Deep Stacked Generalization Ensemble Learning models in early diagnosis of Depression illness from wearable devices data

Nguyen, Duc-Khanh; Chan, Chien-Lung; Li, Ai-Hsien Adams; Phan, Dinh-Van

doi:10.1145/3472813.3472815

Cited by 15 publications

(20 citation statements)

References 5 publications

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“…Year of publication, n (%) [27,30,38,48,52,59,63,64,69,81] 10 ( 13) 2022 [19][20][21]23,25,28,41,45,49,54,61,62,68,73,74,77,78] 17 (25) 2021 [22,29,31,33,40,43,44,53,57,60,66,70,71,76,79] 15 (22) 2020 [26,32,34,42,46,47,51,56,…”

Section: References Values Featuresmentioning

confidence: 99%

“…AI category [19,20,23,25,26,31,33,34,37,39,40,42,46,[49][50][51][52][53][54][55][56][57][58][59][60][61][63][64][65][66][67][69][70][71]73,[75][76][77][78][79][80][81]83,84,86,87] 46 (67) ML a [24,29,32,44,47,62,82...…”

Section: References Studies N (%) Featurementioning

confidence: 99%

“…Ground truth assessment e [19,20,32,[34][35][36]42,43,47,48,62,65,[68][69][70][71]86] 17 (25) MADRS f [23,24,27,28,30,38,52,53,59,60,73,77,83 [27,29,33,37,40,41,45,46,[49][50][51]54,57,58,63,64,66,69,75,76,78,…”

Section: (19) Predictionmentioning

confidence: 99%

“…Validation approach k [21][22][23][24]27,30,32,34,35,37,38,40,41,45,47,51,52,60,62,63,66,68,69,[73][74][75][78][79][80][81][82][83]87] 33 (48) k-fold cross-validation [26,28,29,31,32,34,37,[44][45][46]48,49,51,[60][61][62]66,67,…”

Section: (4) Not Reportedmentioning

confidence: 99%

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Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

Abd‐Alrazaq¹,

AlSaad²,

Aziz³

et al. 2023

J Med Internet Res

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Background Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services. Objective This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. Methods We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis. Results Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine. Conclusions Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies’ results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.

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Section: References Values Featuresmentioning

confidence: 99%

Section: References Studies N (%) Featurementioning

confidence: 99%

Section: (19) Predictionmentioning

confidence: 99%

Section: (4) Not Reportedmentioning

confidence: 99%

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Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

Abd‐Alrazaq¹,

AlSaad²,

Aziz³

et al. 2023

J Med Internet Res

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“…The work aimed to classify non-schizophrenic and schizophrenic participants based on the HMM, and the results showed that the features of the HMM were outperforming other models in terms of classifying non-schizophrenic and schizophrenic participants. Nguyen et al 45 presented a deep stacked generalization ensemble learning approach to classifying healthy controls and depressed patients in a study that shared a dataset with the current study. However, the method of processing the dataset likely led to underestimation of the true generalization error.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

Nguyen

Chan

et al. 2022

Health Informatics J

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In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients’ families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.

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