both health communication and political discourse. War metaphors, which were utilized by the British Health official, are a common feature in political discourse as well as in health-care discourse. Overall, this study highlights the importance of effective communication strategies in promoting healthy behaviors and compliance with health regulations during a pandemic. By analyzing the discourse of health officials on social media, we can gain insights into the strategies employed to manage a crisis and effectively communicate with the public.

Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts.

BACKGROUND AND OBJECTIVE: With the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of events in order of time. Temporal information runs through the whole process of beginning, proceeding, and ending of events, and plays an important role in many natural language processing applications, such as information extraction, question answering, and text summary. Accurately extracting temporal information from Chinese texts and automatically mapping the temporal expressions in natural language to the time axis are crucial to understanding the development of events and dynamic changes in them. METHODS: This study proposes a method integrating machine learning with linguistic features (IMLLF) for extraction and normalization of temporal expressions in Chinese texts to achieve the above objectives. Linguistic features are constructed by analyzing the expression rules of temporal information, and are combined with machine learning to map the natural language form of time onto a one-dimensional timeline. The web text dataset we build is divided into five parts for five-fold cross-validation, to compare the influence of different combinations of linguistic features and different methods. In the open medical dialog dataset, based on the training model obtained from the web text dataset, 200 disease descriptions are randomly selected each time for three rounds of experiments. RESULTS: The F1 of multi-feature fusion is 95.2%, which is better than the single-feature and double-feature combination. The results of experiments showed that the proposed IMLLF method can improve the accuracy of recognition of temporal information in Chinese to a greater extent than classical methods, with an F1-score of over 95% on the web text dataset and medical conversation dataset. In terms of the normalization of time expressions, the accuracy of the IMLLF method is higher than 93%. CONCLUSIONS: IMLLF has better results in extracting and normalizing time expressions on the web text dataset and the medical conversation dataset, which verifies the universality of IMLLF to identify and quantify temporal information. IMLLF method can accurately map the time information to the time axis, which is convenient for doctors to intuitively see when and what happened to the patient, and helps to make better medical decisions.

Miniature Linguistic Systems for Individuals With Autism Spectrum Disorder: A Systematic Review and Meta-Analysis.

PURPOSE: Miniature linguistic systems (also known as matrix training) is a method of organizing learning targets to achieve generative learning or recombinative generalization. This systematic review is aimed at determining whether matrix training is effective for individuals with autism spectrum disorder (ASD) in terms of improving recombinative generalization for instruction-following, expressive language, play skills, and literacy skills. METHOD: A systematic review methodology was employed to limit bias in the various review stages. A multifaceted search was conducted. Potential primary studies were imported into Covidence, a systematic review software, and inclusion criteria were applied. Data were extracted regarding (a) participant characteristics, (b) matrix designs, (c) intervention methods, and (d) dependent variable. A quality appraisal using the What Works Clearinghouse (WWC) Single-Case Design Standards (Version 1.0, Pilot) was carried out. In addition to the visual analysis of the data, an effect size estimate, non-overlap of all pairs (NAP), was generated for each participant. Independent t tests and between-subjects analyses of variance were conducted to identify moderators of effectiveness. RESULTS: Twenty-six studies including 65 participants met criteria for inclusion. All included studies were single-case experimental designs. Eighteen studies received a rating of Meets Standards Without Reservations or Meets Standards With Reservations. The aggregated combined NAP scores for acquisition, recombinative generalization, and maintenance of a range of outcomes were in the high range. CONCLUSIONS: Findings suggested that matrix training is an effective teaching method for individuals with ASD for acquisition, recombinative generalization, and maintenance of a range of outcomes. Statistical analyses to identify moderators of effectiveness were insignificant. Based on the WWC Single-Case Design Standards matrix training meets criteria to be considered an evidence-based practice for individuals with ASD.

Thinking about life in COVID-19: An exploratory study on the influence of temporal framing on streams-of-consciousness.

The COVID-19 global pandemic led to major upheavals in daily life. As a result, mental health has been negatively impacted for many, including college students who have faced increased stress, depression, anxiety, and social isolation. How we think about the future and adjust to such changes may be partly mediated by how we situate our experiences in relation to the pandemic. To test this idea, we investigate how temporal framing influences the way participants think about COVID life. In an exploratory study, we investigate the influence of thinking of life before versus during the pandemic on subsequent thoughts about post-pandemic life. Participants wrote about their lives in a stream-of-consciousness style paradigm, and the linguistic features of their thoughts are extracted using Linguistic Inquiry and Word Count (LIWC). Initial results suggest principal components of LIWC features can distinguish the two temporal framings just from the content of their post-pandemic-oriented texts alone. We end by discussing theoretical implications for our understanding of personal experience and self-generated narrative. We also discuss other aspects of the present data that may be useful for investigating these thought processes in the future, including document-level features, typing dynamics, and individual difference measures.

Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis.

BACKGROUND: Anxiety disorder has become a major clinical and public health problem, causing a significant economic burden worldwide. Public attitudes toward anxiety can impact the psychological state, help-seeking behavior, and social activities of people with anxiety disorder. OBJECTIVE: The purpose of this study was to explore public attitudes toward anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders on Sina Weibo, a Chinese social media platform that has about 582 million users, as well as the psycholinguistic and topical features in the text content of the posts. METHODS: From April 2018 to March 2022, 325,807 Sina Weibo posts with the keyword "anxiety disorder" were collected and analyzed. First, we analyzed the changing trends in the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends in the language features of the posts, in which 20 linguistic features were selected and presented. Third, a topic model (biterm topic model) was used for semantic content analysis to identify specific themes in Weibo users' attitudes toward anxiety. RESULTS: The changing trends in the number and the total length of posts indicated that anxiety-related posts significantly increased from April 2018 to March 2022 (R2=0.6512; P<.001 to R2=0.8133; P<.001, respectively) and were greatly impacted by the beginning of a new semester (spring/fall). The analysis of linguistic features showed that the frequency of the cognitive process (R2=0.1782; P=.003), perceptual process (R2=0.1435; P=.008), biological process (R2=0.3225; P<.001), and assent words (R2=0.4412; P<.001) increased significantly over time, while the frequency of the social process words (R2=0.2889; P<.001) decreased significantly, and public anxiety was greatly impacted by the COVID-19 pandemic. Feature correlation analysis showed that the frequencies of words related to work and family are almost negatively correlated with those of other psychological words. Semantic content analysis identified 5 common topical areas: discrimination and stigma, symptoms and physical health, treatment and support, work and social, and family and life. Our results showed that the occurrence probability of the topical area "discrimination and stigma" reached the highest value and averagely accounted for 26.66% in the 4-year period. The occurrence probability of the topical area "family and life" (R2=0.1888; P=.09) decreased over time, while that of the other 4 topical areas increased. CONCLUSIONS: The findings of our study indicate that public discrimination and stigma against anxiety disorder remain high, particularly in the aspects of self-denial and negative emotions. People with anxiety disorders should receive more social support to reduce the impact of discrimination and stigma.

Linguistic Predictors of Psychological Adjustment in Healthcare Workers during the COVID-19 Pandemic.

COVID-19 broke out in China in December 2019 and rapidly became a worldwide pandemic that demanded an extraordinary response from healthcare workers (HCWs). Studies conducted during the pandemic observed severe depression and PTSD in HCWs. Identifying early predictors of mental health disorders in this population is key to informing effective treatment and prevention. The aim of this study was to investigate the power of language-based variables to predict PTSD and depression symptoms in HCWs. One hundred thirty-five HCWs (mean age = 46.34; SD = 10.96) were randomly assigned to one of two writing conditions: expressive writing (EW n = 73) or neutral writing (NW n = 62) and completed three writing sessions. PTSD and depression symptoms were assessed both pre- and post-writing. LIWC was used to analyze linguistic markers of four trauma-related variables (cognitive elaboration, emotional elaboration, perceived threat to life, and self-immersed processing). Changes in PTSD and depression were regressed onto the linguistic markers in hierarchical multiple regression models. The EW group displayed greater changes on the psychological measures and in terms of narrative categories deployed than the NW group. Changes in PTSD symptoms were predicted by cognitive elaboration, emotional elaboration, and perceived threat to life; changes in depression symptoms were predicted by self-immersed processing and cognitive elaboration. Linguistic markers can facilitate the early identification of vulnerability to mental disorders in HCWs involved in public health emergencies. We discuss the clinical implications of these findings.

For whom do boundaries become restrictions? The role of political orientation.

While the global pandemic highlighted the importance of adhering to boundaries (e.g., social distancing rules), compliance with these boundary-imposing measures has been politically divided. This research proposes one reason that may underlie the observed ideological asymmetries toward COVID-19 prevention measures and boundaries in general: Conservatives and liberals may fundamentally differ in how they construe boundaries. Supporting this prediction, Studies 1a-1d and two follow-up studies (n = 3,231; Studies 1a-1c and follow-up studies: Amazon Mechanical Turk and Prolific users, Study 1d: U.S. students) demonstrate that identifying with political conservatism (vs. liberalism) increases the likelihood to construe boundaries as restrictions. We further show that, due to conservatives' greater preference for order, structure-related words carry a more positive connotation among conservatives versus liberals (Study 2: n = 744; MTurk users). Capitalizing on this finding, we demonstrate that linguistic framing that highlights the structure-providing function of a boundary (e.g., a social distancing sign can "structure" customer flow in a restaurant) can reduce the salience of its usual restrictive aspect and hence effectively improve conservatives' attitudes toward the boundaries (Study 3: n = 740; MTurk users). (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Inclusion of Cultural and Linguistic Diversity in COVID-19 Public Health Research: Research Design Adaptations to Seek Different Perspectives in Victoria, Australia.

Participation of people from culturally and linguistically diverse (CALD) communities in public health research is often limited by challenges with recruitment, retention and second-language data collection. Consequently, people from CALD communities are at risk of their needs being marginalised in public health interventions. This paper presents intrinsic case analyses of two studies which were adapted to increase the cultural competence of research processes. Both cases were part of the Optimise study, a major mixed methods research study in Australia which provided evidence to inform the Victorian state government's decision-making about COVID-19 public health measures. Case study 1 involved the core Optimise longitudinal cohort study and Case study 2 was the CARE Victorian representative survey, an Optimise sub-study. Both case studies engaged cultural advisors and bilingual staff to adjust the survey measures and research processes to suit target CALD communities. Reflexive processes provided insights into the strengths and weaknesses of the inclusive strategies. Selected survey results are provided, demonstrating variation across CALD communities and in comparison to participants who reported speaking English at home. While in most cases a gradient of disadvantage was evident for CALD communities, some patterns were unexpected. The case studies demonstrate the challenge and value of investing in culturally competent research processes to ensure research guiding policy captures a spectrum of experiences and perspectives.

Deep Mobile Linguistic Therapy for Patients with ASD.

Autistic spectrum disorder (ASD) is one of the most complex groups of neurobehavioral and developmental conditions. The reason is the presence of three different impaired domains, such as social interaction, communication, and restricted repetitive behaviors. Some children with ASD may not be able to communicate using language or speech. Many experts propose that continued therapy in the form of software training in this area might help to bring improvement. In this work, we propose a design of software speech therapy system for ASD. We combined different devices, technologies, and features with techniques of home rehabilitation. We used TensorFlow for Image Classification, ArKit for Text-to-Speech, Cloud Database, Binary Search, Natural Language Processing, Dataset of Sentences, and Dataset of Images with two different Operating Systems designed for Smart Mobile devices in daily life. This software is a combination of different Deep Learning Technologies and makes Human-Computer Interaction Therapy very easy to conduct. In addition, we explain the way these were connected and put to work together. Additionally, we explain in detail the architecture of software and how each component works together as an integrated Therapy System. Finally, it allows the patient with ASD to perform the therapy anytime and everywhere, as well as transmitting information to a medical specialist.

CNN-LSTM Hybrid Real-Time IoT-Based Cognitive Approaches for ISLR with WebRTC: Auditory Impaired Assistive Technology.

In the era of modern technology, people may readily communicate through facial expressions, body language, and other means. As the use of the Internet evolves, it may be a boon to the medical fields. Recently, the Internet of Medical Things (IoMT) has provided a broader platform to handle difficulties linked to healthcare, including people's listening and hearing impairment. Although there are many translators that exist to help people of various linguistic backgrounds communicate more effectively. Using kinesics linguistics, one may assess or comprehend the communications of auditory and hearing-impaired persons who are standing next to each other. When looking at the present COVID-19 scenario, individuals are still linked in some way via online platforms; however, persons with disabilities have communication challenges with online platforms. The work provided in this research serves as a communication bridge inside the challenged community and the rest of the globe. The proposed work for Indian Sign Linguistic Recognition (ISLR) uses three-dimensional convolutional neural networks (3D-CNNs) and long short-term memory (LSTM) technique for analysis. A conventional hand gesture recognition system involves identifying the hand and its location or orientation, extracting certain essential features and applying an appropriate machine learning algorithm to recognise the completed action. In the calling interface of the web application, WebRTC has been implemented. A teleprompting technology is also used in the web app, which transforms sign language into audible sound. The proposed web app's average recognition rate is 97.21%.

Linguistic Pattern-Infused Dual-Channel Bidirectional Long Short-term Memory With Attention for Dengue Case Summary Generation From the Program for Monitoring Emerging Diseases-Mail Database: Algorithm Development Study.

BACKGROUND: Globalization and environmental changes have intensified the emergence or re-emergence of infectious diseases worldwide, such as outbreaks of dengue fever in Southeast Asia. Collaboration on region-wide infectious disease surveillance systems is therefore critical but difficult to achieve because of the different transparency levels of health information systems in different countries. Although the Program for Monitoring Emerging Diseases (ProMED)-mail is the most comprehensive international expert-curated platform providing rich disease outbreak information on humans, animals, and plants, the unstructured text content of the reports makes analysis for further application difficult. OBJECTIVE: To make monitoring the epidemic situation in Southeast Asia more efficient, this study aims to develop an automatic summary of the alert articles from ProMED-mail, a huge textual data source. In this paper, we proposed a text summarization method that uses natural language processing technology to automatically extract important sentences from alert articles in ProMED-mail emails to generate summaries. Using our method, we can quickly capture crucial information to help make important decisions regarding epidemic surveillance. METHODS: Our data, which span a period from 1994 to 2019, come from the ProMED-mail website. We analyzed the collected data to establish a unique Taiwan dengue corpus that was validated with professionals' annotations to achieve almost perfect agreement (Cohen κ=90%). To generate a ProMED-mail summary, we developed a dual-channel bidirectional long short-term memory with attention mechanism with infused latent syntactic features to identify key sentences from the alerting article. RESULTS: Our method is superior to many well-known machine learning and neural network approaches in identifying important sentences, achieving a macroaverage F1 score of 93%. Moreover, it can successfully extract the relevant correct information on dengue fever from a ProMED-mail alerting article, which can help researchers or general users to quickly understand the essence of the alerting article at first glance. In addition to verifying the model, we also recruited 3 professional experts and 2 students from related fields to participate in a satisfaction survey on the generated summaries, and the results show that 84% (63/75) of the summaries received high satisfaction ratings. CONCLUSIONS: The proposed approach successfully fuses latent syntactic features into a deep neural network to analyze the syntactic, semantic, and contextual information in the text. It then exploits the derived information to identify crucial sentences in the ProMED-mail alerting article. The experiment results show that the proposed method is not only effective but also outperforms the compared methods. Our approach also demonstrates the potential for case summary generation from ProMED-mail alerting articles. In terms of practical application, when a new alerting article arrives, our method can quickly identify the relevant case information, which is the most critical part, to use as a reference or for further analysis.

Lexical Features of Economic Legal Policy and News in China Since the COVID-19 Outbreak.

Lexical features are influenced by different languages and genres. The study of lexical features in different genres of texts on the same topic is helpful to understand the universalities and peculiarities of languages. This study constructs a research on the lexical feature and word collocations of two self-build corpora (China's economic Legal Policy Corpus and English News Corpus during the COVID-19 pandemic), the methods of Quantitative Linguistics and context interpretation are adopted. It was found that: (1) the word length, word frequency, word cluster and high frequency word distribution in English economic news and Chinese economic legal policies are influenced by language and genre to some extent, and they conform to different functional image distribution; (2) during the COVID-19 pandemic, "development" has been the focus of China's economic legal policies and English news, the two have attached importance to economic recovery and taken a positive attitude toward it in different ways. These findings suggest that: (1) There are some universalities and peculiarities between English economic news and Chinese economic legal policies in the distribution of lexical feature; (2) there is a certain synchronization between laws and news, and both of them maintain a positive and objective attitude toward the economic development during the pandemic. This study carries out a macroscopic investigation on internal structure and external interpretation, which enriches the study on lexical features and cultural features of language and provides some references for relevant studies.

Latent Linguistic Motifs in Social Media Postings Resisting COVID-19 Misinformation.

Social media has become a predominant source of information for many health care consumers. However, false and misleading information is a pervasive problem in this context. Specifically, health-related misinformation has become a significant public health challenge, impeding the effectiveness of public health awareness campaigns and resulting in suboptimal responsiveness to the communication of legitimate risk-related information. Little is known about the mechanisms driving the seeding and spreading of such information. In this paper, we specifically examine COVID-19 tweets which attempt to correct misinformation. We employ a mixed-methods approach comprising qualitative coding, deep learning classification, and computerized text analysis to understand the manifestation of speech acts and other linguistic variables. Results indicate significant differences in linguistic variables (e.g., positive emotion, tone, authenticity) of corrective tweets and their dissemination level. Our deep learning classifier has a macro average performance of 0.82. Implications for effective and persuasive misinformation correction efforts are discussed.

Research on emergency management of urban waterlogging based on similarity fusion of multi-source heterogeneous data.

Global warming has seriously affected the local climate characteristics of cities, resulting in the frequent occurrence of urban waterlogging with severe economic losses and casualties. Aiming to improve the effectiveness of disaster emergency management, we propose a novel emergency decision model embedding similarity algorithms of heterogeneous multi-attribute based on case-based reasoning. First, this paper establishes a multi-dimensional attribute system of urban waterlogging catastrophes cases based on the Wuli-Shili-Renli theory. Due to the heterogeneity of attributes of waterlogging cases, different algorithms to measure the attribute similarity are designed for crisp symbols, crisp numbers, interval numbers, fuzzy linguistic variables, and hesitant fuzzy linguistic term sets. Then, this paper combines the best-worst method with the maximal deviation method for a more reasonable weight allocation of attributes. Finally, the hybrid similarity between the historical and the target cases is obtained by aggregating attribute similarities via the weighted method. According to the given threshold value, a similar historical case set is built whose emergency measures are used to provide the reference for the target case. Additionally, a case of urban waterlogging emergency is conducted to demonstrate the applicability and effectiveness of the proposed model, which exploits historical experiences and retrieves the optimal scheme for the current disaster emergency with heterogeneous multi attributes. Consequently, the proposed model solves the problem of diverse data types to satisfy the needs of case presentation and retrieval. Compared with the existing model, it can better realize the multi-dimensional expression and fast matching of the cases.

Health-Related Content in Transformer-Based Deep Neural Network Language Models: Exploring Cross-Linguistic Syntactic Bias.

This paper explores a methodology for bias quantification in transformer-based deep neural network language models for Chinese, English, and French. When queried with health-related mythbusters on COVID-19, we observe a bias that is not of a semantic/encyclopaedical knowledge nature, but rather a syntactic one, as predicted by theoretical insights of structural complexity. Our results highlight the need for the creation of health-communication corpora as training sets for deep learning.

Translating Enhanced Conversational Recast to a Telepractice Setting.

PURPOSE: This investigation adapted a well-studied language treatment method, Enhanced Conversational Recast, paired with auditory bombardment to a teletherapy format. METHOD: The study used a single case series approach (n = 7) to determine the feasibility of teletherapy with children ages 5 and 6 years of age. Treatment targeted grammatical errors in the context of dialogic reading and craft activities. Clinicians administered 24 doses in the form of focused conversational recasting, followed by 12 doses consisting of simple sentences containing the grammatical forms targeted for remediation. Children were treated for up to 26 sessions, with four children treated on consecutive weekdays and three treated twice a week. Treatment progress was operationalized as generalization of target grammatical forms to untreated linguistic contexts, as well as spontaneous use of the treated form. To control for nontreatment effects, generalization of an untreated form was also tracked throughout the treatment period. RESULTS: Six of the seven children showed clinically meaningful gains in the use of the grammatical forms targeted for treatment within the treatment period. This was true for children enrolled in both treatment schedules. Learning for treated forms was retained after treatment was discontinued. In comparison, no change was seen for untreated forms for six of the seven children. CONCLUSIONS: The results suggest that this treatment method is feasible in a telepractice format, even with young children. The range of individual results is generally comparable to previous face-to-face versions of this treatment.

Evaluation of Emergency Intelligence Capability of Major Public Health Events in Probabilistic Uncertain Language Environment.

Emergency intelligence capability is an important index reflecting the intelligence level of emergency management. The accuracy of emergency intelligence capability evaluation is related to the scientific nature of emergency decision-making. By analyzing the operation process and mechanism of the emergency intelligence system for major public health events, this paper establishes the evaluation index system of the emergency intelligence capability. By using the decision method of VIKOR and considering the preference of experts in the evaluation process, this paper proposes the evaluation model based on the probabilistic uncertain language environment. The use of probabilistic uncertain linguistic term set (PULTS) to describe the uncertain information is helpful to improve the scientific and accuracy of emergency rescue decision-making of major public health events and then realize the organic unity of emergency information and scientific decision-making of public health events.

A Cross-Linguistic Validation of the Test for Rating Emotions in Speech: Acoustic Analyses of Emotional Sentences in English, German, and Hebrew.

PURPOSE: The Test for Rating Emotions in Speech (T-RES) has been developed in order to assess the processing of emotions in spoken language. In this tool, spoken sentences, which are composed of emotional content (anger, happiness, sadness, and neutral) in both semantics and prosody in different combinations, are rated by listeners. To date, English, German, and Hebrew versions have been developed, as well as online versions, iT-RES, to adapt to COVID-19 social restrictions. Since the perception of spoken emotions may be affected by linguistic (and cultural) variables, it is important to compare the acoustic characteristics of the stimuli within and between languages. The goal of the current report was to provide cross-linguistic acoustic validation of the T-RES. METHOD: T-RES sentences in the aforementioned languages were acoustically analyzed in terms of mean F0, F0 range, and speech rate to obtain profiles of acoustic parameters for different emotions. RESULTS: Significant within-language discriminability of prosodic emotions was found, for both mean F0 and speech rate. Similarly, these measures were associated with comparable patterns of prosodic emotions for each of the tested languages and emotional ratings. CONCLUSIONS: The results demonstrate the lack of dependence of prosody and semantics within the T-RES stimuli. These findings illustrate the listeners' ability to clearly distinguish between the different prosodic emotions in each language, providing a cross-linguistic validation of the T-RES and iT-RES.

Leveraging Unsupervised Machine Learning to Discover Patterns in Linguistic Health Summaries for Eldercare.

The Center for Eldercare and Rehabilitation Technology, at University of Missouri, has researched the use of smart, unobtrusive sensors for older adult residents' health monitoring and alerting in aging-in-place communities for many years. Sensors placed in the apartments of older adult residents generate a deluge of daily data that is automatically aggregated, analyzed, and summarized to aid in health awareness, clinical care, and research for healthy aging. When anomalies or concerning trends are detected within the data, the sensor information is converted into linguistic health messages using fuzzy computational techniques, so as to make it understandable to the clinicians. Sensor data are analyzed at the individual level, therefore, through this study we aim to discover various combinations of patterns of anomalies happening together and recurrently in the older adult's population using these text summaries. Leveraging various computational text data processing techniques, we are able to extract relevant analytical features from the health messages. These features are transformed into a transactional encoding, then processed with frequent pattern mining techniques for association rule discovery. At individual level analysis, resident ID 3027 was considered as an exemplar to describe the analysis. Seven combinations of anomalies/rules/associations were discovered in this resident, out of which rule group three showed an increased recurrence during the COVID lockdown of facility. At the population level, a total of 38 associations were discovered that highlight the health patterns, and we continue to explore the health conditions associated with them. Ultimately, our goal is to correlate the combinations of anomalies with certain health conditions, which can then be leveraged for predictive analytics and preventative care. This will improve the current clinical care systems for older adult residents in smart sensor, aging-in-place communities.