Detecting Depression Using a Framework Combining Deep Multimodal Neural Networks with a Purpose-Built Automated Evaluation

Victor, Ezekiel; Aghajan, Zahra M.; Sewart, Amy R.; Christian, Ray

doi:10.31234/osf.io/vqpcz

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…12 13 Table 2 includes the characteristics of all included studies. 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87…”

Section: Resultsmentioning

confidence: 99%

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

2022

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Objective As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations. Methods We conducted a systematic literature review on three public databases. Two authors reviewed every abstract for inclusion. Articles were included if they used or developed machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics were excluded. Results From 2,260 identified papers, we included 78. Of the machine learning models used, neural networks were relied upon most frequently (44.9%). Studies had a median population of 661.5 patients, and diseases and disorders of 10 different body systems were studied. Of the 35.9% (N = 28) of papers that included race data, 57.1% (N = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1% were majority Black. In 75% (N = 21) of papers, White was the largest racial group represented. Of the papers included, 43.6% (N = 34) included the sex ratio of the patient population. Discussion With the power to build robust algorithms supported by massive quantities of clinical data, machine learning is shaping the future of diagnostics. Limitations of the underlying data create potential biases, especially if patient demographics are unknown or not included in the training. Conclusion As the movement toward clinical reliance on machine learning accelerates, both recording demographic information and using diverse training sets should be emphasized. Extrapolating algorithms to demographics beyond the original study population leaves large gaps for potential biases.

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Section: Resultsmentioning

confidence: 99%

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

2022

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“…The accuracy and precision evaluation parameters can also be computed using the confusion matrix. The "True Positive, True Negative, False Positive, and False Negative" can be computed [49], as shown in Figure 2. Apart from taking ready to use data for building the model more labeled data from the psychiatrists and mental health specialists can be taken to obtain more accurate results.…”

Section: Referencementioning

confidence: 99%

On the review of image and video-based depression detection using machine learning

Ashraf

Gunawan

Riza³

et al. 2020

IJEECS

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<span>Machine learning has been introduced in the sphere of the medical field to enhance the accuracy, precision, and analysis of diagnostics while reducing laborious jobs. With the mounting evidence, machine learning has the capability to detect mental distress like depression. Since depression is the most prevalent mental disorder in our society at present, and almost the majority of the population suffers from this issue. Hence there is an extreme need for the depression detection models, which will provide a support system and early detection of depression. This review is based on the image and video-based depression detection model using machine learning techniques. This paper analyses the data acquisition techniques along with their databases. The indicators of depression are also reviewed in this paper. The evaluation of different researches, along with their performance parameters, is summarized. The paper concludes with remarks about the techniques used and the future scope of using the image and video-based depression prediction. </span>

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Internet-Based Management for Depressive Disorder

Wang

Niu

Yang

et al. 2019

Advances in Experimental Medicine and Biology

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Detecting Depression Using a Framework Combining Deep Multimodal Neural Networks with a Purpose-Built Automated Evaluation

Cited by 3 publications

References 4 publications

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

On the review of image and video-based depression detection using machine learning

Internet-Based Management for Depressive Disorder

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