Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

“… Patient Selection: Overall Risk of Bias Patient Election: Concerns Regarding Applicability Index Test: Index Test Results Interpreted Without Knowledge of the Results of the Reference Standard? Index Test: If Threshold Pre-Specified Index Test: Overall Risk of Bias Index Test: Concerns Regarding Applicability Reference Test: Reference Test Correctly Classifies Target Condition Reference Test: Reference Standard Results Interpreted Blind to Index Test Reference Test: Overall Risk of Bias Reference Test: Concerns Regarding Applicability Flow/Timing: Appropriate Interval Between Index Test and Reference Standard Flow/Timing: All Participants Receive Same Reference Test Flow/Timing: All Participants Included in Analysis Flow/Timing: Overall Risk of Bias Flow/Timing: Concerns Regarding Applicability Dogrucu 2020a 54 ◎ ○ ◎ ◎ ◎ ○ ○ ▽ ▽ ◎ ● ◎ ◎ ○ ○ ○ ▽ ▽ Dogrucu 2020b 54 ◎ ○ ◎ ◎ ◎ ○ ○ ▽ ▽ ◎ ● ◎ ◎ ○ ○ ○ ▽ ▽ Dogrucu 2020c 54 ◎ ○ ...…”

“…In addition, the level of depression indicated by the PHQ-9 score shows a high correlation with the features of depression that can be detected by a smartphone. 12,42,[53][54][55] In addition to the merits of PHQ-9 as a discriminate tool for depression, it has high sensitivity and specificity. In a primary care study PHQ-9 data, the algorithm sensitivity and specificity was 73% and 98%, 56 respectively.…”

“…44 In recent studies, diagnostic measures [eg, sensitivity, specificity, area under the receiver operating characteristic curve (AUC)] are implemented in machine learning by extending the existing statistical approach for the selection criteria and prediction of PHQ-9. 13,47,54,[57][58][59] Machine learning is used in psychiatry to increase the accuracy of diagnosis and prognosis and make treatment and prevention decisions. 60,61 It is particularly useful for predicting human behavior, including high-risk behaviors, and it is effective for discriminating psychopathology.…”

“…These have strengths in the identification of depression by using machine learning techniques. 53,54,58,59,70,71 In this study, studies were analyzed using machine learning techniques on PHQ-9 depression screening data collected through mobile devices according to the latest depression screening trends. In addition, we intend to contribute to predicting depression through mobile devices in the future by examining the predictive diagnostic power of PHQ-9 on mobile devices through diagnostic metaanalysis.…”

“…These have strengths in the identification of depression by using machine learning techniques. 53 , 54 , 58 , 59 , 70 , 71 …”