Detecting causality from online psychiatric texts using inter-sentential language patterns

Abstract

BackgroundOnline psychiatric texts are natural language texts expressing depressive problems, published by Internet users via community-based web services such as web forums, message boards and blogs. Understanding the cause-effect relations embedded in these psychiatric texts can provide insight into the authors’ problems, thus increasing the effectiveness of online psychiatric services.MethodsPrevious studies have proposed the use of word pairs extracted from a set of sentence pairs to identify cause-effect relations between sentences. A word pair is made up of two words, with one coming from the cause text span and the other from the effect text span. Analysis of the relationship between these words can be used to capture individual word associations between cause and effect sentences. For instance, (broke up, life) and (boyfriend, meaningless) are two word pairs extracted from the sentence pair: “I broke up with my boyfriend. Life is now meaningless to me”. The major limitation of word pairs is that individual words in sentences usually cannot reflect the exact meaning of the cause and effect events, and thus may produce semantically incomplete word pairs, as the previous examples show. Therefore, this study proposes the use of inter-sentential language patterns such as ≪broke up, boyfriend>, <life, meaningless≫ to detect causality between sentences. The inter-sentential language patterns can capture associations among multiple words within and between sentences, thus can provide more precise information than word pairs. To acquire inter-sentential language patterns, we develop a text mining framework by extending the classical association rule mining algorithm such that it can discover frequently co-occurring patterns across the sentence boundary.ResultsPerformance was evaluated on a corpus of texts collected from PsychPark (http://www.psychpark.org), a virtual psychiatric clinic maintained by a group of volunteer professionals from the Taiwan Association of Mental Health Informatics. Experimental results show that the use of inter-sentential language patterns outperformed the use of word pairs proposed in previous studies.ConclusionsThis study demonstrates the acquisition of inter-sentential language patterns for causality detection from online psychiatric texts. Such semantically more complete and precise features can improve causality detection performance.

Text mining applications in psychiatry: a systematic literature review

“…() used these messages to identify the associations between events and depressive episodes. Two studies conducted further investigation to automatically detect depressive symptoms from questions addressed by patients to Mentalhelp.net and PsychPark.org (Neuman et al ., ; Wu et al ., ). Screening natural language in texts is challenging, particularly on the Internet.…”

Machine learning in mental health: a scoping review of methods and applications

“…For example, several papers focused on the use of supervised ML techniques with neuroimaging data to differentiate Alzheimer's disease from normal ageing (Sheela Kumari et al ., 2014; Doan et al ., 2017 a ), to improve early diagnosis of psychosis (Koutsouleris et al ., 2012), and to predict vulnerability to depression (Sato et al ., 2015). A novel approach identified for detection of conditions is the use of unstructured text with natural language processing techniques, including detection of suicide ideation from counselling transcripts (Oseguera et al ., 2017), detection of schizophrenia from written texts (Strous et al ., 2009), and analysis of social media data to detect depressive symptoms (Wu et al ., 2012). Supervised ML has also been applied to wearable sensor data to assess general wellbeing (Sano et al ., 2015), and to ambient sensors to detect psychiatric emergencies (Alam et al ., 2016).…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…For example, several papers focused on the use of ML with neuroimaging data to differentiate Alzheimer's disease from normal ageing [6,7], to improve early diagnosis of psychosis [8], and to predict vulnerability to depression [9]. A novel approach identified for detection of conditions is the use of unstructured text, including detection of suicide ideation from counselling transcripts [10], detection of schizophrenia from written texts [11], and analysis of social media data to detect depressive symptoms [12]. ML has also been applied to wearable sensor data to assess general wellbeing [13], and to ambient, in-home sensors to detect psychiatric emergencies [14].…”