Toward explainable AI (XAI) for mental health detection based on language behavior

Advances in artificial intelligence (AI) in general and Natural Language Processing (NLP) in particular are paving the new way forward for the automated detection and prediction of mental health disorders among the population. Recent research in this area has prioritized predictive accuracy over model interpretability by relying on deep learning methods. However, prioritizing predictive accuracy over model interpretability can result in a lack of transparency in the decision-making process, which is critical in sensitive applications such as healthcare. There is thus a growing need for explainable AI (XAI) approaches to psychiatric diagnosis and prediction. The main aim of this work is to address a gap by conducting a systematic investigation of XAI approaches in the realm of automatic detection of mental disorders from language behavior leveraging textual data from social media. In pursuit of this aim, we perform extensive experiments to evaluate the balance between accuracy and interpretability across predictive mental health models. More specifically, we build BiLSTM models trained on a comprehensive set of human-interpretable features, encompassing syntactic complexity, lexical sophistication, readability, cohesion, stylistics, as well as topics and sentiment/emotions derived from lexicon-based dictionaries to capture multiple dimensions of language production. We conduct extensive feature ablation experiments to determine the most informative feature groups associated with specific mental health conditions. We juxtapose the performance of these models against a “black-box” domain-specific pretrained transformer adapted for mental health applications. To enhance the interpretability of the transformers models, we utilize a multi-task fusion learning framework infusing information from two relevant domains (emotion and personality traits). Moreover, we employ two distinct explanation techniques: the local interpretable model-agnostic explanations (LIME) method and a model-specific self-explaining method (AGRAD). These methods allow us to discern the specific categories of words that the information-infused models rely on when generating predictions. Our proposed approaches are evaluated on two public English benchmark datasets, subsuming five mental health conditions (attention-deficit/hyperactivity disorder, anxiety, bipolar disorder, depression and psychological stress)

Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features

Research at the intersection of personality psychology, computer science, and linguistics has recently focused increasingly on modeling and predicting personality from language use. We report two major improvements in predicting personality traits from text data: (1) to our knowledge, the most comprehensive set of theory-based psycholinguistic features and (2) hybrid models that integrate a pre-trained Transformer Language Model BERT and Bidirectional Long Short-Term Memory (BLSTM) networks trained on within-text distributions ('text contours') of psycholinguistic features. We experiment with BLSTM models (with and without Attention) and with two techniques for applying pre-trained language representations from the transformer model - 'feature-based' and 'fine-tuning'. We evaluate the performance of the models we built on two benchmark datasets that target the two dominant theoretical models of personality: the Big Five Essay dataset and the MBTI Kaggle dataset. Our results are encouraging as our models outperform existing work on the same datasets. More specifically, our models achieve improvement in classification accuracy by 2.9% on the Essay dataset and 8.28% on the Kaggle MBTI dataset. In addition, we perform ablation experiments to quantify the impact of different categories of psycholinguistic features in the respective personality prediction models.Comment: accepted at WASSA 202

SIMULTANEOUS ESTIMATION OF MOMETASONE FUROATE AND FORMOTEROL FUMARATE BY HPLC METHOD IN ROTACAPS

Objective: To develop and validate a simple and sensitive RP-HPLC method for the simultaneous determination of mometasone furoate (MOM) and formoterol fumarate (FOR) in pharmaceutical dosage forms. Methods: In RP-HPLC method, chromatographic separation was achieved using a mixture of a solvent system consisting of methanol–water (pH 3.5) in the ratio of 85:15 % v/v at a flow rate of 1 ml/min and detection was carried out at 225 nm. Results: The run time for the simultaneous estimation of drugs for the proposed method was 10 min as drugs eluted at 5.217 min (MOM) and 8.650 min (FOR). The linearity was found in the range of 33.33-299.97 μg/ml and 1-9 μg/ml for MOM and FOR, respectively. The values of limit of detection and limit of quantification were 3.634, 0.266 µg/ml and 11.014, 0.807 µg/ml, which indicates the sensitivity of the method for the estimation of MOM and FOR, respectively. The results of recovery studies for both the drugs were within the range i.e. 98.87-101.48 % which indicates the accuracy of the method. Relative standard deviation obtained from repeatability and reproducibility studies were less than 2% indicates the precision of the method. The proposed method was validated according to ICH guidelines. Conclusion: The proposed RP-HPLC method was found to be sensitive and precise because of the low LOD, LOQ and % RSD values (<2). The proposed work does not require acetonitrile and ion pairing reagent as compared to the reported methods. Therefore, method can be used preferably for routine analysis due to its simplicity and economic advantages

A prognostic index predicting survival in transformed Waldenström macroglobulinemia

Histological transformation into diffuse large B-cell lymphoma is a rare complication in patients with Waldenström macroglobulinemia (WM) usually associated with a poor prognosis. The objective of this study was to develop and validate a prognostic index for survival in transformed WM patients. Through this multicenter, international collaborative effort, we developed a scoring system based on data from 133 patients with transformed WM who were evaluated between 1995 and 2016 (training cohort). Univariate and multivariate analyses were used to propose a prognostic index with 2-year survival after transformation as an end-point. For external validation, a data set of 67 patients was used to evaluate the performance of the model (validation cohort). By multivariate analysis, three adverse covariates were identified as independent predictors of 2-year survival after transformation: elevated serum LDH (2 points), platelet count < 100 x 109/L (1 point) and any previous treatment for WM (1 point). Three risk groups were defined: low-risk (0-1 point, 24% of patients), intermediate-risk (2-3 points, 59%, hazard ratio (HR) = 3.4) and high-risk (4 points, 17%, HR = 7.5). Two-year survival rates were 81%, 47%, and 21%, respectively (P < 0.0001). This model appeared to be a better discriminant than the International Prognostic Index (IPI) and the revised IPI (R-IPI). We validated this model in an independent cohort. This easy-to-compute scoring index is a robust tool that may allow identification of groups of transformed WM patients with different outcomes and could be used for improving the development of risk-adapted treatment strategies

Symptomatic improvement with gluten restriction in irritable bowel syndrome: a prospective, randomized, double blinded placebo controlled trial

Background/AimsThe existence of non-celiac gluten sensitivity has been debated. Indeed, the intestinal and extra-intestinal symptoms of many patients with irritable bowel syndrome (IBS) but without celiac disease or wheat allergy have been shown to improve on a gluten-free diet. Therefore, this study set out to evaluate the effects of gluten on IBS symptoms.MethodsWe performed a double-blind randomized placebo-controlled rechallenge trial in a tertiary care hospital with IBS patients who fulfilled the Rome III criteria. Patients with celiac disease and wheat allergy were appropriately excluded. The participants were administered a gluten-free diet for 4 weeks and were asked to complete a symptom-based questionnaire to assess their overall symptoms, abdominal pain, bloating, wind, and tiredness on the visual analog scale (0-100) at the baseline and every week thereafter. The participants who showed improvement were randomly assigned to one of two groups to receive either a placebo (gluten-free breads) or gluten (whole cereal breads) as a rechallenge for the next 4 weeks.ResultsIn line with the protocol analysis, 60 patients completed the study. The overall symptom score on the visual analog scale was significantly different between the two groups (P<0.05). Moreover, the patients in the gluten intervention group scored significantly higher in terms of abdominal pain, bloating, and tiredness (P<0.05), and their symptoms worsened within 1 week of the rechallenge.ConclusionsA gluten diet may worsen the symptoms of IBS patients. Therefore, some form of gluten sensitivity other than celiac disease exists in some of them, and patients with IBS may benefit from gluten restrictions

Viral MicroRNA Effects on Pathogenesis of Polyomavirus SV40 Infections in Syrian Golden Hamsters

Shaojie Zhang, Vojtech Sroller, Preeti Zanwar, Steven J. Halvorson, Nadim J. Ajami, Corey W. Hecksel, Jody L. Swain, Connie Wong, Janet S. Butel, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, United States of AmericaChun Jung Chen, Christopher S. Sullivan, Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, United States of AmericaJody L. Swain, Center for Comparative Medicine, Baylor College of Medicine, Houston, Texas, United States of AmericaEffects of polyomavirus SV40 microRNA on pathogenesis of viral infections in vivo are not known. Syrian golden hamsters are the small animal model for studies of SV40. We report here effects of SV40 microRNA and influence of the structure of the regulatory region on dynamics of SV40 DNA levels in vivo. Outbred young adult hamsters were inoculated by the intracardiac route with 1×107 plaque-forming units of four different variants of SV40. Infected animals were sacrificed from 3 to 270 days postinfection and viral DNA loads in different tissues determined by quantitative real-time polymerase chain reaction assays. All SV40 strains displayed frequent establishment of persistent infections and slow viral clearance. SV40 had a broad tissue tropism, with infected tissues including liver, kidney, spleen, lung, and brain. Liver and kidney contained higher viral DNA loads than other tissues; kidneys were the preferred site for long-term persistent infection although detectable virus was also retained in livers. Expression of SV40 microRNA was demonstrated in wild-type SV40-infected tissues. MicroRNA-negative mutant viruses consistently produced higher viral DNA loads than wild-type SV40 in both liver and kidney. Viruses with complex regulatory regions displayed modestly higher viral DNA loads in the kidney than those with simple regulatory regions. Early viral transcripts were detected at higher levels than late transcripts in liver and kidney. Infectious virus was detected infrequently. There was limited evidence of increased clearance of microRNA-deficient viruses. Wild-type and microRNA-negative mutants of SV40 showed similar rates of transformation of mouse cells in vitro and tumor induction in weanling hamsters in vivo. This report identified broad tissue tropism for SV40 in vivo in hamsters and provides the first evidence of expression and function of SV40 microRNA in vivo. Viral microRNA dampened viral DNA levels in tissues infected by SV40 strains with simple or complex regulatory regions.This work was supported in part by research grants R01 CA134524 (JSB) and R01 AI077746 (CSS) from the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Molecular BiosciencesEmail: [email protected]