Spoken responses produced by subjects during neuropsychological exams can provide diagnostic markers beyond exam performance. In particular, characteristics of the spoken language itself can discriminate between subject groups. We present results on the utility of such markers in discriminating between healthy elderly subjects and subjects with mild cognitive impairment (MCI). Given the audio and transcript of a spoken narrative recall task, a range of markers are automatically derived. These markers include speech features such as pause frequency and duration, and many linguistic complexity measures. We examine measures calculated from manually annotated time alignments (of the transcript with the audio) and syntactic parse trees, as well as the same measures calculated from automatic (forced) time alignments and automatic parses. We show statistically significant differences between clinical subject groups for a number of measures. These differences are largely preserved with automation. We then present classification results, and demonstrate a statistically significant improvement in the area under the ROC curve (AUC) when using automatic spoken language derived features in addition to the neuropsychological test scores. Our results indicate that using multiple, complementary measures can aid in automatic detection of MCI.
Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health-much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research. We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful.
This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder (PTSD) and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control, and (3) depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures.
Analyzing symptoms of schizophrenia has traditionally been challenging given the low prevalence of the condition, affecting around 1% of the U.S. population. We explore potential linguistic markers of schizophrenia using the tweets 1 of self-identified schizophrenia sufferers, and describe several natural language processing (NLP) methods to analyze the language of schizophrenia. We examine how these signals compare with the widelyused LIWC categories for understanding mental health (Pennebaker et al., 2007), and provide preliminary evidence of additional linguistic signals that may aid in identifying and getting help to people suffering from schizophrenia.
We consider the diagnostic utility of various syntactic complexity measures when extracted from spoken language samples of healthy and cognitively impaired subjects. We examine measures calculated from manually built parse trees, as well as the same measures calculated from automatic parses. We show statistically significant differences between clinical subject groups for a number of syntactic complexity measures, and these differences are preserved with automatic parsing. Different measures show different patterns for our data set, indicating that using multiple, complementary measures is important for such an application.
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