No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Temporal Facial Features for Depression Screening
... In the development of depression detection automation, there is a technology called OpenFace. OpenFace is a commonly used software to assist in the analysis of MDD based on facial features [18]. Features in the facial area will be extracted using OpenFace [19]. ...
... Flores et al. detected depression based on facial landmarks, eye gaze, and FAU. The results of this study, the eye gaze feature is the best feature for depression analysis with an F1-Score of 0.81 [18]. Another study by Flores et al. that used the LSTM model also argued the same thing and emphasized that eye gaze proved to be the most valuable temporal facial feature, both in unimodal and multimodal models [32]. ...
... Similarly, Muzammel et al. also focused on the eye and lip areas of FAU [12]. Therefore, Flores et al. the result has a high F1-Score because the eye is the most correlated feature in detecting depression [18]. ...
Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. Therefore, researchers have turned to deep learning models to explore the detection of depression. The objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. This Systematic Literature Review found that facial expressions do have relevant patterns related to MDD.
Physiological studies have confirmed that there are differences in facial activities between depressed and healthy individuals. Therefore, while protecting the privacy of subjects, substantial efforts are made to predict the depression severity of individuals by analyzing Facial Keypoints Representation Sequences (FKRS) and Action Units Representation Sequences (AURS). However, those works has struggled to examine the spatial distribution and temporal changes of Facial Keypoints (FKs) and Action Units (AUs) simultaneously, which is limited in extracting the facial dynamics characterizing depressive cues. Besides, those works don’t realize the complementarity of effective information extracted from FKRS and AURS, which reduces the prediction accuracy. To this end, we intend to use the recently proposed Multi-Layer Perceptrons with gating (gMLP) architecture to process FKRS and AURS for predicting depression levels. However, the channel projection in the gMLP disrupts the spatial distribution of FKs and AUs, leading to input and output sequences not having the same spatiotemporal attributes. This discrepancy hinders the additivity of residual connections in a physical sense. Therefore, we construct a novel MLP architecture named DepressionMLP. In this model, we propose the Dual Gating (DG) and Mutual Guidance (MG) modules. The DG module embeds cross-location and cross-frame gating results into the input sequence to maintain the physical properties of data to make up for the shortcomings of gMLP. The MG module takes the global information of FKRS (AURS) as a guidance mask to filter the AURS (FKRS) to achieve the interaction between FKRS and AURS. Experimental results on several benchmark datasets show the effectiveness of our method.
Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are both heterogeneous in their clinical presentations, manifesting with unique symptom profiles. Despite this, prior digital phenotype research has primarily focused on disorder-level detection rather than symptom-level detection. In this research, we predict the existence of individual symptoms of MDD and GAD with SMS log metadata, and ensemble these symptom-level classifiers to screen for depression and anxiety, thus accounting for disorder heterogeneity. Further, we collect an additional dataset of retrospectively harvested SMS logs to augment an existing dataset collected after COVID-19 altered communication patterns, and propose two new types of distribution features: consecutive messages and conversation ratio. Our symptom-level detectors achieved a balanced accuracy of 0.7 in 13 of the 16 MDD and GAD symptoms, with reply latency distribution features achieving a balanced accuracy of 0.78 when detecting anxiety symptom trouble relaxing. When combined into disorder-level ensembles, these symptom-level detectors achieved a balanced accuracy of 0.76 for depression screening and 0.73 for anxiety screening, with tree boosting methods demonstrating particular efficacy. Accounting for disorder heterogeneity, our research provides insight into the value of SMS logs for the assessment of depression and anxiety diagnostic criteria.
Early detection of depression has always been a challenge. Currently, research on automatic depression detection mainly focuses on using low-level features such as audio, text, or video from interview dialogue as input data, ignoring some high-level features contained in the dialogue. We proposes a multimodal depression detection method for extracting emotional and behavioral features from dialogue and detecting early depression. Specifically, we design an emotional feature extraction module and a behavioral feature extraction module, which input the extracted emotional and behavioral features as high-level features into the depression detection network. In this process, a weighted attention fusion module is used to guide the learning of text and audio modalities and predict the final result. Experimental results on the public dataset DAIC-WOZ show that the extracted emotional and behavioral features effectively complement the high-level semantics missing in the network. Our proposed method improves the F1-score by 6% compared to traditional approaches. The experimental data also indicate the importance of the model’s detection results in early depression detection. This technology has certain application value in professional fields such as caregiving, emotional interaction, psychological diagnosis, and treatment.
Depression is one of the most common mental illnesses, affecting people’s quality of life and posing a risk to their health. Low-cost and objective automatic depression detection (ADD) is becoming increasingly critical. However, existing ADD methods usually treat depression detection as a regression problem for predicting patient health questionnaire-8 (PHQ-8) scores, ignoring the scores’ ambiguity caused by multiple questionnaire issues. To effectively leverage the score labels, we propose an uncertainty-aware label contrastive and distribution learning (ULCDL) method to estimate PHQ-8 scores, thus detecting depression automatically. ULCDL first simulates the ambiguity within PHQ-8 scores by converting single-valued scores into discrete label distributions. Afterward, it learns to predict the PHQ-8 score distribution by minimizing the Kullback–Leibler (KL) divergence between the score distribution and the discrete label distribution. Finally, the predicted PHQ-8 score distribution outperforms the PHQ-8 score in ADD. Moreover, label-based contrastive learning (LBCL) is introduced to facilitate the model for learning common features related to depression in multimodal data. A multibranch fusion module is proposed to align and fuse multimodal data for better exploring the uncertainty of PHQ-8 labels. The proposed method is evaluated on the publicly available DAIC-WOZ dataset. Experiment results show that ULCDL outperforms regression-based depression detection methods and achieves state-of-the-art performance. The code will be released after acceptance.
Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed pre-training scheme, leading to substantial improvments in classification accuracy using Transformer, one-dimensional convolutional neural network (CNN-1D), and Bidirectional Long Short-Term Memory (Bi-LSTM) network. The code and the pre-trained model will be available at https://github.com/linlei1214/SITS-BERT upon publication.
Recently, new emphasis was put on reducing waiting times in mental health services as there is an ongoing concern that longer waiting time for treatment leads to poorer health outcomes. However, little is known about delays within the mental health service system and its impact on patients. We explore the impact of waiting times on patient outcomes in the context of Early Intervention in Psychosis (EIP) services in England from April 2012 to March 2015. We use the Mental Health Services Data Set and the routine outcome measure the Health of the Nation Outcome Scale. In a generalised linear regression model, we control for baseline outcomes, previous service use and treatment intensity to account for possible endogeneity in waiting time. We find that longer waiting time is significantly associated with a deterioration in patient outcomes 12 months after acceptance for treatment for patients that are still in EIP care. Effects are strongest for waiting times longer than 3 months and effect sizes are small to moderate. Patients with shorter treatment periods are not affected. The results suggest that policies should aim to reduce excessively long waits in order to improve outcomes for patients waiting for treatment for psychosis.
A key enabler for optimizing business processes is accurately estimating the probability distribution of a time series future given its past. Such probabilistic forecasts are crucial for example for reducing excess inventory in supply chains. In this paper we propose DeepAR, a novel methodology for producing accurate probabilistic forecasts, based on training an auto-regressive recurrent network model on a large number of related time series. We show through extensive empirical evaluation on several real-world forecasting data sets that our methodology is more accurate than state-of-the-art models, while requiring minimal feature engineering.
The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions. The goal of the Challenge is to provide a common benchmark test set for multi-modal information processing and to bring together the depression and emotion recognition communities, as well as the audio, video and physiological processing communities, to compare the relative merits of the various approaches to depression and emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.
In order to improve the recognition accuracy of the Depression Classification Sub-Challenge (DCC) of the AVEC 2016, in this paper we propose a decision tree for depression classification. The decision tree is constructed according to the distribution of the multimodal prediction of PHQ-8 scores and participants' characteristics (PTSD/Depression Diagnostic , sleep-status, feeling and personality) obtained via the analysis of the transcript files of the participants. The proposed gender specific decision tree provides a way of fusing the upper level language information with the results obtained using low level audio and visual features. Experiments are carried out on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) database, results show that the proposed depression classification schemes obtain very promising results on the development set, with F1 score reaching 0.857 for class depressed and 0.964 for class not depressed. Despite of the over-fitting problem in training the models of predicting the PHQ-8 scores, the classification schemes still obtain satisfying performance on the test set. The F1 score reaches 0.571 for class depressed and 0.877 for class not depressed, with the average 0.724 which is higher than the baseline result 0.700.
We present SimSensei Kiosk, an implemented virtual human interviewer designed to create an engaging face-to-face interaction where the user feels comfortable talking and sharing information. SimSensei Kiosk is also designed to create interactional situations favorable to the automatic assessment of distress indicators, defined as verbal and nonverbal behaviors correlated with depression, anxiety or post-traumatic stress disorder (PTSD). In this paper, we summarize the design methodology, performed over the past two years, which is based on three main development cycles: (1) analysis of face-to-face human interactions to identify potential distress indicators, dialogue policies and virtual human gestures, (2) development and analysis of a Wizard-of-Oz prototype system where two human operators were deciding the spoken and gestural responses, and (3) development of a fully automatic virtual interviewer able to engage users in 15-25 minute interactions. We show the potential of our fully automatic virtual human interviewer in a user study, and situate its performance in relation to the Wizard-of-Oz prototype. Copyright © 2014, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Depression is a common and disabling mental health disorder, which impacts not only on the sufferer but also their families, friends and the economy overall. Our ultimate aim is to develop an automatic objective affective sensing system that supports clinicians in their diagnosis and monitoring of clinical depression. Here, we analyse the performance of head pose and movement features extracted from face videos using a 3D face model projected on a 2D Active Appearance Model (AAM). In a binary classification task (depressed vs. non-depressed), we modelled low-level and statistical functional features for an SVM classifier using real-world clinically validated data. Although the head pose and movement would be used as a complementary cue in detecting depression in practice, their recognition rate was impressive on its own, giving 71.2% on average, which illustrates that head pose and movement hold effective cues in diagnosing depression. When expressing positive and negative emotions, recognising depression using positive emotions was more accurate than using negative emotions. We conclude that positive emotions are expressed less in depressed subjects at all times, and that negative emotions have less discriminatory power than positive emotions in detecting depression. Analysing the functional features statistically illustrates several behaviour patterns for depressed subjects: (1) slower head movements, (2) less change of head position, (3) longer duration of looking to the right, (4) longer duration of looking down, which may indicate fatigue and eye contact avoidance. We conclude that head movements are significantly different between depressed patients and healthy subjects, and could be used as a complementary cue.
Depression is a common and disabling mental health disorder, which impacts not only on the sufferer but also on their families, friends and the economy overall. Despite its high prevalence, current diagnosis relies almost exclusively on patient self-report and clinical opinion, leading to a number of subjective biases. Our aim is to develop an objective affective sensing system that supports clinicians in their diagnosis and monitoring of clinical depression. In this paper, we analyse the performance of eye movement features extracted from face videos using Active Appearance Models for a binary classification task (depressed vs. non-depressed). We find that eye movement low-level features gave 70% accuracy using a hybrid classifier of Gaussian Mixture Models and Support Vector Machines, and 75% accuracy when using statistical measures with SVM classifiers over the entire interview. We also investigate differences while expressing positive and negative emotions, as well as the classification performance in gender-dependent versus gender-independent modes. Interestingly, even though the blinking rate was not significantly different between depressed and healthy controls, we find that the average distance between the eyelids ('eye opening') was significantly smaller and the average duration of blinks significantly longer in depressed subjects, which might be an indication of fatigue or eye contact avoidance.
Standard recurrent nets cannot deal with long minimal time lags between relevant signals. Several recent NIPS papers propose alternative methods. We first show: problems used to promote various previous algorithms can be solved more quickly by random weight guessing than by the proposed algorithms. We then use LSTM, our own recent algorithm, to solve a hard problem that can neither be quickly solved by random search nor by any other recurrent net algorithm we are aware of.
Depression is a common mental health disorder with large social and economic consequences. It can be costly and difficult to detect, traditionally requiring hours of assessment by a trained clinical. Recently, machine learning models have been trained to screen for depression with patient voice recordings collected during an interview with a virtual agent. To engage the patient in a conversation and increase the quantity of responses, the virtual interviewer asks a series of follow-up questions. However, asking fewer questions would reduce the time burden of screening for the participant. We, therefore, assess if these follow-up questions have a tangible impact on the performance of deep learning models for depression classification. Specifically, we study the effect of including the vocal and transcribed replies to one, two, three, four, five, or all follow-up questions in the depression screening models. We notably achieve this using unimodal and multimodal pre-trained transfer learning models. Our findings reveal that follow-up questions can help increase F1 scores for the majority of the interview questions. This research can be leveraged for the design of future mental illness screening applications by providing important information about both question selection and the best number of follow-up questions.
The rates of mental illness, especially anxiety and depression, have increased greatly since the start of the COVID-19 pandemic. Traditional mental illness screening instruments are too cumbersome and biased to screen an entire population. In contrast, smartphone call and text logs passively capture communication patterns and thus represent a promising screening alternative. To facilitate the advancement of such research, we collect and curate the DepreST Call and Text log (DepreST-CAT) dataset from over 365 crowdsourced participants during the COVID-19 pandemic. The logs are labeled with traditional anxiety and depression screening scores essential for training machine learning models. We construct time series ranging from 2 to 16 weeks in length from the retrospective smartphone logs. To demonstrate the screening capabilities of these time series, we then train a variety of unimodal and multimodal machine and deep learning models. These models provide insights into the relative screening value of the different types of logs, lengths of log time series, and classification methods. The DepreST-CAT dataset is a valuable resource for the research community to model communication patterns during the COVID-19 pandemic and further the development of machine learning algorithms for passive mental illness screening.
The growing prevalence of depression and suicidal ideation among college students further exacerbated by the Coronavirus pandemic is alarming, highlighting the need for universal mental illness screening technology. With traditional screening questionnaires too burdensome to achieve universal screening in this population, data collected through mobile applications has the potential to rapidly identify at-risk students. While prior research has mostly focused on collecting passive smartphone modalities from students, smartphone sensors are also capable of capturing active modalities. The general public has demonstrated more willingness to share active than passive modalities through an app, yet no such dataset of active mobile modalities for mental illness screening exists for students. Knowing which active modalities hold strong screening capabilities for student populations is critical for developing targeted mental illness screening technology. Thus, we deployed a mobile application to over 300 students during the COVID-19 pandemic to collect the Student Suicidal Ideation and Depression Detection (StudentSADD) dataset. We report on a rich variety of machine learning models including cutting-edge multimodal pretrained deep learning classifiers on active text and voice replies to screen for depression and suicidal ideation. This unique StudentSADD dataset is a valuable resource for the community for developing mobile mental illness screening tools.
Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification without human-designed features. In our model, we apply a recurrent structure to capture contextual information as far as possible when learning word representations, which may introduce considerably less noise compared to traditional window-based neural networks. We also employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the key components in texts. We conduct experiments on four commonly used datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets, particularly on document-level datasets.
The prevalence of suicide has been on the rise since the 20th century, causing severe emotional damage to individuals, families, and communities alike. Despite the severity of this suicide epidemic, there is so far no reliable and systematic way to assess suicide intent of a given individual. Through efforts to automate and systematize diagnosis of mental illnesses over the past few years, verbal and acoustic behaviors have received increasing attention as biomarkers, but little has been done to study eyelids, gaze, and head pose in evaluating suicide risk. This study explores statistical analysis, feature selection, and machine learning classification as means of suicide risk evaluation and nonverbal behavioral interpretation. Applying these methods to the eye and head signals extracted from our unique dataset, this study finds that high-risk suicidal individuals experience psycho-motor retardation and symptoms of anxiety and depression, characterized by eye contact avoidance, slower blinks and a downward eye gaze. By comparing results from different methods of classification, we determined that these features are highly capable of automatically classifying different levels of suicide risk consistently and with high accuracy, above 98%. Our conclusion corroborates psychological studies, and shows great potential of a systematic approach in suicide risk evaluation that is adoptable by both healthcare providers and naïve observers.KeywordsAffective computingSuicide riskNonverbal behaviourExplainable AI
Depression is a common, but serious mental disorder that affects people all over the world. Besides providing an easier way of diagnosing the disorder, a computer-aided automatic depression assessment system is demanded in order to reduce subjective bias in the diagnosis. We propose a multimodal fusion of speech and linguistic representation for depression detection. We train our model to infer the Patient Health Questionnaire (PHQ) score of subjects from AVEC 2019 DDS Challenge database, the E-DAIC corpus. For the speech modality, we use deep spectrum features extracted from a pretrained VGG-16 network and employ a Gated Convolutional Neural Network (GCNN) followed by a LSTM layer. For the textual embeddings, we extract BERT textual features and employ a Convolutional Neural Network (CNN) followed by a LSTM layer. We achieved a CCC score equivalent to 0.497 and 0.608 on the E-DAIC corpus development set using the unimodal speech and linguistic models respectively. We further combine the two modalities using a feature fusion approach in which we apply the last representation of each single modality model to a fully-connected layer in order to estimate the PHQ score. With this multimodal approach, it was possible to achieve the CCC score of 0.696 on the development set and 0.403 on the testing set of the E-DAIC corpus, which shows an absolute improvement of 0.283 points from the challenge baseline.
Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice because of its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to the critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined. Expected final online publication date for the Annual Review of Clinical Psychology Volume 14 is May 7, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
We present a novel convolutional neural network (CNN) design for facial landmark coordinate regression. We examine the intermediate features of a standard CNN trained for landmark detection and show that features extracted from later, more specialized layers capture rough landmark locations. This provides a natural means of applying differential treatment midway through the network, tweaking processing based on facial alignment. The resulting Tweaked CNN model (TCNN) harnesses the robustness of CNNs for landmark detection, in an appearance-sensitive manner without training multi-part or multi-scale models. Our results on standard face landmark detection and face verification benchmarks show TCNN to surpasses previously published performances by wide margins.
Depression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities (audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements (using Landmark Motion History Histograms and Landmark Motion Magnitude), global head motion, and eye blinks. These features were combined with statistically derived features from pre-extracted features (emotions, action units, gaze, and pose). Both speech rate and word-level semantic content were also evaluated. Classification results are reported using four different classification schemes: i) gender-based models for each individual modality, ii) the feature fusion model, ii) the decision fusionmodel, and iv) the posterior probability classification model. Proposed approaches outperforming the reference classification accuracy include the one utilizing statistical descriptors of low-level audio features. This approach achieved f1-scores of 0.59 for identifying depressed and 0.87 for identifying not-depressed individuals on the development set and 0.52/0.81, respectively for the test set.
Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDD's impact on these processes is reflected in an individual's communication via coupled mechanisms: vocal articulation, facial gesturing and choice of content to convey in a dialogue. In particular, MDD-induced neurophysiological changes are associated with a decline in dynamics and coordination of speech and facial motor control, while neurocognitive changes influence dialogue semantics. In this paper, biomarkers are derived from all of these modalities, drawing first from previously developed neurophysiologically-motivated speech and facial coordination and timing features. In addition, a novel indicator of lower vocal tract constriction in articulation is incorporated that relates to vocal projection. Semantic features are analyzed for subject/avatar dialogue content using a sparse coded lexical embedding space, and for contextual clues related to the subject's present or past depression status. The features and depression classification system were developed for the 6th International Audio/Video Emotion Challenge (AVEC), which provides data consisting of audio, video-based facial action units, and transcribed text of individuals communicating with the human-controlled avatar. A clinical Patient Health Questionnaire (PHQ) score and binary depression decision are provided for each participant. PHQ predictions were obtained by fusing outputs from a Gaussian staircase regressor for each feature set, with results on the development set of mean F1=0.81, RMSE=5.31, and MAE=3.34. These compare favorably to the challenge baseline development results of mean F1=0.73, RMSE=6.62, and MAE=5.52. On test set evaluation, our system obtained a mean F1=0.70, which is similar to the challenge baseline test result. Future work calls for consideration of joint feature analyses across modalities in an effort to detect neurological disorders based on the interplay of motor, linguistic, affective, and cognitive components of communication.
OBJECTIVE: While considerable attention has focused on improving the detection of depression, assessment of severity is also important in guiding treatment decisions. Therefore, we examined the validity of a brief, new measure of depression severity.
MEASUREMENTS: The Patient Health Questionnaire (PHQ) is a self-administered version of the PRIME-MD diagnostic instrument for common mental disorders. The PHQ-9 is the depression module, which scores each of the 9 DSM-IV criteria as “0” (not at all) to “3” (nearly every day). The PHQ-9 was completed by 6,000 patients in 8 primary care clinics and 7 obstetrics-gynecology clinics. Construct validity was assessed using the 20-item Short-Form General Health Survey, self-reported sick days and clinic visits, and symptom-related difficulty. Criterion validity was assessed against an independent structured mental health professional (MHP) interview in a sample of 580 patients.
RESULTS: As PHQ-9 depression severity increased, there was a substantial decrease in functional status on all 6 SF-20 subscales. Also, symptom-related difficulty, sick days, and health care utilization increased. Using the MHP reinterview as the criterion standard, a PHQ-9 score ≥10 had a sensitivity of 88% and a specificity of 88% for major depression. PHQ-9 scores of 5, 10, 15, and 20 represented mild, moderate, moderately severe, and severe depression, respectively. Results were similar in the primary care and obstetrics-gynecology samples.
CONCLUSION: In addition to making criteria-based diagnoses of depressive disorders, the PHQ-9 is also a reliable and valid measure of depression severity. These characteristics plus its brevity make the PHQ-9 a useful clinical and research tool.
This paper presents a new approach for learning in structured domains (SDs) using a constructive neural network for graphs (NN4G). The new model allows the extension of the input domain for supervised neural networks to a general class of graphs including both acyclic/cyclic, directed/undirected labeled graphs. In particular, the model can realize adaptive contextual transductions, learning the mapping from graphs for both classification and regression tasks. In contrast to previous neural networks for structures that had a recursive dynamics, NN4G is based on a constructive feedforward architecture with state variables that uses neurons with no feedback connections. The neurons are applied to the input graphs by a general traversal process that relaxes the constraints of previous approaches derived by the causality assumption over hierarchical input data. Moreover, the incremental approach eliminates the need to introduce cyclic dependencies in the definition of the system state variables. In the traversal process, the NN4G units exploit (local) contextual information of the graphs vertices. In spite of the simplicity of the approach, we show that, through the compositionality of the contextual information developed by the learning, the model can deal with contextual information that is incrementally extended according to the graphs topology. The effectiveness and the generality of the new approach are investigated by analyzing its theoretical properties and providing experimental results.
Hélio Clemente Cuve Santiago Castiello Brook Shiferaw Eri Ichijo Caroline Catmur and Geoffrey Bird. 2021. Alexithymia explains atypical spatiotemporal dynamics of eye gaze in autism
A survey of convolutional neural networks: analysis, applications, and prospects
- Zewen Li
- Fan Liu
- Wenjie Yang
- Shouheng Peng
- Li Zewen
A structured self-attentive sentence embedding
- Zhouhan Lin
- Minwei Feng
- Cicero Nogueira
- Mo Santos
- Bing Yu
- Bowen Xiang
- Yoshua Zhou
- Bengio
- Lin Zhouhan
World mental health report: transforming mental health for all . Geneva : World Health Organization
- World Health Organization
Voice2series: Reprogramming acoustic models for time series classification
- Chao- Han Huck Yang
- Yun-Yun Tsai
- Pin-Yu Chen
- Yang Han Huck