Using ChatGPT in Psychiatry to Design Script Concordance Tests in Undergraduate Medical Education: Mixed Methods Study.

Background: Undergraduate medical studies represent a wide range of learning opportunities served in the form of various teaching-learning modalities for medical learners. A clinical scenario is frequently used as a modality, followed by multiple-choice and open-ended questions among other learning and teaching methods. As such, script concordance tests (SCTs) can be used to promote a higher level of clinical reasoning. Recent technological developments have made generative artificial intelligence (AI)-based systems such as ChatGPT (OpenAI) available to assist clinician-educators in creating instructional materials. Objective: The main objective of this project is to explore how SCTs generated by ChatGPT compared to SCTs produced by clinical experts on 3 major elements: the scenario (stem), clinical questions, and expert opinion. Methods: This mixed method study evaluated 3 ChatGPT-generated SCTs with 3 expert-created SCTs using a predefined framework. Clinician-educators as well as resident doctors in psychiatry involved in undergraduate medical education in Quebec, Canada, evaluated via a web-based survey the 6 SCTs on 3 criteria: the scenario, clinical questions, and expert opinion. They were also asked to describe the strengths and weaknesses of the SCTs. Results: A total of 102 respondents assessed the SCTs. There were no significant distinctions between the 2 types of SCTs concerning the scenario (P=.84), clinical questions (P=.99), and expert opinion (P=.07), as interpretated by the respondents. Indeed, respondents struggled to differentiate between ChatGPT- and expert-generated SCTs. ChatGPT showcased promise in expediting SCT design, aligning well with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria, albeit with a tendency toward caricatured scenarios and simplistic content. Conclusions: This study is the first to concentrate on the design of SCTs supported by AI in a period where medicine is changing swiftly and where technologies generated from AI are expanding much faster. This study suggests that ChatGPT can be a valuable tool in creating educational materials, and further validation is essential to ensure educational efficacy and accuracy.

Enhancing Predictive Power: Integrating a Linear Support Vector Classifier with Logistic Regression for Patient Outcome Prognosis in Virtual Reality Therapy for Treatment-Resistant Schizophrenia.

(1) Background: Approximately 30% of schizophrenia patients are known to be treatment-resistant. For these cases, more personalized approaches must be developed. Virtual reality therapeutic approaches such as avatar therapy (AT) are currently undergoing investigations to address these patients' needs. To further tailor the therapeutic trajectory of patients presenting with this complex presentation of schizophrenia, quantitative insight about the therapeutic process is warranted. The aim of the study is to combine a classification model with a regression model with the aim of predicting the therapeutic outcomes of patients based on the interactions taking place during their first immersive session of virtual reality therapy. (2) Methods: A combination of a Linear Support Vector Classifier and logistic regression was conducted over a dataset comprising 162 verbatims of the immersive sessions of 18 patients who previously underwent AT. As a testing dataset, 17 participants, unknown to the dataset, had their first immersive session presented to the combinatory model to predict their clinical outcome. (3) Results: The model accurately predicted the clinical outcome for 15 out of the 17 participants. Classification of the therapeutic interactions achieved an accuracy of 63%. (4) Conclusion: To our knowledge, this is the first attempt to predict the outcome of psychotherapy patients based on the content of their interactions with their therapist. These results are important as they open the door to personalization of psychotherapy based on quantitative information about the interactions taking place during AT.

The Use of Alternative Rooms in Forensic and Regular Psychiatric Units: A Scoping Review.

(1) Background: Emotional regulation, distress and relational conflicts often occur during hospitalization and rehabilitation on psychiatric units, especially in patients suffering from severe and persistent mental disorders. While widely used in children and geriatric patients, little literature exists on the use and outcomes of alternative rooms in the context of forensic and regular psychiatric units for adult patients. Considering the scarcity of the literature on alternative use, this study is motivated by the following research question: what are the main uses and outcomes of alternative rooms in the context of forensic and regular psychiatric units? The main objective of this study is to conduct a scoping review of the use and outcomes of alternative rooms for the context of psychiatric inpatients. (2) Methods: A systematic search was performed in the electronic databases of MedLine, Web of Science, PsycNet (PsycINFO) and Google Scholar from their inception dates until 2022. (3) Results: A total of nine studies were analyzed. Sensory, multisensory rooms, Snoezelen, and comfort rooms are the types of alternative rooms discussed in these studies. Distress and anxiety reduction, increase in self-esteem, impact on seclusion rates, patient-staff communication and alliances, heart and respiration rate reduction, and improvement of alexithymia were identified among the main uses and outcomes of these rooms. (4) Conclusions: The scarcity of literature available to draw information from for this review and possible impact on improving patient outcomes and quality of treatment in psychiatric units opens the door to future studies to better understand the efficacy of such rooms. Research into the ideal implementation tactics of such rooms, long-term outcomes, and the influence on diverse patient demographics could be areas of improvement in the use of alternative rooms.

Exploration of the role of emotional expression of treatment-resistant schizophrenia patients having followed virtual reality therapy: a content analysis.

BACKGROUND: Emotional responses are an important component of psychotherapeutic processes. Avatar therapy (AT) is a virtual reality-based therapy currently being developed and studied for patients suffering from treatment resistant schizophrenia. Considering the importance of identifying emotions in therapeutical processes and their impact on the therapeutic outcome, an exploration of such emotions is needed. METHODS: The aim of this study is to identify the underlying emotions at the core of the patient-Avatar interaction during AT by content analysis of immersive sessions transcripts and audio recordings. A content analysis of AT transcripts and audio recordings using iterative categorization was conducted for 16 patients suffering from TRS who underwent AT between 2017 and 2022 (128 transcripts and 128 audio recordings). An iterative categorization technique was conducted to identify the different emotions expressed by the patient and the Avatar during the immersive sessions. RESULTS: The following emotions were identified in this study: Anger, Contempt/ Disgust, Fear, Sadness, Shame/ Embarrassment, Interest, Surprise, Joy and Neutral. Patients expressed mostly neutral, joy and anger emotions whereas the Avatar expressed predominantly interest, disgust/contempt, and neutral emotions. CONCLUSIONS: This study portrays a first qualitative insight on the emotions that are expressed in AT and serves as a steppingstone for further investigation in the role of emotions in the therapeutic outcomes of AT.

Unsupervised Machine Learning Driven Analysis of Verbatims of Treatment-Resistant Schizophrenia Patients Having Followed Avatar Therapy.

(1) Background: The therapeutic mechanisms underlying psychotherapeutic interventions for individuals with treatment-resistant schizophrenia are mostly unknown. One of these treatment techniques is avatar therapy (AT), in which the patient engages in immersive sessions while interacting with an avatar representing their primary persistent auditory verbal hallucination. The aim of this study was to conduct an unsupervised machine-learning analysis of verbatims of treatment-resistant schizophrenia patients that have followed AT. The second aim of the study was to compare the data clusters obtained from the unsupervised machine-learning analysis with previously conducted qualitative analysis. (2) Methods: A k-means algorithm was performed over the immersive-session verbatims of 18 patients suffering from treatment-resistant schizophrenia who followed AT to cluster interactions of the avatar and the patient. Data were pre-processed using vectorization and data reduction. (3): Results: Three clusters of interactions were identified for the avatar's interactions whereas four clusters were identified for the patient's interactions. (4) Conclusion: This study was the first attempt to conduct unsupervised machine learning on AT and provided a quantitative insight into the inner interactions that take place during immersive sessions. The use of unsupervised machine learning could yield a better understanding of the type of interactions that take place in AT and their clinical implications.

Dyadic Interactions of Treatment-Resistant Schizophrenia Patients Having Followed Virtual Reality Therapy: A Content Analysis.

(1) Background: Very little is known about the inner therapeutic processes of psychotherapy interventions for patients suffering from treatment-resistant schizophrenia. Avatar therapy (AT) is one such modalities in which the patient is undergoing immersive sessions in which they interact with an Avatar representing their main persistent auditory verbal hallucination. The aim of this study is to identify the most prevalent dyadic interactions between the patient and the Avatar in AT for patient's suffering from TRS. (2) Methods: A content analysis of 256 verbatims originating from 32 patients who completed AT between 2017 and 2022 at the Institut universitaire en santé mentale de Montréal was conducted to identify dyadic interactions between the patients and their Avatar. (3) Results: Five key dyads were identified to occur on average more than 10 times for each participant during the immersive sessions across their AT: (Avatar: Reinforcement, Patient: Self-affirmation), (Avatar: Provocation, Patient: Self-affirmation), (Avatar: Coping mechanisms, Patient: Prevention), (Patient: Self-affirmation, Avatar: Reinforcement), and (Patient: Self-appraisal, Avatar: Reinforcement). (4) Conclusion: These dyads offer a first qualitative insight to the interpersonal dynamics and patient-avatar relationships taking place during AT. Future studies on the implication of such dyadic interactions with the therapeutic outcome of AT should be conducted considering the importance of dyadic relationships in psychotherapy.

Association Between Cannabis and Violence in Community-Dwelling Patients With Severe Mental Disorders: A Cross-sectional Study Using Machine Learning.

ABSTRACT: The objective of this cross-sectional study was to identify cannabis-related features and other characteristics predictive of violence using a data-driven approach in patients with severe mental disorders (SMDs). A Least Absolute Shrinkage and Selection Operator regularization regression model was used on the database consisting of 97 patients with SMD who completed questionnaires measuring substance use and violence. Cannabis use, particularly related to patients' decision to consume or time spent using, was a key predictor associated with violence. Other patterns of substance use and personality traits were identified as strong predictors. Regular patterns of cannabis use and interpersonal issues related to cannabis/stimulant abuse were inversely correlated to violence. This study identified the effect of several predictors correlated to violence in patients with SMD using a regularization regression model. Findings open the door to better identify the profiles of patients that may be more susceptible to perpetrate violent behaviors.

Implementation of a machine learning algorithm for automated thematic annotations in avatar: A linear support vector classifier approach.

Avatar Therapy (AT) is a modern therapeutic alternative for patients with schizophrenia suffering from persistent auditory verbal hallucinations. Its intrinsic therapeutical process is currently qualitatively analyzed via human coders that annotate session transcripts. This process is time and resource demanding. This creates a need to find potential algorithms that can operate on small datasets and perform such annotations. The first objective of this study is to conduct the automated text classification of interactions in AT and the second objective is to assess if this classification is comparable to the classification done by human coders. A Linear Support Vector Classifier was implemented to perform automated theme classifications on Avatar Therapy session transcripts with the use of a limited dataset with an accuracy of 66.02% and substantial classification agreement of 0.647. These results open the door to additional research such as predicting the outcome of a therapy.

The impacts of the COVID-19 pandemic on treatment-resistant schizophrenia patients having followed virtual reality therapy or cognitive behavioural therapy: a content analysis.

PURPOSE: The COVID-19 pandemic led to exacerbation of mental health symptoms and deterioration in psychological well-being in individuals suffering from schizophrenia. The primary objective of this study is to evaluate the impacts of the COVID-19 pandemic on patients suffering from treatment-resistant schizophrenia (TRS) with auditory verbal hallucinations (AVH) having undergone virtual reality therapy (VRT) or cognitive behavioural therapy (CBT) on their symptomatology. The secondary objective is to identify the differences and similarities in relation to the response to the COVID 19 pandemic between these two groups of patients. METHODS: Qualitative analysis of semi-structured interviews was conducted with 42 patients suffering from TRS who had previously followed VRT or CBT. All interviews were recorded, transcribed, and analysed. RESULTS: Four themes emerged in this study: Psychotherapeutic Interventions, Impact of COVID-19 and Public health and safety policies, Substance use and Psychiatric follow-up. Participants from both groups reported that their therapy was beneficial in controlling AVH. Patients having followed CBT reported more depressive symptoms whereas patients having followed VRT reported more anxious symptoms. CONCLUSIONS: This study offers a first qualitative insight in patients suffering from TRS and the impacts of COVID-19 on them and opens the door to the protective factors of CBT and VRT for this specific population.

The Use of Computer-Driven Technologies in the Treatment of Borderline Personality Disorder: A Systematic Review.

The objective of this study was to perform a systematic review of the effectiveness of computer-driven technologies for treatment of patients suffering from BPD. A systematic literature review was conducted using the Pubmed, EMBASE, PsycNET (PsycINFO), CINAHL and Google Scholar electronic databases for the period from their inception dates until 2022. Thirty studies were selected for abstract screening. Seven studies were excluded for not meeting inclusion criteria. The remaining 23 studies were fully assessed, and 12 were excluded. Therefore, 11 studies were included in the analysis of the effectiveness of computer-driven technologies, which encompassed mobile applications, telehealth interventions, internet-based interventions, virtual reality MBT and dialogue-based integrated interventions. Computer-driven interventions are showing signs of effectiveness in the treatment of BPD symptoms. The limited number of articles found on the subject demonstrates a need for further exploration of this subject.

Prediction of quality of life in schizophrenia using machine learning models on data from Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial.

While research focus remains mainly on psychotic symptoms, it is questionable whether we are placing enough emphasis on improving the quality of life (QoL) of schizophrenia patients. To date, the predictive power of QoL remained limited. Therefore, this study aimed to accurately predict the QoL within schizophrenia using supervised learning methods. The authors report findings from participants of a large randomized, double-blind clinical trial for schizophrenia treatment. Potential predictors of QoL included all available and non-redundant variables from the dataset. By optimizing parameters, three linear LASSO regressions were calculated (N = 697, 692, and 786), including 44, 47, and 41 variables, with adjusted R-squares ranging from 0.31 to 0.36. Best predictors included social and emotion-related symptoms, neurocognition (processing speed), education, female gender, treatment attitudes, and mental, emotional, and physical health. These results demonstrate that machine learning is an excellent predictive tool to process clinical data. It appears that the patient's perception of their treatment has an important impact on patients' QoL and that interventions should consider this aspect.Trial registration: ClinicalTrials.gov Identifier: NCT00014001.

[Insomnia and suicide risk in cluster B personality disorder: A comparative cross-sectional study]. / Insomnie et risque suicidaire dans les troubles de la personnalité du groupe B : une étude comparative transversale.

Objective The suicide mortality rate among people suffering from cluster B personality disorders is estimated at approximately 20%. High occurrence of comorbid depression and anxiety, as well as substance abuse, are known contributors to this risk. Not only have recent studies indicated that insomnia may be a suicide risk factor, but it is also thought to be highly prevalent in this clinical group. However, the mechanisms explaining this association are still unknown. It has been suggested that emotion dysregulation and impulsivity may mediate the link between insomnia and suicide. In order to better understand the association between insomnia and suicide in cluster B personality disorders, it is important to consider the influence of comorbidities. The aims of this study were first to compare the levels of insomnia symptoms and impulsivity between a group of patients with cluster B personality disorder and a healthy control group and second, to measure the relationships between insomnia, impulsivity, anxiety, depression, substance abuse and suicide risk within the cluster B personality disorder sample. Methods Cross-sectional study including 138 patients (mean age = 33.74; 58.7% women) with cluster B personality disorder. Data from this group were extracted from a Quebec-based mental health institution database (Signature bank: www.banquesignature.ca) and were compared to that of 125 healthy subjects matched for age and sex, with no history of personality disorder. Patient diagnosis was determined by diagnostic interview upon admission to a psychiatric emergency service. Anxiety, depression, impulsivity and substance abuse were also assessed at that time point via self-administered questionnaires. Participants from the control group visited the Signature center to complete the questionnaires. A correlation matrix and multiple linear regression models were used to explore relations between variables. Results In general, more severe insomnia symptoms and higher levels of impulsivity distinguished the group of patients with cluster B personality from the sample of healthy subjects, although groups did not differ on total sleep time. When all variables were included as predictors in a linear regression model to estimate suicide risk, subjective sleep quality, lack of premeditation, positive urgency, depression level and substance use were significantly associated with higher scores on the Suicidal Questionnaire-Revised (SBQ-R). The model explained 46.7% of the variance of scores at the SBQ-R. Conclusion This study yields preliminary evidence indicating the possible implication of insomnia and impulsivity in suicide risk for individuals with cluster B personality disorder. It is proposed that this association seems to be independent of comorbidity and substance use levels. Future studies may shed light on the possible clinical relevance of addressing insomnia and impulsivity in this clinical population.

Use of Automated Thematic Annotations for Small Data Sets in a Psychotherapeutic Context: Systematic Review of Machine Learning Algorithms.

BACKGROUND: A growing body of literature has detailed the use of qualitative analyses to measure the therapeutic processes and intrinsic effectiveness of psychotherapies, which yield small databases. Nonetheless, these approaches have several limitations and machine learning algorithms are needed. OBJECTIVE: The objective of this study is to conduct a systematic review of the use of machine learning for automated text classification for small data sets in the fields of psychiatry, psychology, and social sciences. This review will identify available algorithms and assess if automated classification of textual entities is comparable to the classification done by human evaluators. METHODS: A systematic search was performed in the electronic databases of Medline, Web of Science, PsycNet (PsycINFO), and Google Scholar from their inception dates to 2021. The fields of psychiatry, psychology, and social sciences were selected as they include a vast array of textual entities in the domain of mental health that can be reviewed. Additional records identified through cross-referencing were used to find other studies. RESULTS: This literature search identified 5442 articles that were eligible for our study after the removal of duplicates. Following abstract screening, 114 full articles were assessed in their entirety, of which 107 were excluded. The remaining 7 studies were analyzed. Classification algorithms such as naive Bayes, decision tree, and support vector machine classifiers were identified. Support vector machine is the most used algorithm and best performing as per the identified articles. Prediction classification scores for the identified algorithms ranged from 53%-91% for the classification of textual entities in 4-7 categories. In addition, 3 of the 7 studies reported an interjudge agreement statistic; these were consistent with agreement statistics for text classification done by human evaluators. CONCLUSIONS: A systematic review of available machine learning algorithms for automated text classification for small data sets in several fields (psychiatry, psychology, and social sciences) was conducted. We compared automated classification with classification done by human evaluators. Our results show that it is possible to automatically classify textual entities of a transcript based solely on small databases. Future studies are nevertheless needed to assess whether such algorithms can be implemented in the context of psychotherapies.

Anorexia nervosa in adolescents: evolution of weight history and impact of excess premorbid weight.

The purpose of our study is to establish if the proportion of patients diagnosed with anorexia nervosa that have a history of excess weight has increased over a 10-year period and to study how different premorbid weight groups vary in terms of clinical characteristics. We performed a single-center, retrospective cohort study of all new patients presenting with anorexia nervosa, restrictive and binge/purge subtypes, in 2004 and 2014 at the Adolescent Medicine Clinic of Sainte-Justine University Health Centre (n = 172). The prevalence of excess premorbid weight was similar in both cohorts (32% in 2004 versus 29.5% in 2014). The historically overweight subgroup had a lower heart rate at intake (64.77 versus 69.75, p = 0.03). Patients with excess premorbid weight lost an average of 1 kg more per month than their historically thinner counterparts (2.6 kg versus 1.6 kg/month, p = 0.0011). The total decrease in BMI was much greater in patients with a history of excess weight (7 BMI points versus 3.8, p = 0.0001).Conclusion: Since overweight and obese patients present with significant weight suppression values, our study stresses the importance of screening for AN in all patients rather than in only the noticeably underweight. What is Known: • More than one third of patients presenting with AN have a history of overweight or obesity, which is comparable to the general population. • A delay between AN onset and diagnosis has been described in overweight adolescents. What is New: • Historically overweight patients presenting with AN demonstrate increased speed of weight loss, greater drop in BMI, and lower heart rate at presentation. • For patients with a history of excess weight considered as having recovered from AN, the average BMI at discharge was within normal limits.