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Journal articles on the topic 'Automated depression estimation'
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Mohamed, Islam Ismail, Mohamed Tarek El-Wakad, Khaled Abbas Shafie, Mohamed A. Aboamer, and Nader A. Rahman Mohamed. "Major depressive disorder: early detection using deep learning and pupil diameter." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (August 1, 2024): 916. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp916-932.
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Major depressive disorder stands as a highly prevalent mental disorder on a global scale. Detecting depression at its early stages holds paramount importance for effective treatment. However, due to the coexistence of depression with other conditions and the subjective nature of diagnosis, early identification poses a significant challenge. In recent times, machine learning techniques have emerged as valuable tools for the development of automated depression estimation systems, aiding in the diagnostic process. In this particular study, a deep learning approach utilizing pupil diameter was employed to distinguish between individuals diagnosed with depression and those who are considered mentally healthy. Pupillometric recordings were collected from a total of 58 individuals, comprising 29 healthy individuals and 29 individuals diagnosed with depression. Pupil size was recorded every 4 ms. The performance of three pretrained convolutional neural networks (GoogLeNet, SqueezeNet, and AlexNet) was evaluated for depression classification using the pupil size data. The highest accuracy of 98.28% was obtained with AlexNet. This finding highlights the potential of utilizing pupil diameter as a reliable indicator for objectively measuring depression.
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Bensassi, I., J. Lopez-Castroman, R. Calati, and P. Courtet. "Hippocampal Volume Recovery After Depression: Evidence from an Elderly Sample." European Psychiatry 41, S1 (April 2017): S170. http://dx.doi.org/10.1016/j.eurpsy.2017.01.2061.
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ObjectivesStructural neuroimaging studies have revealed a consistent pattern of volumetric reductions in both the hippocampus and the anterior cingulate cortex (ACC) of individuals with a major depressive episode (MDE). This study investigated hippocampal and ACC volume differences in the elderly comparing currently depressed individuals and individuals with a past lifetime history of MDE versus healthy controls.MethodsWe studied non-demented individuals from a cohort of community-dwelling people aged 65 and over (ESPRIT study). T1-weighted magnetic resonance images were used to acquire anatomical scans from 150 currently depressed individuals, 79 individuals with at least one past MDE, and 310 healthy controls. We derived quantitative regional estimates of subcortical volume of hippocampus and ACC using FreeSurfer Software (automated method). Concerning hippocampus, we also used a manual method of measurement. General Linear Model was used to study brain volumes in current and past depression adjusting for gender, age, education level, total brain volume, and anxiety disorder comorbidity.ResultsAfter adjustment, current depression was associated with a lower left posterior hippocampal volume (F = 10.38, P = 0.001) using manual estimation of volume. No other significant differences were observed. A positive correlation was found between time since the last MDE and left posterior hippocampal volume.ConclusionsThe finding of left posterior hippocampal volume reduction in currently depressed individuals but not in those with a past MDE compared to healthy controls could be related to brain neuroplasticity. Additionally, our results suggest manual measures to be more sensitive than automated methods.Disclosure of interestThe authors have not supplied their declaration of competing interest.
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KALPANA, V., S. T. HAMDE, and L. M. WAGHMARE. "NON-INVASIVE ESTIMATION OF DIABETES RELATED FEATURES FROM ECG USING GRAPHICAL PROGRAMAMING LANGUAGE AND MATLAB." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1240016. http://dx.doi.org/10.1142/s0219519412400167.
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Electrocardiography deals with the electrical activity of the heart. The condition of cardiac health is given by the electrocardiogram (ECG). ECG analysis is one of the most important aspects of research in the field of biomedical sciences and healthcare. The precision in the identification of various parameters in the ECG is of great importance for the reliability of an automated ECG analyzing system and diagnosis of cardiac diseases. Many algorithms have been developed in the last few years, each with their own advantages and limitations. In this work, we have developed an algorithm for 12-lead ECG parameter detection which works in three steps. Initially, the signal is denoised by the wavelet transform approach using a graphical programming language called LabVIEW (Laboratory Virtual Instrument Engineering Workbench). Next, primary features are detected from the denoised ECG signal using Matlab, and lastly, the secondary features related to diabetes are estimated from the detected primary features. Diabetes mellitus (DM), which is characterized by raised blood glucose levels in an individual, affects an estimated 2–4% of the world's population, making it one of the major chronic illnesses prevailing today. Recently, there has been increasing interest in the study of relationship between diabetes and cardiac health. Thus, in this work, we estimate diabetic-related secondary ECG features like corrected QT interval (QTc), QT dispersion (QTd), P wave dispersion (PD), and ST depression (STd). Our software performance is evaluated using CSE DS-3 multi-lead data base and the data acquired at SGGS IE & T, Nanded, MS, which contains 5000 samples recorded at a sampling frequency of 500 HZ. The proposed algorithm gives a sensitivity of 99.75% and a specificity of 99.83%.
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Zhang, Xin, Binayak Ojha, Hermann Bichlmaier, Ingo Hartmann, and Heinz Kohler. "Extensive Gaseous Emissions Reduction of Firewood-Fueled Low Power Fireplaces by a Gas Sensor Based Advanced Combustion Airflow Control System and Catalytic Post-Oxidation." Sensors 23, no. 10 (May 11, 2023): 4679. http://dx.doi.org/10.3390/s23104679.
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In view of the tremendous emissions of toxic gases and particulate matter (PM) by low-power firewood-fueled fireplaces, there is an urgent need for effective measures to lower emissions to keep this renewable and economical source for private home heating available in the future. For this purpose, an advanced combustion air control system was developed and tested on a commercial fireplace (HKD7, Bunner GmbH, Eggenfelden, Germany), complemented with a commercial oxidation catalyst (EmTechEngineering GmbH, Leipzig, Germany) placed in the post-combustion zone. Combustion air stream control of the wood-log charge combustion was realized by five different control algorithms to describe all situations of combustion properly. These control algorithms are based on the signals of commercial sensors representing catalyst temperature (thermocouple), residual oxygen concentration (LSU 4.9, Bosch GmbH, Gerlingen, Germany) and CO/HC-content in the exhaust (LH-sensor, Lamtec Mess- und Regeltechnik für Feuerungen GmbH & Co. KG, Walldorf (Germany)). The actual flows of the combustion air streams, as calculated for the primary and secondary combustion zone, are adjusted by motor-driven shutters and commercial air mass flow sensors (HFM7, Bosch GmbH, Gerlingen, Germany) in separate feedback control loops. For the first time, the residual CO/HC-content (CO, methane, formaldehyde, etc.) in the flue gas is in-situ monitored with a long-term stable AuPt/YSZ/Pt mixed potential high-temperature gas sensor, which allows continuous estimation of the flue gas quality with an accuracy of about ±10%. This parameter is not only an essential input for advanced combustion air stream control but also provides monitoring of the actual combustion quality and logging of this value over a whole heating period. By many firing experiments in the laboratory and by field tests over four months, it could be demonstrated that with this long-term stable and advanced automated firing system, depression of the gaseous emissions by about 90% related to manually operated fireplaces without catalyst could be achieved. In addition, preliminary investigations at a firing appliance complemented by an electrostatic precipitator yielded PM emission depression between 70% and 90%, depending on the firewood load.
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Copăcean, Loredana, Luminiţa Cojocariu, M. Simon, I. Zisu, and C. Popescu. "GEOMATIC TECHNIQUES APPLIED FOR REMOTE DETERMINATION OF THE HAY QUANTITY IN AGROSILVOPASTORAL SYSTEMS." Present Environment and Sustainable Development 14, no. 2 (October 14, 2020): 89–101. http://dx.doi.org/10.15551/pesd2020142006.
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The paper presents a descriptive model, applicable in agricultural theory and practice, for determining the quantity of alfalfa hay obtained from a land surface, using remote investigations, by geospatial methods and means. The working algorithm was tested in a rural area located in the northern part of Romania, in the Humor Depression, and the data acquisition was made with DJI Phantom 4 Pro - Unmanned Aerial Vehicle equipment. For the automated calculation of the amount of alfalfa hay harvested from a certain surface and stored as haystacks, the following steps were carried out: processing the images acquired with the drone to obtain the point clouds, determining the 3D model of the haystacks, calculating the volume of hay stored in the stacks and converting the volume in quantity of hay/surface. As a result of the measurements and calculations carried out, a quantity of hay of 11.96 tons/ha was obtained, data verified and validated by the researches from the specialized literature. Compared with the agronomic methods, the use of the geomatics techniques, to determine the quantity of hay harvested from an agricultural area, presents a series of practical and economic advantages: they exclude the manual measurements in the field and, therefore, the displacements on extended surfaces; reduce the working time; have high precision because, for the estimation of the haystacks volume, three-dimensional models are used, instead of the traditional mathematical formulas. At the same time, geospatial data is acquired through drone flying, which can be used in other types of analysis. The working algorithm can also be applied to other studied objectives or research topics.
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An, Yi, Zhen Qu, Ning Xu, and Zhaxi Nima. "Automatic depression estimation using facial appearance." Journal of Image and Graphics 25, no. 11 (2020): 2415–27. http://dx.doi.org/10.11834/jig.200322.
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Sun, Hao, Jiaqing Liu, Shurong Chai, Zhaolin Qiu, Lanfen Lin, Xinyin Huang, and Yenwei Chen. "Multi-Modal Adaptive Fusion Transformer Network for the Estimation of Depression Level." Sensors 21, no. 14 (July 12, 2021): 4764. http://dx.doi.org/10.3390/s21144764.
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Depression is a severe psychological condition that affects millions of people worldwide. As depression has received more attention in recent years, it has become imperative to develop automatic methods for detecting depression. Although numerous machine learning methods have been proposed for estimating the levels of depression via audio, visual, and audiovisual emotion sensing, several challenges still exist. For example, it is difficult to extract long-term temporal context information from long sequences of audio and visual data, and it is also difficult to select and fuse useful multi-modal information or features effectively. In addition, how to include other information or tasks to enhance the estimation accuracy is also one of the challenges. In this study, we propose a multi-modal adaptive fusion transformer network for estimating the levels of depression. Transformer-based models have achieved state-of-the-art performance in language understanding and sequence modeling. Thus, the proposed transformer-based network is utilized to extract long-term temporal context information from uni-modal audio and visual data in our work. This is the first transformer-based approach for depression detection. We also propose an adaptive fusion method for adaptively fusing useful multi-modal features. Furthermore, inspired by current multi-task learning work, we also incorporate an auxiliary task (depression classification) to enhance the main task of depression level regression (estimation). The effectiveness of the proposed method has been validated on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge) in terms of the PHQ-8 scores. Experimental results indicate that the proposed method achieves better performance compared with currently state-of-the-art methods. Our proposed method achieves a concordance correlation coefficient (CCC) of 0.733 on AVEC 2019 which is 6.2% higher than the accuracy (CCC = 0.696) of the state-of-the-art method.
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Kashid, Onkar, Rashmi Bhumbare, Eshwar Dange, Ajit Waghmare, and Raj Nikam. "Depression Monitoring System via Social Media Data using Machine Learning frameworkk." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 3431–37. http://dx.doi.org/10.22214/ijraset.2023.51811.
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Abstract: Stress and Depression is one of the most widely recognized and handicapping mental issue that relevantly affects society. Automatic health monitoring systems could be crucial and important to improve depression and stress detection system using social networking. Sentiment Analysis alludes to the utilization of natural language processing and content mining approaches planning to recognize feeling or opinion. Full of feeling Computing is the examination and advancement of frameworks and gadgets that can perceive, decipher, process, and mimic human effects. Sentiment Analysis and deep learning techniques could give powerful algorithms and frameworks to a target appraisal and observing of mental issue and, specifically of depression and stress. In this paper, the application of sentiment analysis and deep learning methodologies to depression and stress detection and monitoring are discussed. In addition, a fundamental plan of an incorporated multimodal framework for stress and depression checking, that incorporates estimation investigation and full of feeling processing strategies, is proposed. In particular, the paper traces the fundamental issues and moves comparative with the structure of such a framework.
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Kaur, Chamandeep, Preeti Singh, and Sukhtej Sahni. "Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique." European Neurology 81, no. 1-2 (2019): 63–75. http://dx.doi.org/10.1159/000500414.
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Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signals of depression patients. Method: As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying VMD. Then, closely related functions are analyzed using the inverse modelling-based source localization procedures such as sLORETA. Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. Results: The performance of the algorithm has been assessed using localization error (LE), mean square error and signal to noise ratio output corresponding to simulated EEG dipole sources and real EEG signals for depression. In order to study the spatial resolution for cortical potential distribution, the main focus has been on studying the effects of noise sources and estimating LE of inverse solutions. More accurate and robust localization results show that this methodology is very promising for EEG source localization of depression signals. Conclusion: It can be said that proposed algorithm efficiently suppresses the influence of noise in the EEG inverse problem using simulated EEG activity and EEG database for depression. Such a system may offer an effective solution for clinicians as a crucial stage of EEG pre-processing in automated depression detection systems and may prevent delay in diagnosis.
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Ghosh, Priyanka, Siddharth Talwar, and Arpan Banerjee. "Unsupervised Characterization of Prediction Error Markers in Unisensory and Multisensory Streams Reveal the Spatiotemporal Hierarchy of Cortical Information Processing." eneuro 11, no. 5 (May 2024): ENEURO.0251–23.2024. http://dx.doi.org/10.1523/eneuro.0251-23.2024.
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Elicited upon violation of regularity in stimulus presentation, mismatch negativity (MMN) reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection whereas P300 is associated with cognitive processes such as updating of the working memory. To date, there has been extensive research on the roles of MMN and P300 individually, because of their potential to be used as clinical markers of consciousness and attention, respectively. Here, we intend to explore with an unsupervised and rigorous source estimation approach, the underlying cortical generators of MMN and P300, in the context of prediction error propagation along the hierarchies of brain information processing in healthy human participants. The existing methods of characterizing the two ERPs involve only approximate estimations of their amplitudes and latencies based on specific sensors of interest. Our objective is twofold: first, we introduce a novel data-driven unsupervised approach to compute latencies and amplitude of ERP components accurately on an individual-subject basis and reconfirm earlier findings. Second, we demonstrate that in multisensory environments, MMN generators seem to reflect a significant overlap of “modality-specific” and “modality-independent” information processing while P300 generators mark a shift toward completely “modality-independent” processing. Advancing earlier understanding that multisensory contexts speed up early sensory processing, our study reveals that temporal facilitation extends to even the later components of prediction error processing, using EEG experiments. Such knowledge can be of value to clinical research for characterizing the key developmental stages of lifespan aging, schizophrenia, and depression.
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Brennan, Michael J., Christopher C. Hennon, and Richard D. Knabb. "The Operational Use of QuikSCAT Ocean Surface Vector Winds at the National Hurricane Center." Weather and Forecasting 24, no. 3 (June 1, 2009): 621–45. http://dx.doi.org/10.1175/2008waf2222188.1.
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Abstract The utility and shortcomings of near-real-time ocean surface vector wind retrievals from the NASA Quick Scatterometer (QuikSCAT) in operational forecast and analysis activities at the National Hurricane Center (NHC) are described. The use of QuikSCAT data in tropical cyclone (TC) analysis and forecasting for center location/identification, intensity (maximum sustained wind) estimation, and analysis of outer wind radii is presented, along with shortcomings of the data due to the effects of rain contamination and wind direction uncertainties. Automated QuikSCAT solutions in TCs often fail to show a closed circulation, and those that do are often biased to the southwest of the NHC best-track position. QuikSCAT winds show the greatest skill in TC intensity estimation in moderate to strong tropical storms. In tropical depressions, a positive bias in QuikSCAT winds is seen due to enhanced backscatter by rain, while in major hurricanes rain attenuation, resolution, and signal saturation result in a large negative bias in QuikSCAT intensity estimates. QuikSCAT wind data help overcome the large surface data void in the analysis and forecast area of NHC’s Tropical Analysis and Forecast Branch (TAFB). These data have resulted in improved analyses of surface features, better definition of high wind areas, and improved forecasts of high-wind events. The development of a climatology of gap wind events in the Gulf of Tehuantepec has been possible due to QuikSCAT wind data in a largely data-void region. The shortcomings of ocean surface vector winds from QuikSCAT in the operational environment at NHC are described, along with requirements for future ocean surface vector wind missions. These include improvements in the timeliness and quality of the data, increasing the wind speed range over which the data are reliable, and decreasing the impact of rain to allow for accurate retrievals in all-weather conditions.
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Ruiz, Francisco J., Miguel A. Segura-Vargas, Paula Odriozola-González, and Juan C. Suárez-Falcón. "Psychometric properties of the Automatic Thoughts Questionnaire-8 in two Spanish nonclinical samples." PeerJ 8 (September 16, 2020): e9747. http://dx.doi.org/10.7717/peerj.9747.
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Background The ATQ is a widely used instrument consisting of 30 items that assess the frequency of negative automatic thoughts. However, the extensive length of the ATQ could compromise its measurement efficiency in survey research. Consequently, an 8-item shortened version of the ATQ has been developed. This study aims to analyze the validity of the ATQ-8 in two Spanish samples. Method The ATQ-8 was administered to a total sample of 1,148 participants (302 undergraduates and 846 general online population). To analyze convergent construct validity, the questionnaire package also included the Dysfunctional Attitude Scale-Revised (DAS-R), Depression Anxiety and Stress Scale-21 (DASS-21), Acceptance Action Questionnaire-II (AAQ-II), Cognitive Fusion Questionnaire (CFQ), Generalized Pliance Questionnaire (GPQ), and Satisfaction with Life Scale (SWLS). To analyze internal consistency, we computed Cronbach’s alpha and McDonald’s omega. A confirmatory factor analysis was conducted to test the one-factor structure of the ATQ-8. In so doing, a robust diagonally weighted least square estimation method (Robust DWLS) was adopted using polychoric correlations. Afterward, we analyzed measurement invariance across samples, gender, groupage, and education level. Lastly, we evaluated convergent construct validity by computing Pearson correlations between the ATQ-8 and the remaining instruments. Results The internal consistency across samples was adequate (alpha and omega = .89). The one-factor model demonstrated a good fit to the data (RMSEA = 0.10, 90% CI [0.089, 0.112], CFI = 0.98, NNFI = 0.97, and SRMR = 0.048). The ATQ-8 showed scalar metric invariance across samples, gender, groupage, and education level. The ATQ-8 scores were significantly associated with emotional symptoms (DASS-21), satisfaction with life (SWLS), dysfunctional schemas (DAS-R), cognitive fusion (CFQ), experiential avoidance (AAQ-II), and generalized pliance (GPQ). In conclusion, the Spanish version of the ATQ-8 demonstrated adequate psychometric properties in Spanish samples.
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Wang, Jitao, Zhenke Wu, Sung Won Choi, Srijan Sen, Xinghui Yan, Jennifer A. Miner, Angelle M. Sander, Angela K. Lyden, Jonathan P. Troost, and Noelle E. Carlozzi. "The Dosing of Mobile-Based Just-in-Time Adaptive Self-Management Prompts for Caregivers: Preliminary Findings From a Pilot Microrandomized Study." JMIR Formative Research 7 (September 14, 2023): e43099. http://dx.doi.org/10.2196/43099.
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Background Caregivers of people with chronic illnesses often face negative stress-related health outcomes and are unavailable for traditional face-to-face interventions due to the intensity and constraints of their caregiver role. Just-in-time adaptive interventions (JITAIs) have emerged as a design framework that is particularly suited for interventional mobile health studies that deliver in-the-moment prompts that aim to promote healthy behavioral and psychological changes while minimizing user burden and expense. While JITAIs have the potential to improve caregivers’ health-related quality of life (HRQOL), their effectiveness for caregivers remains poorly understood. Objective The primary objective of this study is to evaluate the dose-response relationship of a fully automated JITAI-based self-management intervention involving personalized mobile app notifications targeted at decreasing the level of caregiver strain, anxiety, and depression. The secondary objective is to investigate whether the effectiveness of this mobile health intervention was moderated by the caregiver group. We also explored whether the effectiveness of this intervention was moderated by (1) previous HRQOL measures, (2) the number of weeks in the study, (3) step count, and (4) minutes of sleep. Methods We examined 36 caregivers from 3 disease groups (10 from spinal cord injury, 11 from Huntington disease, and 25 from allogeneic hematopoietic cell transplantation) in the intervention arm of a larger randomized controlled trial (subjects in the other arm received no prompts from the mobile app) designed to examine the acceptability and feasibility of this intensive type of trial design. A series of multivariate linear models implementing a weighted and centered least squares estimator were used to assess the JITAI efficacy and effect. Results We found preliminary support for a positive dose-response relationship between the number of administered JITAI messages and JITAI efficacy in improving caregiver strain, anxiety, and depression; while most of these associations did not meet conventional levels of significance, there was a significant association between high-frequency JITAI and caregiver strain. Specifically, administering 5-6 messages per week as opposed to no messages resulted in a significant decrease in the HRQOL score of caregiver strain with an estimate of –6.31 (95% CI –11.76 to –0.12; P=.046). In addition, we found that the caregiver groups and the participants’ levels of depression in the previous week moderated JITAI efficacy. Conclusions This study provides preliminary evidence to support the effectiveness of the self-management JITAI and offers practical guidance for designing future personalized JITAI strategies for diverse caregiver groups. Trial Registration ClinicalTrials.gov NCT04556591; https://clinicaltrials.gov/ct2/show/NCT04556591
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Riemann, Dieter, Raphael J. Dressle, Fee Benz, Laura Palagini, and Bernd Feige. "The Psychoneurobiology of Insomnia: Hyperarousal and REM Sleep Instability." Clinical and Translational Neuroscience 7, no. 4 (September 28, 2023): 30. http://dx.doi.org/10.3390/ctn7040030.
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Chronic insomnia (insomnia disorder—ID) afflicts up to 10% of the adult population, increases with age and affects more women than men. ID is associated with significant daytime impairments and an increased risk for developing major somatic and mental disorders, especially depression and anxiety disorders. Almost all insomnia models assume persistent hyperarousal on cognitive, emotional, cortical and physiological levels as a central pathophysiological component. The marked discrepancy between only minor objective alterations in polysomnographic parameters of sleep continuity and the profound subjective impairment in patients with insomnia is still puzzling. We and others have proposed that alterations in the microstructure of sleep, especially in REM sleep (REM sleep instability), may explain this discrepancy and be at the core of the experience of fragmented and poor sleep in ID. The REM sleep instability concept is based on evidence showing REM time to be related to subjective wake time in insomnia as well as increased micro- and macro-arousals during REM sleep in insomnia patients compared to good-sleeper controls. Our own work showed that ID patients awoken from REM sleep more frequently reported the perception of having been awake than good sleepers as well as having had more negative ideations. The continuous measurement of event-related potentials throughout the whole night demonstrated reduced P2 amplitudes specifically during phasic REM sleep in insomnia, which points to a mismatch negativity in ID reflecting automatic change detection in the auditory system and a concomitant orienting response. REM sleep represents the most highly aroused brain state during sleep and thus might be particularly prone to fragmentation in individuals with persistent hyperarousal, resulting in a more conscious-like wake experience reflecting pre-sleep concerns of patients with ID, i.e., worries about poor sleep and its consequences, thus leading to the subjective over-estimation of nocturnal waking time and the experience of disrupted and non-restorative sleep. Chronic REM sleep instability might also lead to a dysfunction in a ventral emotional neural network, including limbic and paralimbic areas activated during REM sleep. Along with a postulated weakened functioning in a dorsal executive neural network, including frontal and prefrontal areas, this might contribute to emotional and cognitive alterations and an elevated risk of developing depression and anxiety.
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Chen, Ming, Jane S. Hankins, Min Zhang, Justin Gatwood, James E. Bailey, and Kenneth I. Ataga. "Prevalence and Time Trends of Oral Anticoagulant Utilization in Adults with Sickle Cell Disease." Blood 142, Supplement 1 (November 28, 2023): 5294. http://dx.doi.org/10.1182/blood-2023-188030.
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Background: Sickle cell disease (SCD) is an inherited red blood cell disorder, caused by a genetic mutation in the β-hemoglobin chain. SCD is associated with chronic activation of coagulation and an increased risk of venous thromboembolism (VTE). Although the American Society of Hematology guidelines recommend indefinite anticoagulation for unprovoked or recurrent provoked VTE, no guidance is given regarding choice of anticoagulant due to a paucity of data. Existing evidence on the use of anticoagulants in patients with SCD has been mostly focused on oral anticoagulants, was limited by small sample sizes, and did not clearly demonstrate the temporal utilization patterns. Real-world evidence on oral anticoagulant use in SCD from large population-based studies is largely lacking. This study examined the prevalence and time trends of oral anticoagulant utilization in patients with SCD in the United States (US). Methods: This retrospective, repeated cross-sectional study analyzed the Cerner Health Facts® database from January 1, 2011, to December 31, 2016, which captures longitudinal electronic medical records (EMR) of patient data from participating facilities in the US. To be included, patients were required to (1) have ≥ 1 inpatient or emergency department encounters with SCD, or 2 outpatient encounters that were 30 days apart in a calendar year from 2012 to 2016, (2) have ≥ 1 prescription records on the encounters with SCD, (3) aged ≥ 18 years at index, and (4) have ≥ 1 encounter with SCD in a year prior to the index. For patients with multiple encounters with SCD in the calendar year, we randomly selected one encounter with SCD and defined it as the index. In this design, a patient contributed only one observation per year but may contribute to multiple years of observations if meeting the selection criterion. The outcome was the use of anticoagulants, defined as any prescription drug encounter for an oral anticoagulant (Vitamin K antagonist [VKA]: warfarin; direct oral anticoagulants [DOACs]: apixaban, rivaroxaban, dabigatran, edoxaban) within a grace period of 7 days discharged from the index. The independent variable was each calendar year measured as dummy variables. Covariates were measured during the 12-month baseline period prior to the index, which included patient demographics, comorbidities and complications of SCD, and facility characteristics. Time trends on the proportions of eligible patients with anticoagulant use against the calendar years of the SCD diagnosis were plotted, and the significance on the crude trend was examined using Cochran-Armitage trend test. In the main analysis, a modified Poisson regression by using generalized estimating equations (GEEs) estimated the adjusted prevalence ratios (APRs) for the trends and covariates associated with any anticoagulant utilization. In subgroup analysis, we estimated the APRs of covariates for the use of DOACs versus VKA. Results: The cohort included 8,824 adult patients with SCD in 182 facilities using automated Cerner EMR system for the years 2012 to 2016. Prescription rates of all oral anticoagulants gradually increased from 2012 to 2014; the rates of VKA prescriptions substantially dropped from 11.1/1000 to 4.1/1000 persons between 2014 and 2016, whereas prescription rates of DOACs increased significantly from 12.0/1000 to 29.1/1000. Prevalence of VTE was stable at approximately 6.2/1000 to 7.5/1000 between 2012 and 2016. Prescription rates of DOACs were lower in patients with (versus without) VTE (6.1/1000 vs 7.3/1000), while a reversed prescription pattern was observed for VKA (VTE [yes vs no]: 4.4/1000 vs 1.8/1000). Controlling for covariates, our main analysis demonstrated that patients with a diagnosis of acute chest syndrome, pulmonary hypertension, and depression were 1.51, 2.30, and 1.57 times more likely to have oral anticoagulant prescription, respectively (all p <0.05). Subgroup analysis showed that having a diagnosis of VTE was associated with a decreased likelihood of DOACs prescription relative to VKA (APR= 0.75, 95%CI: 0.62-0.91). Conclusion: Prescription of oral anticoagulants in adults with SCD has significantly increased from 2012 to 2016 and the increase was dominated by DOACs. The treatment paradox of a lower relative rate of DOACs utilization versus VKA in adults with SCD requires more study to understand the reasons and additional clinical outcomes associated with the utilization patterns.
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Uddin, Md Azher, Joolekha Bibi Joolee, and Kyung-Ah Sohn. "Deep Multi-Modal Network Based Automated Depression Severity Estimation." IEEE Transactions on Affective Computing, 2022, 1. http://dx.doi.org/10.1109/taffc.2022.3179478.
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Agarwal, Navneet, Gaël Dias, and Sonia Dollfus. "Multi-view graph-based interview representation to improve depression level estimation." Brain Informatics 11, no. 1 (June 4, 2024). http://dx.doi.org/10.1186/s40708-024-00227-w.
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AbstractDepression is a serious mental illness that affects millions worldwide and consequently has attracted considerable research interest in recent years. Within the field of automated depression estimation, most researchers focus on neural network architectures while ignoring other research directions. Within this paper, we explore an alternate approach and study the impact of input representations on the learning ability of the models. In particular, we work with graph-based representations to highlight different aspects of input transcripts, both at the interview and corpus levels. We use sentence similarity graphs and keyword correlation graphs to exemplify the advantages of graphical representations over sequential models for binary classification problems within depression estimation. Additionally, we design multi-view architectures that split interview transcripts into question and answer views in order to take into account dialogue structure. Our experiments show the benefits of multi-view based graphical input encodings over sequential models and provide new state-of-the-art results for binary classification on the gold standard DAIC-WOZ dataset. Further analysis establishes our method as a means for generating meaningful insights and visual summaries of interview transcripts that can be used by medical professionals.
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Milintsevich, Kirill, Kairit Sirts, and Gaël Dias. "Towards automatic text-based estimation of depression through symptom prediction." Brain Informatics 10, no. 1 (February 13, 2023). http://dx.doi.org/10.1186/s40708-023-00185-9.
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AbstractMajor Depressive Disorder (MDD) is one of the most common and comorbid mental disorders that impacts a person’s day-to-day activity. In addition, MDD affects one’s linguistic footprint, which is reflected by subtle changes in speech production. This allows us to use natural language processing (NLP) techniques to build a neural classifier to detect depression from speech transcripts. Typically, current NLP systems discriminate only between the depressed and non-depressed states. This approach, however, disregards the complexity of the clinical picture of depression, as different people with MDD can suffer from different sets of depression symptoms. Therefore, predicting individual symptoms can provide more fine-grained information about a person’s condition. In this work, we look at the depression classification problem through the prism of the symptom network analysis approach, which shifts attention from a categorical analysis of depression towards a personalized analysis of symptom profiles. For that purpose, we trained a multi-target hierarchical regression model to predict individual depression symptoms from patient–psychiatrist interview transcripts from the DAIC-WOZ corpus. Our model achieved results on par with state-of-the-art models on both binary diagnostic classification and depression severity prediction while at the same time providing a more fine-grained overview of individual symptoms for each person. The model achieved a mean absolute error (MAE) from 0.438 to 0.830 on eight depression symptoms and showed state-of-the-art results in binary depression estimation (73.9 macro-F1) and total depression score prediction (3.78 MAE). Moreover, the model produced a symptom correlation graph that is structurally identical to the real one. The proposed symptom-based approach provides more in-depth information about the depressive condition by focusing on the individual symptoms rather than a general binary diagnosis.
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Pérez, Anxo, Javier Parapar, and Álvaro Barreiro. "Automatic depression score estimation with word embedding models." Artificial Intelligence in Medicine, August 2022, 102380. http://dx.doi.org/10.1016/j.artmed.2022.102380.
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Chamanzar, Alireza, Jonathan Elmer, Lori Shutter, Jed Hartings, and Pulkit Grover. "Noninvasive and reliable automated detection of spreading depolarization in severe traumatic brain injury using scalp EEG." Communications Medicine 3, no. 1 (August 19, 2023). http://dx.doi.org/10.1038/s43856-023-00344-3.
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Abstract Background Spreading depolarizations (SDs) are a biomarker and a potentially treatable mechanism of worsening brain injury after traumatic brain injury (TBI). Noninvasive detection of SDs could transform critical care for brain injury patients but has remained elusive. Current methods to detect SDs are based on invasive intracranial recordings with limited spatial coverage. In this study, we establish the feasibility of automated SD detection through noninvasive scalp electroencephalography (EEG) for patients with severe TBI. Methods Building on our recent WAVEFRONT algorithm, we designed an automated SD detection method. This algorithm, with learnable parameters and improved velocity estimation, extracts and tracks propagating power depressions using low-density EEG. The dataset for testing our algorithm contains 700 total SDs in 12 severe TBI patients who underwent decompressive hemicraniectomy (DHC), labeled using ground-truth intracranial EEG recordings. We utilize simultaneously recorded, continuous, low-density (19 electrodes) scalp EEG signals, to quantify the detection accuracy of WAVEFRONT in terms of true positive rate (TPR), false positive rate (FPR), as well as the accuracy of estimating SD frequency. Results WAVEFRONT achieves the best average validation accuracy using Delta band EEG: 74% TPR with less than 1.5% FPR. Further, preliminary evidence suggests WAVEFRONT can estimate how frequently SDs may occur. Conclusions We establish the feasibility, and quantify the performance, of noninvasive SD detection after severe TBI using an automated algorithm. The algorithm, WAVEFRONT, can also potentially be used for diagnosis, monitoring, and tailoring treatments for worsening brain injury. Extension of these results to patients with intact skulls requires further study.
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Rostami, Reza, Reza Kazemi, Zahra Nasiri, Somayeh Ataei, Abed L. Hadipour, and Nematollah Jaafari. "Cold Cognition as Predictor of Treatment Response to rTMS; A Retrospective Study on Patients With Unipolar and Bipolar Depression." Frontiers in Human Neuroscience 16 (July 25, 2022). http://dx.doi.org/10.3389/fnhum.2022.888472.
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BackgroundCognitive impairments are prevalent in patients with unipolar and bipolar depressive disorder (UDD and BDD, respectively). Considering the fact assessing cognitive functions is increasingly feasible for clinicians and researchers, targeting these problems in treatment and using them at baseline as predictors of response to treatment can be very informative.MethodIn a naturalistic, retrospective study, data from 120 patients (Mean age: 33.58) with UDD (n = 56) and BDD (n = 64) were analyzed. Patients received 20 sessions of bilateral rTMS (10 Hz over LDLPFC and 1 HZ over RDLPFC) and were assessed regarding their depressive symptoms, sustained attention, working memory, and executive functions, using the Beck Depression Inventory (BDI-II) and Neuropsychological Test Automated Battery Cambridge, at baseline and after the end of rTMS treatment course. Generalized estimating equations (GEE) and logistic regression were used as the main statistical methods to test the hypotheses.ResultsFifty-three percentage of all patients (n = 64) responded to treatment. In particular, 53.1% of UDD patients (n = 34) and 46.9% of BDD patients (n = 30) responded to treatment. Bilateral rTMS improved all cognitive functions (attention, working memory, and executive function) except for visual memory and resulted in more modulations in the working memory of UDD compared to BDD patients. More improvements in working memory were observed in responded patients and visual memory, age, and sex were determined as treatment response predictors. Working memory, visual memory, and age were identified as treatment response predictors in BDD and UDD patients, respectively.ConclusionBilateral rTMS improved cold cognition and depressive symptoms in UDD and BDD patients, possibly by altering cognitive control mechanisms (top-down), and processing negative emotional bias.
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Mertens, Stien, Lennart Verbraeken, Heike Sprenger, Sam De Meyer, Kirin Demuynck, Bernard Cannoot, Julie Merchie, et al. "Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform." Plant Methods 19, no. 1 (November 23, 2023). http://dx.doi.org/10.1186/s13007-023-01102-1.
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Abstract Background Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. Results The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic differences in the plants’ water-use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated, including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple TIR indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. Conclusion Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.