Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders.
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ABSTRACT:
SUBMITTER: Tang SX
PROVIDER: S-EPMC8121795 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundHealth researchers are increasingly using natural language processing (NLP) to study various mental health conditions using both social media and electronic health records (EHRs). There is currently no published synthesis that relates specifically to the use of NLP methods for bipolar disorder, and this scoping review was conducted to synthesize valuable insights that have been presented in the literature.ObjectiveThis scoping review explored how NLP methods have been used in research to better understand bipolar disorder and identify opportunities for further use of these methods.MethodsA systematic, computerized search of index and free-text terms related to bipolar disorder and NLP was conducted using 5 databases and 1 anthology: MEDLINE, PsycINFO, Academic Search Ultimate, Scopus, Web of Science Core Collection, and the ACL Anthology.ResultsOf 507 identified studies, a total of 35 (6.9%) studies met the inclusion criteria. A narrative synthesis was used to describe the data, and the studies were grouped into four objectives: prediction and classification (n=25), characterization of the language of bipolar disorder (n=13), use of EHRs to measure health outcomes (n=3), and use of EHRs for phenotyping (n=2). Ethical considerations were reported in 60% (21/35) of the studies.ConclusionsThe current literature demonstrates how language analysis can be used to assist in and improve the provision of care for people living with bipolar disorder. Individuals with bipolar disorder and the medical community could benefit from research that uses NLP to investigate risk-taking, web-based services, social and occupational functioning, and the representation of gender in bipolar disorder populations on the web. Future research that implements NLP methods to study bipolar disorder should be governed by ethical principles, and any decisions regarding the collection and sharing of data sets should ultimately be made on a case-by-case basis, considering the risk to the data participants and whether their privacy can be ensured.
| S-EPMC9077496 | biostudies-literature
Project description:In terms of thought disorder, the language of patients with schizophrenia itself could be a valuable resource. Some valuable studies on the language of patients with schizophrenia have been performed. However, most such studies have been confined to English-speaking countries, or at least those where Indo-European languages are spoken. Therefore, we investigated linguistic anomalies in the language of Korean patients with schizophrenia. Short texts written by 69 patients with schizophrenia from a single mental hospital and matched normal control participants were analyzed. We evaluated these texts in terms of semantic and syntactic errors. Then, we compared the error rates adjusted for text length between patients and normal control participants. We also divided the patients with schizophrenia into two groups by their duration of illness and compared these two groups to investigate the relationship between the duration of illness and linguistic anomalies. The patients with schizophrenia committed a total of 1.86 (2.52) semantic errors and 1.37 (1.79) syntactic errors per 100 characters, which were significantly more frequent than errors committed by normal control participants. Furthermore, there was a notably high number of semantic errors relative to syntactic errors in the language of patients with schizophrenia. Our study results are consistent with previous studies from English-speaking countries, implying that the linguistic anomalies of patients with schizophrenia are not confined to a single language. Because language is essential in mental function, further research on linguistic anomalies in patients with schizophrenia is recommended.
| S-EPMC9661510 | biostudies-literature
Project description:In social, personality and mental health research, the tendency to select absolute end-points on Likert scales has been linked to certain cultures, lower intelligence, lower income and personality/mental health disorders. It is unclear whether this response style reflects an absolutist cognitive style or is merely an experimental artefact. In this study, we introduce an alternative, more informative, flexible and ecologically valid approach for estimating absolute responding, that uses natural language markers. We focussed on 'function words' (e.g. particles, conjunctions, prepositions) as they are more generalizable because they do not depend on any specific context. To identify such linguistic markers and test their generalizability, we conducted a text analysis of online reviews for films, tourist attractions and consumer products. All written reviews were accompanied by a rating scale (akin to Likert scale), which allowed us to label text samples as absolute/moderate. The data was split into independent 'training' and 'test' sets. Using the training set we identified a rank order of linguistic markers for absolute and moderate text, which were evaluated in a classifier on the test set. The top three markers alone ("but", "!" and "seem") produced 88% classification accuracy, which increased to 91% using 31 linguistic markers.
| S-EPMC6085512 | biostudies-literature
Project description:BackgroundLanguage development is a highly interactive activity. However, most research on linguistic environment has focused on the quantity and complexity of linguistic input to children, with current models showing that complexity facilitates language in both typically developing (TD) and autistic children.AimsAfter reviewing existing work on caregiver engagement of children's utterances, we aim to operationalize such engagement with automated measures of linguistic alignment, thereby providing scalable tools to assess caregivers' active reuse of their children's language. By assessing the presence of alignment, its sensitivity to the child's individual differences and how well it predicts language development beyond current models across the two groups, we showcase the usefulness of the approach and provide initial empirical foundations for further conceptual and empirical investigations.MethodsWe measure lexical, syntactic and semantic types of caregiver alignment in a longitudinal corpus involving 32 adult-autistic child and 35 adult-TD child dyads, with children between 2 and 5 years of age. We assess the extent to which caregivers repeat their children's words, syntax, and semantics, and whether these repetitions predict language development beyond more standard predictors.ResultsCaregivers tend to re-use their child's language in a way that is related to the child's individual, primarily linguistic, differences. Caregivers' alignment provides unique information improving our ability to predict future language development in both typical and autistic children.ConclusionsWe provide evidence that language development also relies on interactive conversational processes, previously understudied. We share carefully detailed methods, and open-source scripts so as to systematically extend our approach to new contexts and languages.
| S-EPMC11223773 | biostudies-literature
Project description:The adverse metabolic risks associated with second generation antipsychotics (SGAs) are well known, and likely contribute to the high rate of premature mortality due to cardiovascular disease in schizophrenia. Female schizophrenia patients appear to be diagnosed with metabolic diseases at higher rates than males, which may reflect disparate adverse responses to SGAs. However, the relationship between sex, metabolic risk, and drug use is less developed. We aimed to explore this relationship further by identifying rates of metabolic disease in community dwelling schizophrenia patients by sex and SGA risk. Schizophrenia participants (N = 287, 40.4% female) were included in this analysis. Oneway-ANOVA and Fisher's Exact Test were used to compare groups, as appropriate, and Cohen's d was employed to estimate the effect size of sex. In the group as a whole, the rate of metabolic syndrome was higher than previously reported, but did not differ by sex. For females, greater metabolic disturbances across all medication risk groups were seen in BMI and waist circumference (p < 0.005) but most commonly in those receiving high risk medication (clozapine or olanzapine). Additionally, the number of participants receiving medications for these metabolic disturbances was extremely low (<30%). These results suggest that female schizophrenia patients taking clozapine or olanzapine represent a group at uniquely high risk for metabolic dysfunction and future adverse cardiovascular outcomes, and warrant close monitoring by clinicians to prevent worsening of metabolic risk through proper monitoring and interventions.
| S-EPMC5472174 | biostudies-literature
Project description:Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clinical care that may be used to improve outcomes and understanding of disease in cardiology. The purpose of this systematic review is to provide an understanding of NLP, review how it has been used to date within cardiology and illustrate the opportunities that this approach provides for both research and clinical care. We systematically searched six scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, PubMed and Scopus) for studies published in 2015-2020 describing the development or application of NLP methods for clinical text focused on cardiac disease. Studies not published in English, lacking a description of NLP methods, non-cardiac focused and duplicates were excluded. Two independent reviewers extracted general study information, clinical details and NLP details and appraised quality using a checklist of quality indicators for NLP studies. We identified 37 studies developing and applying NLP in heart failure, imaging, coronary artery disease, electrophysiology, general cardiology and valvular heart disease. Most studies used NLP to identify patients with a specific diagnosis and extract disease severity using rule-based NLP methods. Some used NLP algorithms to predict clinical outcomes. A major limitation is the inability to aggregate findings across studies due to vastly different NLP methods, evaluation and reporting. This review reveals numerous opportunities for future NLP work in cardiology with more diverse patient samples, cardiac diseases, datasets, methods and applications.
| S-EPMC9046466 | biostudies-literature
Project description:Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.
| S-EPMC9371745 | biostudies-literature
Project description:Motivational interviewing (MI) is an efficacious treatment for substance use disorders and other problem behaviors. Studies on MI fidelity and mechanisms of change typically use human raters to code therapy sessions, which requires considerable time, training, and financial costs. Natural language processing techniques have recently been utilized for coding MI sessions using machine learning techniques, rather than human coders, and preliminary results have suggested these methods hold promise. The current study extends this previous work by introducing two natural language processing models for automatically coding MI sessions via computer. The two models differ in the way they semantically represent session content, utilizing either 1) simple discrete sentence features (DSF model) and 2) more complex recursive neural networks (RNN model). Utterance- and session-level predictions from these models were compared to ratings provided by human coders using a large sample of MI sessions (N=341 sessions; 78,977 clinician and client talk turns) from 6 MI studies. Results show that the DSF model generally had slightly better performance compared to the RNN model. The DSF model had "good" or higher utterance-level agreement with human coders (Cohen's kappa>0.60) for open and closed questions, affirm, giving information, and follow/neutral (all therapist codes); considerably higher agreement was obtained for session-level indices, and many estimates were competitive with human-to-human agreement. However, there was poor agreement for client change talk, client sustain talk, and therapist MI-inconsistent behaviors. Natural language processing methods provide accurate representations of human derived behavioral codes and could offer substantial improvements to the efficiency and scale in which MI mechanisms of change research and fidelity monitoring are conducted.
| S-EPMC4842096 | biostudies-literature
Project description:BackgroundDelirium is an acute neurocognitive disorder that affects up to half of older hospitalized medical patients and can lead to dementia, longer hospital stays, increased health costs, and death. Although delirium can be prevented and treated, it is difficult to identify and predict.ObjectiveThis study aimed to improve machine learning models that retrospectively identify the presence of delirium during hospital stays (eg, to measure the effectiveness of delirium prevention interventions) by using the natural language processing (NLP) technique of sentiment analysis (in this case a feature that identifies sentiment toward, or away from, a delirium diagnosis).MethodsUsing data from the General Medicine Inpatient Initiative, a Canadian hospital data and analytics network, a detailed manual review of medical records was conducted from nearly 4000 admissions at 6 Toronto area hospitals. Furthermore, 25.74% (994/3862) of the eligible hospital admissions were labeled as having delirium. Using the data set collected from this study, we developed machine learning models with, and without, the benefit of NLP methods applied to diagnostic imaging reports, and we asked the question "can NLP improve machine learning identification of delirium?"ResultsAmong the eligible 3862 hospital admissions, 994 (25.74%) admissions were labeled as having delirium. Identification and calibration of the models were satisfactory. The accuracy and area under the receiver operating characteristic curve of the main model with NLP in the independent testing data set were 0.807 and 0.930, respectively. The accuracy and area under the receiver operating characteristic curve of the main model without NLP in the independent testing data set were 0.811 and 0.869, respectively. Model performance was also found to be stable over the 5-year period used in the experiment, with identification for a likely future holdout test set being no worse than identification for retrospective holdout test sets.ConclusionsOur machine learning model that included NLP (ie, sentiment analysis in medical image description text mining) produced valid identification of delirium with the sentiment analysis, providing significant additional benefit over the model without NLP.
| S-EPMC9812273 | biostudies-literature
Project description:BackgroundWith the increased sharing of electronic health information as required by the US 21st Century Cures Act, there is an increased risk of breaching patient, parent, or guardian confidentiality. The prevalence of sensitive terms in clinical notes is not known.ObjectiveThe aim of this study is to define sensitive terms that represent the documentation of content that may be private and determine the prevalence and characteristics of provider notes that contain sensitive terms.MethodsUsing keyword expansion, we defined a list of 781 sensitive terms. We searched all provider history and physical, progress, consult, and discharge summary notes for patients aged 0-21 years written between January 1, 2019, and December 31, 2019, for a direct string match of sensitive terms. We calculated the prevalence of notes with sensitive terms and characterized clinical encounters and patient characteristics.ResultsSensitive terms were present in notes from every clinical context in all pediatric ages. Terms related to the mental health category were most used overall (254,975/1,338,297, 19.5%), but terms related to substance abuse and reproductive health were most common in patients aged 0-3 years. History and physical notes (19,854/34,771, 57.1%) and ambulatory progress notes (265,302/563,273, 47.1%) were most likely to include sensitive terms. The highest prevalence of notes with sensitive terms was found in pain management (950/1112, 85.4%) and child abuse (1092/1282, 85.2%) clinics.ConclusionsNotes containing sensitive terms are not limited to adolescent patients, specific note types, or certain specialties. Recognition of sensitive terms across all ages and clinical settings complicates efforts to protect patient and caregiver privacy in the era of information-blocking regulations.
| S-EPMC9233261 | biostudies-literature