Automated analysis of free speech predicts psychosis onset in high-risk youths Schizophrenia

In this equation, there are two symptom variables (sums of subthreshold psychotic and negative symptoms, respectively, Atotal, Btotal) and three speech variables (minimum semantic coherence, normalized use of determiners, and maximum phrase length). To investigate whether standard clinical ratings could differentiate CHR+ and CHR− individuals, we entered variables from clinical ratings—the SIPS/SOPS13—into several classifiers. The best prediction obtained was less accurate than the automated analysis, misclassifying 3 of 5 CHR+ patients and 4 of 29 CHR− patients to yield an accuracy of 79%, consistent with prior studies (see Table 2 for classification performance metrics). Of the 34 participants, 5 were known to develop schizophrenia (or schizoaffective disorder) within 2.5 years. Respectively, their times to psychosis onset from time of speech sampling were 3, 4, 8, 12, and 16 months. Twenty-nine participants were known to not develop psychosis over follow-up, with 22 of these participants followed for 2.5 years, 4 participants followed for 2 years, and 3 followed for 1.5 years (these participants’ CHR status was ascertained closer to the end of the overall study).

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It’s an online feed reader that incorporates attention data and semantic analysis in regards to both your feeds and the larger community of Wizag users. It recommends topics, visualized points of intersection between topics and new feeds based on your interaction with the subscriptions you already have. The implementation is a little slow and it’s nothing pretty to look at, but the technology is interesting. Because the concept of semantic coherence we employed does not have a mathematical definition, in this validation we tested the coherence measure against a corpus of classic literature and assessed how the measure changed when we modified the original texts in a way that is relevant to the concept of semantic coherence.

Second, automated speech assessment, if further validated, could provide previously unavailable information for clinicians on which to base treatment and prognostic decisions, effectively functioning as a ‘laboratory test’ for psychiatry. The ease of speech recording makes this approach particularly suitable for clinical applications. Self-report of symptoms, on which much of psychiatric assessment relies, depends on the patient’s motivation and capacity to accurately report their introspective experiences, which may be influenced by psychiatric illness.

• For example, the sentence ‘The cat is under the table’ is tagged by the POS-Tag procedure as ((‘The’, ‘DT’), (‘cat’, ‘NN’), (‘is’, ‘VBZ’), (‘under’, ‘IN’), (‘the’, ‘DT’), (‘table’, ‘NN’)) where DT is the tag for determiners, NN for nouns, VBZ for verbs, and IN for prepositions.
• But we rarely put the two together and use the data available to actually analyse what content works – and why.
• Although clinicians routinely detect disorganized speech on the basis of clinical observations, our data suggest that automated analytic methods allow for superior assessment.
• Psychiatry lacks the objective clinical tests routinely used in other specializations.

Semantic content analysis will disrupt marketing – for the better

Though other attention engines take the time factor into consideration, it’s nice that Wizag makes that data available in visual form and breaks it up into all users or just your feeds. The Topic Cloud displays the topics gleaned from the semantic analysis with the largest number of items and maps out the links between those topics. You can click on the line drawn between two topic nodes to read stories that contain both terms. To crack open the black box, we need to start conducting in-depth semantic analysis of our content. Only then can we begin to truly understand why some content resonates and some doesn’t. Rojo offers something similar, but its relevance function appears to be based entirely on the attention data of the whole Rojo community, whereas in Wizag there’s a lot you can do with your feeds through a filter of your own past behavior alone.

Using automated approaches, we were able to extract indices of speech-semantic coherence and syntax and use these to accurately predict the subsequent development of psychosis in high-risk youths. Prognostic prediction using this approach outperformed prediction on the basis of standard psychiatric ratings. Computerized analysis of complex human behaviors such as speech may present an opportunity to move psychiatry beyond reliance on self-report and clinical observation toward more objective measures of health and illness in the individual patient. The canonical correlation between two sets of features from the same samples, X and Y, estimates the linear combination of X features such that this combined feature has the highest correlation with an also estimated linear combination of Y features. The semantic coherence feature that best contributed to classification of subsequent psychosis onset was the minimum coherence between two consecutive phrases (i.e., the maximum discontinuity) that occurred in the interview.

This approach, although providing important information about the potential predictive capacity of these novel speech measures, may have resulted in higher estimates of the predictive accuracy of the model than would be obtained in a larger, separate sample. A vector of features for each participant is extracted and fed into the classifier that was trained on the other participants’ data. Each participant is sequentially left out of the training data set to serve as the test subject once, resulting in accuracy of prediction data for all participants.

The syntactic measure included in classification was the frequency of use of determiners (‘that’, ‘what’, ‘whatever’, ‘which’, and ‘whichever’), normalized by the phrase length. Because speech in emergent psychosis often shows marked reductions in verbosity (referred to clinically as poverty of speech), we also included the maximum number of words per phrase in the classification. A computer program that analyses natural speech could help predict the onset of psychosis in young people at risk. People with schizophrenia have subtle disorganization in speech, even before they first develop psychosis.

To complement the semantic analysis, we defined another measure for processing the documents, on the basis of Part Of Speech tagging (POS-Tag). For example, the sentence ‘The cat is under the table’ is tagged by the POS-Tag procedure as ((‘The’, ‘DT’), (‘cat’, ‘NN’), (‘is’, ‘VBZ’), (‘under’, ‘IN’), (‘the’, ‘DT’), (‘table’, ‘NN’)) where DT is the tag for determiners, NN for nouns, VBZ for verbs, and IN for prepositions. For every transcript, we calculated the POS-Tag information (with NLTK5) and used the frequencies of each tag as an additional attribute of the text. Tagging automation uses a hand-tagged corpus to train a parsing process using a variety of heuristics. The Trend Graph displays the fastest rising and fading topics in your feeds or all user feeds over whatever period of time you select.

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As experienced marketers, we come prepackaged with a deep understanding of – and fascination with – psychology and our audience, meaning we’ve already got the skills on paper to analyse our content. We also know that analysing our data to very specifically target audiences is crucial for great ROI. But we rarely put the two together and use the data available to actually analyse what content works – and why. This lack of knowledge – despite all the tools and techniques we use to offer insight – is what we at Datasine call the ‘black box’ because when it comes to understanding why, we are left in the dark. Just looking at results doesn’t give us the insight needed to truly understand content preferences in an actionable way. On a daily basis, we’re faced with countless blogs, podcasts, speakers and everything in-between promising that if we perfectly optimise our targeting, our messaging will beat the daunting odds of the 0.9% CTR cited by WordStream.

All the authors reviewed the results, edited the manuscript, and gave final approval for submission of the manuscript. Drs Corcoran and Cecchi had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. With these two features, we were able to characterize semantic coherence by measuring components of the distributions of first- and second-order coherence over the speech samples, including features such as the minimum, mean, median, and s.d. Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals.