Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology最新文献

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Overcoming the bottleneck in traditional assessments of verbal memory: Modeling human ratings and classifying clinical group membership 克服传统言语记忆评估的瓶颈:模拟人类评分和分类临床小组成员
Chelsea Chandler, P. Foltz, Jian Cheng, J. Bernstein, E. Rosenfeld, A. Cohen, Terje B. Holmlund, B. Elvevåg
{"title":"Overcoming the bottleneck in traditional assessments of verbal memory: Modeling human ratings and classifying clinical group membership","authors":"Chelsea Chandler, P. Foltz, Jian Cheng, J. Bernstein, E. Rosenfeld, A. Cohen, Terje B. Holmlund, B. Elvevåg","doi":"10.18653/v1/W19-3016","DOIUrl":"https://doi.org/10.18653/v1/W19-3016","url":null,"abstract":"Verbal memory is affected by numerous clinical conditions and most neuropsychological and clinical examinations evaluate it. However, a bottleneck exists in such endeavors because traditional methods require expert human review, and usually only a couple of test versions exist, thus limiting the frequency of administration and clinical applications. The present study overcomes this bottleneck by automating the administration, transcription, analysis and scoring of story recall. A large group of healthy participants (n = 120) and patients with mental illness (n = 105) interacted with a mobile application that administered a wide range of assessments, including verbal memory. The resulting speech generated by participants when retelling stories from the memory task was transcribed using automatic speech recognition tools, which was compared with human transcriptions (overall word error rate = 21%). An assortment of surface-level and semantic language-based features were extracted from the verbal recalls. A final set of three features were used to both predict expert human ratings with a ridge regression model (r = 0.88) and to differentiate patients from healthy individuals with an ensemble of logistic regression classifiers (accuracy = 76%). This is the first ‘outside of the laboratory’ study to showcase the viability of the complete pipeline of automated assessment of verbal memory in naturalistic settings.","PeriodicalId":201097,"journal":{"name":"Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology","volume":"6 3-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131179855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 17
Mental Health Surveillance over Social Media with Digital Cohorts 基于数字群组的社交媒体心理健康监测
Silvio Amir, Mark Dredze, J. Ayers
{"title":"Mental Health Surveillance over Social Media with Digital Cohorts","authors":"Silvio Amir, Mark Dredze, J. Ayers","doi":"10.18653/v1/W19-3013","DOIUrl":"https://doi.org/10.18653/v1/W19-3013","url":null,"abstract":"The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance. However, inferring accurate population-level trends requires representative samples of the underlying population, which can be challenging given the biases inherent in social media data. While previous work has adjusted samples based on demographic estimates, the populations were selected based on specific outcomes, e.g. specific mental health conditions. We depart from these methods, by conducting analyses over demographically representative digital cohorts of social media users. To validated this approach, we constructed a cohort of US based Twitter users to measure the prevalence of depression and PTSD, and investigate how these illnesses manifest across demographic subpopulations. The analysis demonstrates that cohort-based studies can help control for sampling biases, contextualize outcomes, and provide deeper insights into the data.","PeriodicalId":201097,"journal":{"name":"Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116729616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 26
Dictionaries and Decision Trees for the 2019 CLPsych Shared Task 2019年CLPsych共享任务的字典和决策树
Micah Iserman, Taleen Nalabandian, Molly Ireland
{"title":"Dictionaries and Decision Trees for the 2019 CLPsych Shared Task","authors":"Micah Iserman, Taleen Nalabandian, Molly Ireland","doi":"10.18653/v1/W19-3025","DOIUrl":"https://doi.org/10.18653/v1/W19-3025","url":null,"abstract":"In this summary, we discuss our approach to the CLPsych Shared Task and its initial results. For our predictions in each task, we used a recursive partitioning algorithm (decision trees) to select from our set of features, which were primarily dictionary scores and counts of individual words. We focused primarily on Task A, which aimed to predict suicide risk, as rated by a team of expert clinicians (Shing et al., 2018), based on language used in SuicideWatch posts on Reddit. Category-level findings highlight the potential importance of social and moral language categories. Word-level correlates of risk levels underline the value of fine-grained data-driven approaches, revealing both theory-consistent and potentially novel correlates of suicide risk that may motivate future research.","PeriodicalId":201097,"journal":{"name":"Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129485879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model 志趣相投:用混合模型评估自杀风险
Lushi Chen, Abeer Aldayel, Nikolay Bogoychev, Tao Gong
{"title":"Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model","authors":"Lushi Chen, Abeer Aldayel, Nikolay Bogoychev, Tao Gong","doi":"10.18653/v1/W19-3018","DOIUrl":"https://doi.org/10.18653/v1/W19-3018","url":null,"abstract":"This paper describes our system submission for the CLPsych 2019 shared task B on suicide risk assessment. We approached the problem with three separate models: a behaviour model; a language model and a hybrid model. For the behavioral model approach, we model each user’s behaviour and thoughts with four groups of features: posting behaviour, sentiment, motivation, and content of the user’s posting. We use these features as an input in a support vector machine (SVM). For the language model approach, we trained a language model for each risk level using all the posts from the users as the training corpora. Then, we computed the perplexity of each user’s posts to determine how likely his/her posts were to belong to each risk level. Finally, we built a hybrid model that combines both the language model and the behavioral model, which demonstrates the best performance in detecting the suicide risk level.","PeriodicalId":201097,"journal":{"name":"Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126891663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
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