scholarly journals Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study (Preprint)

2020 ◽  
Author(s):  
Michael Leo Birnbaum ◽  
Prathamesh "Param" Kulkarni ◽  
Anna Van Meter ◽  
Victor Chen ◽  
Asra F Rizvi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Digital Data ◽  
Diagnostic Process ◽  
Schizophrenia Spectrum Disorders ◽  
Internet Search ◽  
Online Search ◽  
Search Activity ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders

BACKGROUND Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions. OBJECTIVE We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders. METHODS We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity. RESULTS Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health. CONCLUSIONS Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.

scholarly journals Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

10.2196/19348 ◽  
2020 ◽  
Vol 7 (9) ◽  
pp. e19348
Author(s):  
Michael Leo Birnbaum ◽  
Prathamesh "Param" Kulkarni ◽  
Anna Van Meter ◽  
Victor Chen ◽  
Asra F Rizvi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Digital Data ◽  
Diagnostic Process ◽  
Schizophrenia Spectrum Disorders ◽  
Internet Search ◽  
Online Search ◽  
Search Activity ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders

Background Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions. Objective We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders. Methods We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity. Results Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health. Conclusions Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.

Self-Harm Among Forensic Psychiatric Inpatients With Schizophrenia Spectrum Disorders: An Explorative Analysis

2021 ◽  
pp. 0306624X2110621
Author(s):  
Johannes René Kappes ◽  
David Alen Huber ◽  
Johannes Kirchebner ◽  
Martina Sonnweber ◽  
Moritz Philipp Günther ◽  
...  
Keyword(s):  
Machine Learning ◽  
Forensic Psychiatry ◽  
Supervised Machine Learning ◽  
Depression And Anxiety ◽  
Schizophrenia Spectrum Disorders ◽  
Forensic Patients ◽  
Self Injury ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders ◽  
Forensic Psychiatric

The burden of self-injury among offenders undergoing inpatient treatment in forensic psychiatry is substantial. This exploratory study aims to add to the previously sparse literature on the correlates of self-injury in inpatient forensic patients with schizophrenia spectrum disorders (SSD). Employing a sample of 356 inpatients with SSD treated in a Swiss forensic psychiatry hospital, patient data on 512 potential predictor variables were retrospectively collected via file analysis. The dataset was examined using supervised machine learning to distinguish between patients who had engaged in self-injurious behavior during forensic hospitalization and those who had not. Based on a combination of ten variables, including psychiatric history, criminal history, psychopathology, and pharmacotherapy, the final machine learning model was able to discriminate between self-injury and no self-injury with a balanced accuracy of 68% and a predictive power of AUC = 71%. Results suggest that forensic psychiatric patients with SSD who self-injured were younger both at the time of onset and at the time of first entry into the federal criminal record. They exhibited more severe psychopathological symptoms at the time of admission, including higher levels of depression and anxiety and greater difficulty with abstract reasoning. Of all the predictors identified, symptoms of depression and anxiety may be the most promising treatment targets for the prevention of self-injury in inpatient forensic patients with SSD due to their modifiability and should be further substantiated in future studies.

scholarly journals S136. CLASSIFYING SCHIZOPHRENIA USING PHONOLOGICAL, SEMANTIC AND SYNTACTIC FEATURES OF LANGUAGE; A COMBINATORY MACHINE LEARNING APPROACH

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S87-S87
Author(s):  
Alban Voppel ◽  
Janna de Boer ◽  
Fleur Slegers ◽  
Hugo Schnack ◽  
Iris Sommer
Keyword(s):  
Machine Learning ◽  
Computational Linguistics ◽  
Parental Education ◽  
Feature Reduction ◽  
Sentence Length ◽  
Healthy Controls ◽  
Schizophrenia Spectrum Disorders ◽  
Syntactic Features ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders

Abstract Background The diagnosis of schizophrenia is currently based on anamnesis and psychiatric examination only. Language biomarkers may be useful to provide a quantitative and reproducible risk estimate for this spectrum of disorders. While people with schizophrenia spectrum disorders may show one or more language abnormalities, such as incoherence, affective flattening, failure of reference as well as changes in sentence length and complexity, the clinical picture can vary largely between individuals and language abnormalities will reflect this heterogeneity. Computational linguistics can be used to quantify these features of language. Because of the heterogeneous character of the various symptoms present in schizophrenia spectrum subjects, we expect some subjects to show semantic incoherence, while others may have more affective symptoms such as monotonous speech. Here, we combine phonological, semantic and syntactic features of semi-spontaneous language with machine learning algorithms for classification in order to develop a biomarker sensitive to the broad spectrum of schizophrenia. Methods Semi-spontaneous natural language samples were collected from 50 subjects with schizophrenia spectrum disorders and 50 age, gender and parental education matched controls, using recorded neutral-topic, open-ended interviews. The audio samples were speaker coded; audio belonging to the subject was extracted and transcribed. Phonological features were extracted using OpenSMILE; semantic features were calculated using a word2vec model using a moving windows of coherence approach, and finally syntactic aspects were calculated using the T-scan tool. Feature reduction was applied to each of the domains. To distinguish groups, results from machine learning classifiers trained using leave-one-out cross-validation on each of these aspects were combined, incorporating a voting mechanism. Results The machine-learning classifier approach obtained 75–78% accuracy for the semantic, syntactic and phonological domains individually. As most distinguishing features of their respective domain, we found reduced timbre and intonation for the phonological domain, increased variance of coherence for the semantic domain and decreased complexity of speech in the syntactic domain. The combined approach, using a voting algorithm across the domains, achieved an accuracy of 83% and a precision score of 89%. No significant differences in age, gender or parental education between healthy controls and subjects with schizophrenia spectrum disorders was found. Discussion In this study we demonstrated that computational features derived from different linguistic domains capture aspects of symptomatic language of schizophrenia spectrum disorder subjects. The combination of these features was useful to improve classification for this heterogeneous disorder, as we showed high accuracy and precision from the language parameters in distinguishing schizophrenia patients from healthy controls. These values are better than those obtained with imaging or blood analyses, while language is a more easily obtained and cheaper measure than those derived from other methods. Validation in an independent sample is required, and further features of differentiation should be extracted for their respective domains. Our positive results in using language abnormalities to automatically detect schizophrenia show that computational linguistics is a promising method in the search for reliable markers in psychiatry.

scholarly journals Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Michael L. Birnbaum ◽  
Raquel Norel ◽  
Anna Van Meter ◽  
Asra F. Ali ◽  
Elizabeth Arenare ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Negative Emotion ◽  
Clinical Information ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Schizophrenia Spectrum Disorders ◽  
Real Patient ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders

AbstractPrior research has identified associations between social media activity and psychiatric diagnoses; however, diagnoses are rarely clinically confirmed. Toward the goal of applying novel approaches to improve outcomes, research using real patient data is necessary. We collected 3,404,959 Facebook messages and 142,390 images across 223 participants (mean age = 23.7; 41.7% male) with schizophrenia spectrum disorders (SSD), mood disorders (MD), and healthy volunteers (HV). We analyzed features uploaded up to 18 months before the first hospitalization using machine learning and built classifiers that distinguished SSD and MD from HV, and SSD from MD. Classification achieved AUC of 0.77 (HV vs. MD), 0.76 (HV vs. SSD), and 0.72 (SSD vs. MD). SSD used more (P < 0.01) perception words (hear, see, feel) than MD or HV. SSD and MD used more (P < 0.01) swear words compared to HV. SSD were more likely to express negative emotions compared to HV (P < 0.01). MD used more words related to biological processes (blood/pain) compared to HV (P < 0.01). The height and width of photos posted by SSD and MD were smaller (P < 0.01) than HV. MD photos contained more blues and less yellows (P < 0.01). Closer to hospitalization, use of punctuation increased (SSD vs HV), use of negative emotion words increased (MD vs. HV), and use of swear words increased (P < 0.01) for SSD and MD compared to HV. Machine-learning algorithms are capable of differentiating SSD and MD using Facebook activity alone over a year in advance of hospitalization. Integrating Facebook data with clinical information could one day serve to inform clinical decision-making.

Stress, Schizophrenia, and Violence: A Machine Learning Approach

2020 ◽  
pp. 088626052091364 ◽  
Author(s):  
Johannes Kirchebner ◽  
Martina Sonnweber ◽  
Urs M. Nater ◽  
Moritz Günther ◽  
Steffen Lau
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
University Hospital ◽  
Schizophrenia Spectrum Disorders ◽  
Violent Offending ◽  
Schizophrenia Spectrum ◽  
Patients At Risk ◽  
Spectrum Disorders ◽  
Violent Offenses

This study employs machine learning algorithms to examine the causes for engaging in violent offending in individuals with schizophrenia spectrum disorders. Data were collected from 370 inpatients at the Centre for Inpatient Forensic Therapy, Zurich University Hospital of Psychiatry, Switzerland. Based on findings of the general strain theory and using logistic regression and machine learning algorithms, it was analyzed whether accumulation and type of stressors in the inpatients’ history influenced the severity of an offense. A higher number of stressors led to more violent offenses, and five types of stressors were identified as being highly influential regarding violent offenses. Our findings suggest that an accumulation of stressful experiences in the course of life and certain types of stressors might be particularly important in the development of violent offending in individuals suffering from schizophrenia spectrum disorders. A better understanding of risk factors that lead to violent offenses should be helpful for the development of preventive and therapeutic strategies for patients at risk and could thus potentially reduce the prevalence of violent offenses.

Structured Interview for Hoarding Disorder (SIHD)

2019 ◽  
Vol 35 (4) ◽  
pp. 512-520
Author(s):  
Caterina Novara ◽  
Paolo Cavedini ◽  
Stella Dorz ◽  
Susanna Pardini ◽  
Claudio Sica
Keyword(s):  
Convergent Validity ◽  
Structured Interview ◽  
Diagnostic Process ◽  
Schizophrenia Spectrum Disorders ◽  
Clinical Environment ◽  
Obsessive Compulsive ◽  
Rater Reliability ◽  
Hoarding Disorder ◽  
Compulsive Disorder ◽  
Schizophrenia Spectrum

Abstract. The Structured Interview for Hoarding Disorder (SIHD) is a semi-structured interview designed to assist clinicians in diagnosing a hoarding disorder (HD). This study aimed to validate the Italian version of the SIHD. For this purpose, its inter-rater reliability has been analyzed as well as its ability to differentiate HD from other disorders often comorbid. The sample was composed of 74 inpatients who had been diagnosed within their clinical environment: 9 with HD, 11 with obsessive-compulsive disorder (OCD) and HD, 22 with OCD, 19 with major depressive disorder (MDD), and 13 with schizophrenia spectrum disorders (SSD). The results obtained indicated “substantial” or “perfect” inter-rater reliability for all the core HD criteria, HD diagnosis, and specifiers. The SIHD differentiated between subjects suffering from and not suffering from a HD. Finally, the results indicated “good” convergent validity and high scores were shown in terms of both sensitivity and specificity for HD diagnosis. Altogether, the SIHD represents a useful instrument for evaluating the presence of HD and is a helpful tool for the clinician during the diagnostic process.

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