scholarly journals Machine Learning based Detection of Depression and Anxiety

2021 ◽  
Vol 183 (45) ◽  
pp. 20-23
Author(s):  
Guna Sekhar Sajja
Keyword(s):  
Machine Learning ◽  
Depression And Anxiety ◽  
Detection Of Depression

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.

Predicting Therapy Outcome in a Digital Mental Health Intervention for Depression and Anxiety: A Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Silvan Hornstein ◽  
Valerie Forman-Hoffman ◽  
Albert Nazander ◽  
Kristian Ranta ◽  
Kevin Hilbert
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Work Productivity ◽  
Mental Healthcare ◽  
Predictive Performance ◽  
Additional Treatment ◽  
Biological Data ◽  
Depression And Anxiety ◽  
Activity Impairment ◽  
Large Patient

BACKGROUND Predicting the outcomes of individual patients for treatment interventions appears central for making mental healthcare more tailored and effective. Machine Learning (ML) has been proven to be able to make such predictions with notable accuracy. However, little work has been done to investigate the performance of such ML-based predictions within digital mental health (DMH) interventions. Implementing ML approaches in such a context would be quite easy as data is readily available for large patient populations. OBJECTIVE This study evaluates the performance of ML in predicting treatment outcomes in a DMH intervention designed for treating depression and anxiety. METHODS Several algorithms were trained based on the data of 970 patients to predict significant reduction in depression and anxiety symptoms, by using clinical and sociodemographic variables. As a Random Forest Classifier (RF) performed best over cross-validation, it was used to predict the outcomes of 279 new patients. RESULTS The RF achieved an accuracy of 0.71 for the testset (base-rate: 0.67, AUC: 0.60, P = .001, balanced accuracy: 0.60). Additionally, predicted non-responders showed less average reduction of their PHQ-9 (-2.7 , P = .004) and GAD-7 values (-3.7, P < .001) compared to responders. Besides pre-treatment PHQ and GAD values, the self-reported motivation, type of referral into the program (self versus healthcare provider) as well as Work Productivity and Activity Impairment Questionnaire (WPAI) items contributed most to the predictions. CONCLUSIONS This study highlights that, also within DMH, social-demographic and clinical variables can be used for ML to predict therapy outcomes. Despite the overall moderate performance, this appears promising as these predictions can potentially improve the outcomes of nonresponders by monitoring their progress or by offering alternative or additional treatment. Behavioural patterns measured by smartphone-based interventions, such as app-usage, as well as biological data from wearable devices in DMH interventions are highlighted as paths towards improved predictive performance.

Optimizing existing mental health screening methods in a dementia screening and risk-factor app: An Observational, machine learning study (Preprint)

2021 ◽  
Author(s):  
Narayan Kuleindiren ◽  
Raphael Paul Rifkin-Zybutz ◽  
Monika Johal ◽  
Hamza Selim ◽  
Itai Palmon ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Regression Models ◽  
Superior Performance ◽  
Training Dataset ◽  
Screening Methods ◽  
Depression And Anxiety ◽  
Dementia Screening ◽  
Fold Cross Validation

BACKGROUND Mindset4Dementia is an app that aims to improve dementia screening by assessing cognition and risk factors. It considers important clinical risk factors, including prodromal symptoms, mental health disorders, and differential diagnoses of dementia. The PHQ-9 and GAD-7 are widely validated, and commonly used scales used in screening for depression and anxiety disorders respectively. Shortened versions of both (PHQ-2/GAD-2) have been produced. OBJECTIVE We sought to develop a method that maintained the brevity of these shorter questionnaires while maintaining the better precision of the original questionnaires METHODS Single questions were designed to encompass symptoms covered in the original questionnaires. Answers to these questions were combined with the PHQ-2/GAD-2 and anonymized risk factors collected by Mindset4Dementia. Machine learning models were trained to use these single questions in combination with data already collected by the app - age, response to a joke and reporting of functional impairment to predict binary and continuous outcomes as measured by the PHQ-9/GAD-7. Our model was developed with a training dataset using ten-fold cross-validation and a hold-out testing datasets and compared to results from using the shorter questionnaires (PHQ-2/GAD-2) alone to benchmark performance. RESULTS We were able to achieve superior performance in predicting PHQ-9/GAD-7 screening cut-offs than the PHQ-2 (difference In AUC 0.04, 95% CI 0.00 – 0.08, P = 0.02) but not to GAD-2 (difference in AUC 0.00, 95% CI -0.02 – 0.03, P = 0.42). In regression models we were able to accurately predict total questionnaire scores; PHQ-9 (R2 = 0.655, MAE = 2.267), GAD-7 (R2 = 0.837, MAE = 1.780). CONCLUSIONS We have developed a short screening tool for affective disorders with superior or equivalent performance to well established methods.

scholarly journals Correlation between Depression and Anxiety in Patients with Chronic Non-Malignant Pain

2021 ◽  
Vol 5 (1) ◽  
pp. 83-94
Author(s):  
Irena Kovačević ◽  
Sanja Ledinski Fičko ◽  
Boris Ilić ◽  
Adriano Friganović ◽  
Štefanija Ozimec Vulinec ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Care ◽  
Chronic Pain ◽  
Primary Health Care ◽  
Cultural Differences ◽  
Ethnic Groups ◽  
Depression And Anxiety ◽  
Primary Health ◽  
Primary Care Patients ◽  
Detection Of Depression

Introduction. Two-thirds of primary care patients with depression also have somatic symptoms present, making detection of depression more difficult. Primary health care is the first level of screening for depression, and early detection is key to treatment success. Anxiety also has a high comorbidity rate with chronic pain conditions. Generalized anxiety disorder (GAD) is common among patients with “medically unexplained” chronic pain and chronic physical illness and is also a predictor of chronic musculoskeletal pain after trauma. Belonging to different ethnic groups and ignorance of these differences by primary care physicians can be an obstacle to good health care, especially early recognition of depressive symptoms. Aim. The aim of this proposed, systematic work was to draw conclusions from empirical research dealing with the processes involved in the examination of depression, anxiety, and chronic non malignant pain. The research question for this review paper was to examine the correlation of depression and anxiety with chronic non-malignant pain. The aim was to examine the role of primary health care in recognizing, preventing, and treating depression and anxiety in patients with chronic non-malignant pain, and whether there is a difference in the correlation between depression, anxiety, and chronic non-malignant pain according to ethnicity. Methods. Methods for identifying the study were derived from the Medline database (via PubMed). The analysis included all scientific papers in English, regardless of methodology, published since 2011. The papers dealt with the correlation between depression, anxiety, and chronic non-malignant pain, and included the population of primary care patients over 18 years of age who suffer from chronic nonmalignant pain and at the same time have symptoms of depression and anxiety present or are members of ethnic groups. 403 articles were found, original and review papers, of which, after a detailed reading, 10 were selected that meet the inclusion criteria for the purposes of this review. Results. Depression and anxiety are significantly more present in people with chronic pain (23%), compared to those who do not have chronic pain (12%). The most common is chronic musculoskeletal pain, with one-third of patients having depression. Depression and anxiety are significantly associated with the intensity and duration of pain. Chronic pain and depression also differ according to ethnic groups, with cultural differences and language barriers being a barrier to early detection of depression. Conclusion. Depression is the most common mental health disorder associated with chronic pain. It is extremely important to treat both depression and pain, in order to prevent the development of severe depression and chronic pain at an early stage. The integrated program at the level of primary health care is expected to have positive effects on both the physical and mental condition of patients. Cultural differences and ethnicity, which can significantly reduce the detection of depressive symptoms at the primary health care level, should certainly be taken into account.

scholarly journals Mental Health Prediction Models Using Machine Learning in Higher Education Institution

2021 ◽  
Vol 12 (5) ◽  
pp. 1782-1792
Author(s):  
Sofianita Mutalib, Et. al.
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Higher Education ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Mental Health Problems ◽  
Support Vector ◽  
Depression And Anxiety ◽  
Education Students

Today, mental health problem has become a grave concern in Malaysia. According to the National Health and Morbidity Survey (NHMS) 2017, one in five people in Malaysia suffers from depression, two in five from anxiety, and one in ten from stress. Higher education students are also at risk of being part of the affected community. The increased data size without proper management and analysis, and the lack of counsellors, are compounding the issue. Therefore, this paper presents on identifying factors in mental health problems among selected higher education students. This study aims to classify students into different categories of mental health problems, which are stress, depression, and anxiety, using machine learning algorithms. The data is collected from students in a higher education institute in Kuala Terengganu. The algorithms applied are Decision Tree, Neural Network, Support Vector Machine, Naïve Bayes, and logistic regression. The most accurate model for stress, depression, and anxiety is Decision Tree, Support Vector Machine, and Neural Network, respectively.

Sign in / Sign up

Export Citation Format

Share Document