A hierarchical attention network-based approach for depression detection from transcribed clinical interviews

The high prevalence of depression in society has given rise to a need for new digital tools that can aid its early detection. Among other effects, depression impacts the use of language. Seeking to exploit this, this work focuses on the detection of depressed and non-depressed individuals through the analysis of linguistic information extracted from transcripts of clinical interviews with a virtual agent. Specifically, we investigated the advantages of employing hierarchical attention-based networks for this task. Using Global Vectors (GloVe) pretrained word embedding models to extract low-level representations of the words, we compared hierarchical local-global attention networks and hierarchical contextual attention networks. We performed our experiments on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WoZ) dataset, which contains audio, visual, and linguistic information acquired from participants during a clinical session. Our results using the DAIC-WoZ test set indicate that hierarchical contextual attention networks are the most suitable configuration to detect depression from transcripts. The configuration achieves an Unweighted Average Recall (UAR) of .66 using the test set, surpassing our baseline, a Recurrent Neural Network that does not use attention.Funding by EU- sustAGE (826506), EU-RADAR-CNS (115902), Key Program of the Natural Science Foundation of Tianjin, CHINA (18JCZDJC36300) and BMW Group Research Pages 221-225 https://www.isca-speech.org/archive/Interspeech_2019/index.htm

The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level

Depression is a serious medical condition that is suffered by a large number of people around the world. It significantly affects the way one feels, causing a persistent lowering of mood. In this paper, we propose a novel attention-based deep neural network which facilitates the fusion of various modalities. We use this network to regress the depression level. Acoustic, text and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we empirically justify that the fusion of all three modalities helps in giving the most accurate estimation of depression level. Our proposed approach outperforms the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on mean absolute error (MAE).Comment: 10 pages including references, 2 figure

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Supporting Young Elite Athletes With Mental Health Issues: Coaches’ Experience and Their Perceived Role

This study explored talent-development coaches’ experiences of athletes having faced mental health issues (MHIs). A second objective was to allow participants to share their opinion on how sport environments could improve the support offered to coaches and athletes encountering MHIs. A thematic analysis was performed on 11 verbatim-transcribed interviews conducted with UK-based talent-development coaches. While monitoring and supporting their athletes’ performance and well-being were viewed as day-to-day practice, dealing with MHIs was, however, not considered part of their role for a variety of reasons. Findings also suggest that coaches need more suitable and context-specific knowledge and tools to appropriately respond to and support their athletes. Generating a better understanding of coaches’ perceived role, knowledge, and needs to adequately support their athletes suffering from MHIs is crucial for the design of sport-specific interventions and for the athletes themselves

Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

Integration and Continuity of Primary Care: Polyclinics and Alternatives, a Patient-Centred Analysis of How Organisation Constrains Care Coordination

Background An ageing population, increasingly specialised of clinical services and diverse healthcare provider ownership make the coordination and continuity of complex care increasingly problematic. The way in which the provision of complex healthcare is coordinated produces – or fails to – six forms of continuity of care (cross-sectional, longitudinal, flexible, access, informational, relational). Care coordination is accomplished by a combination of activities by: patients themselves; provider organisations; care networks coordinating the separate provider organisations; and overall health system governance. This research examines how far organisational integration might promote care coordination at the clinical level. Objectives To examine: 1. What differences the organisational integration of primary care makes, compared with network governance, to horizontal and vertical coordination of care. 2. What difference provider ownership (corporate, partnership, public) makes. 3. How much scope either structure allows for managerial discretion and ‘performance’. 4. Differences between networked and hierarchical governance regarding the continuity and integration of primary care. 5. The implications of the above for managerial practice in primary care. Methods Multiple-methods design combining: 1. Assembly of an analytic framework by non-systematic review. 2. Framework analysis of patients’ experiences of the continuities of care. 3. Systematic comparison of organisational case studies made in the same study sites. 4. A cross-country comparison of care coordination mechanisms found in our NHS study sites with those in publicly owned and managed Swedish polyclinics. 5. Analysis and synthesis of data using an ‘inside-out’ analytic strategy. Study sites included professional partnership, corporate and publicly owned and managed primary care providers, and different configurations of organisational integration or separation of community health services, mental health services, social services and acute in-patient care. Results Starting from data about patients' experiences of the coordination or under-coordination of care we identified: 1. Five care coordination mechanisms present in both the integrated organisations and the care networks. 2. Four main obstacles to care coordination within the integrated organisations, of which two were also present in the care networks. 3. Seven main obstacles to care coordination that were specific to the care networks. 4. Nine care coordination mechanisms present in the integrated organisations. Taking everything into consideration, integrated organisations appeared more favourable to producing continuities of care than were care networks. Network structures demonstrated more flexibility in adding services for small care groups temporarily, but the expansion of integrated organisations had advantages when adding new services on a longer term and larger scale. Ownership differences affected the range of services to which patients had direct access; primary care doctors’ managerial responsibilities (relevant to care coordination because of its impact on GP workload); and the scope for doctors to develop special interests. We found little difference between integrated organisations and care networks in terms of managerial discretion and performance. Conclusions On balance, an integrated organisation seems more likely to favour the development of care coordination, and therefore continuities of care, than a system of care networks. At least four different variants of ownership and management of organisationally integrated primary care providers are practicable in NHS-like settings

Multi-type outer product-based fusion of respiratory sounds for detecting COVID-19

This work presents an outer product-based approach to fuse the embedded representations learnt from the spectrograms of cough, breath, and speech samples for the automatic detection of COVID-19. To extract deep learnt representations from the spectrograms, we compare the performance of specific Convolutional Neural Networks (CNNs) trained from scratch and ResNet18-based CNNs fine-tuned for the task at hand. Furthermore, we investigate whether the patients' sex and the use of contextual attention mechanisms are beneficial. Our experiments use the dataset released as part of the Second Diagnosing COVID-19 using Acoustics (DiCOVA) Challenge. The results suggest the suitability of fusing breath and speech information to detect COVID-19. An Area Under the Curve (AUC) of 84.06 % is obtained on the test partition when using specific CNNs trained from scratch with contextual attention mechanisms. When using ResNet18-based CNNs for feature extraction, the baseline model scores the highest performance with an AUC of 84.26 %