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Depressive Tendency Recognition Using the Gated Recurrent Unit From Speech and Text Features
... The metal samples produced by 4D printing exhibit greater mechanical qualities when compared with the majority of 4D printing materials, like hydrogels and polymers, demonstrating that this approach offers a wide variety of technical applications. In addition, laser stimulation's metallic 4D printing technology enables very flexible deformations of Carbon-based materials, such as carbon nanotubes (CNTs) or graphene 10 2 to 10 5 S/m Non-conductive polymers and composites 10 -12 to 10 2 S/m metallic 3D components [74,75]. As demonstrated in Fig. 9, the shape-morphing theory involves using a laser to serve as a stimulating source of heat to deliberately generate internal thermal stress in the specimen in order in order to accomplish 2D-to-3D structural transformation. ...
In recent years,4D printing domain is a growing part of additive manufacturing, received a great deal of interest because of its capacity to make products with the ability to alter form and demonstrate behavioural changes when reacts to stimuli from outside. 4D-printed constructions show smart behaviour with transformation as a function of time. In certain cases, the ideal arrangement of a 4D-printed structure cannot be obtained by merely subjecting the smart materials to an external stimulus. As a component of the interaction process, the stimulus being given must be administered in a particular order and over a sufficient quantity of time. Mathematics is required for 4D printing so as to create the material spread and framework required to accomplish the intended shape, property, or utility modification. Theoretical and numerical representations must be established in order to determine the interactions among four fundamental components: the structure of the material, desirable ultimate shape, material characteristics, and stimulus characteristics. This permits self-assembly, multifunction, and self-repair. This paper provides an in-depth examination of the 4D printing procedure, including a summary of the practical concepts, associated tools, processing parameters and future potential of the field.
Graphical abstract
... e Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or much more group investigations At present, scholars at home and abroad have explored the relevant theories and technologies of knowledge services in the cloud manufacturing environment. For example, author [6] proposed a personalized knowledge service technology based on user perception of the cloud manufacturing environment. e platform user task requirements and information actively push knowledge resources related to user manufacturing tasks. ...
To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users’ psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user’s multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper’s accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues.
Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss their problems. This paper addresses the early detection of depression using machine learning models based on messages on a social platform. In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata. An ensemble of both approaches is shown to achieve state-of-the-art results in a current early detection task. Furthermore, the currently popular ERDE score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated. A slightly modified metric is proposed and compared to the original score. Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.
This paper is the first review into the automatic analysis of speech for use as an objective predictor of depression and suicidality. Both conditions are major public health concerns; depression has long been recognised as a prominent cause of disability and burden worldwide, whilst suicide is a misunderstood and complex course of death that strongly impacts the quality of life and mental health of the families and communities left behind. Despite this prevalence the diagnosis of depression and assessment of suicide risk, due to their complex clinical characterisations, are difficult tasks, nominally achieved by the categorical assessment of a set of specific symptoms. However many of the key symptoms of either condition, such as altered mood and motivation, are not physical in nature; therefore assigning a categorical score to them introduces a range of subjective biases to the diagnostic procedure. Due to these difficulties, research into finding a set of biological, physiological and behavioural markers to aid clinical assessment is gaining in popularity. This review starts by building the case for speech to be considered a key objective marker for both conditions; reviewing current diagnostic and assessment methods for depression and suicidality including key non-speech biological, physiological and behavioural markers and highlighting the expected cognitive and physiological changes associated with both conditions which affect speech production. We then review the key characteristics; size associated clinical scores and collection paradigm, of active depressed and suicidal speech databases. The main focus of this paper is on how common paralinguistic speech characteristics are affected by depression and suicidality and the application of this information in classification and prediction systems. The paper concludes with an in-depth discussion on the key challenges – improving the generalisability through greater research collaboration and increased standardisation of data collection, and the mitigating unwanted sources of variability – that will shape the future research directions of this rapidly growing field of speech processing research.
A large number of people use online communities to discuss mental health issues, thus offering opportunities for new understanding of these communities. This paper aims to study the characteristics of online depression communities (CLINICAL) in comparison with those joining other online communities (CONTROL). We use machine learning and statistical methods to discriminate online messages between depression and control communities using mood, psycholinguistic processes and content topics extracted from the posts generated by members of these communities. All aspects including mood, the written content and writing style are found to be significantly different between two types of communities. Sentiment analysis shows the clinical group have lower valence than people in the control group. For language styles and topics, statistical tests reject the hypothesis of equality on psycholinguistic processes and topics between two groups. We show good predictive validity in depression classification using topics and psycholinguistic clues as features. Clear discrimination between writing styles and contents, with good predictive power is an important step in understanding social media and its use in mental health.
Mental illnesses such as depression and anxiety are highly prevalent, and therapy is increasingly being offered online. This new setting is a departure from face-toface therapy, and offers both a challenge and an opportunity ‐ it is not yet known what features or approaches are likely to lead to successful outcomes in such a different medium, but online text-based therapy provides large amounts of data for linguistic analysis. We present an initial investigation into the application of computational linguistic techniques, such as topic and sentiment modelling, to online therapy for depression and anxiety. We find that important measures such as symptom severity can be predicted with comparable accuracy to face-to-face data, using general features such as discussion topic and sentiment; however, measures of patient progress are captured only by finergrained lexical features, suggesting that aspects of style or dialogue structure may also be important.
We introduce the openSMILE feature extraction toolkit, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities. Audio low-level descriptors such as CHROMA and CENS features, loudness, Mel-frequency cepstral coefficients, perceptual linear predictive cepstral coefficients, linear predictive coefficients, line spectral frequencies, fundamental frequency, and formant frequencies are supported. Delta regression and various statistical functionals can be applied to the low-level descriptors. openSMILE is implemented in C++ with no third-party dependencies for the core functionality. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. It supports on-line incremental processing for all implemented features as well as off-line and batch processing. Numeric compatibility with future versions is ensured by means of unit tests. openSMILE can be downloaded from http://opensmile.sourceforge.net/.
Empirical studies on nonverbal communication in clinician—patient interaction are reviewed for both the psychotherapeutic and medical settings. Nonverbal behavior is considered both as the consequence of antecedent variables, such as individual or relationship characteristics, and as a predictor of clinical effectiveness and patient outcomes. The concept of rapport is considered in relation to nonverbal behavior, both theoretically and empirically. Also, the relevance of a patient's nonverbal behavior to the diagnostic goals of the clinical visit is demonstrated by research on nonverbal cues to psychopathology, Type A/B personality, and pain. Although the topic of nonverbal behavior and clinician—patient interaction has had a promising start, much research is still needed, especially that which experimentally manipulates either antecedent variables or nonverbal behavior itself to demonstrate cause and effect relations.
Psychiatric clinicians should use rating scales and questionnaires often, for they not only facilitate targeted diagnoses and treatment; they also facilitate links to empirical literature and systematize the entire process of management. Clinically oriented and highly practical, the Handbook of Clinical Rating Scales and Assessment in Psychiatry and Mental Health is an ideal tool for the busy psychiatrist, clinical psychologist, family physician, or social worker. In this ground-breaking text, leading researchers provide reviews of the most commonly used outcome and screening measures for the major psychiatric diagnoses and treatment scenarios. The full range of psychiatric disorders are covered in brief but thorough chapters, each of which provides a concise review of measurement issues related to the relevant condition, along with recommendations on which dimensions to measure – and when. The Handbook also includes ready-to-photocopy versions of the most popular, valid, and reliable scales and checklists, along with scoring keys and links to websites containing on-line versions. Moreover, the Handbook describes well known, structured, diagnostic interviews and the specialized training requirements for each. It also includes details of popular psychological tests (such as neuropsychological, personality, and projective tests), along with practical guidelines on when to request psychological testing, how to discuss the case with the assessment consultant and how to integrate information from the final testing report into treatment.
Focused and immensely useful, the Handbook of Clinical Rating Scales and Assessment in Psychiatry and Mental Health is an invaluable resource for all clinicians who care for patients with psychiatric disorders.
This article presents a summary of self-report adult measures that are considered to be most relevant for the assessment of depression in the context of rheumatology clinical and/or research practice. This piece represents an update of the special issue article that appeared in Arthritis Care & Research in 2003; the current review followed similar selection criteria for inclusion of assessment tools. Specifically, measures were selected based on several considerations, including ease of administration, interpretation, and adoption by arthritis health professionals from varying backgrounds and training perspectives; self-report measures providing data from the patient or research participant's perspective; availability of adequate psychometric literature and data involving rheumatology populations; and frequent use in both clinical and research practice with adult rheumatology populations. This study was not intended to be exhaustive. Clinician-administered, semistructured depression interviews requiring specialized training such as the Hamilton Rating Scale for Depression and Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition were not included. Additionally, measures without sufficient use within rheumatology populations, such as the World Health Organization Composite International Diagnostic Interview depression module and the National Institutes of Health Patient-Reported Outcomes Measurement Information System, were also not included in this review.
Valid, reliable biomarkers of depression severity and treatment response would provide new targets for clinical research. Noticeable differences in speech production between depressed and nondepressed patients have been suggested as a potential biomarker.
One hundred five adults with major depression were recruited into a 4-week, randomized, double-blind, placebo-controlled research methodology study. An exploratory objective of the study was to evaluate the generalizability and repeatability of prior study results indicating vocal acoustic properties in speech may serve as biomarkers for depression severity and response to treatment. Speech samples, collected at baseline and study end point using an automated telephone system, were analyzed as a function of clinician-rated and patient-reported measures of depression severity and treatment response.
Regression models of speech pattern changes associated with clinical outcomes in a prior study were found to be reliable and significant predictors of outcome in the current study, despite differences in the methodological design and implementation of the two studies. Results of the current study replicate and support findings from the prior study. Clinical changes in depressive symptoms among patients responding to the treatments provided also reflected significant differences in speech production patterns. Depressed patients who did not improve clinically showed smaller vocal acoustic changes and/or changes that were directionally opposite to treatment responders.
This study supports the feasibility and validity of obtaining clinically important, biologically based vocal acoustic measures of depression severity and treatment response using an automated telephone system.
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