Explainable machine-learning algorithms to differentiate bipolar disorder from major depressive disorder using self-reported symptoms, vital signs, and blood-based markers

Zhu, Tong; Liu, Xiaofei; Wang, Junren; Kou, Ran; Hu, Yao; Yuan, Minlan; Yuan, Cui; Luo, Lei; Zhang, Wei

doi:10.1016/j.cmpb.2023.107723

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…Moreover, most studies, like [ 21 , 25 ], employ simple ML to build predictive models for depression, and studies that employ DL, like [ 26 , 28 ], do not employ a parallel, multiple Evolutionary Algorithm-based optimisation scheme to optimise the model hyperparameters. Furthermore, most studies like [ 46 , 47 , 48 , 49 ] use explainability to develop population-level explanations of various mental health disorders, while our work produces personalised insights.…”

Section: Discussionmentioning

confidence: 99%

“…Studies such as [ 43 , 44 , 45 ] use explainability techniques on ML models to obtain insights into the model outputs. Moreover, recent works have begun exploring explainability in mental health settings [ 24 , 46 , 47 , 48 , 49 ]. However, the use of explainability has been limited to the extraction of the most influential model features/inputs using SHAP or LIME [ 50 ].…”

Section: Introductionmentioning

confidence: 99%

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Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

Chatterjee,

Mishra,

Sundram

et al. 2023

Sensors

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Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental health needs globally. Increasingly, passively acquired data from wearables are augmented with carefully selected active data from depressed individuals to develop Machine Learning (ML) models of depression based on mood scores. However, most ML models are black box in nature, and hence the outputs are not explainable. Depression is also multimodal, and the reasons for depression may vary significantly between individuals. Explainable and personalised models will thus be beneficial to clinicians to determine the main features that lead to a decline in the mood state of a depressed individual, thus enabling suitable personalised therapy. This is currently lacking. Therefore, this study presents a methodology for developing personalised and accurate Deep Learning (DL)-based predictive mood models for depression, along with novel methods for identifying the key facets that lead to the exacerbation of depressive symptoms. We illustrate our approach by using an existing multimodal dataset containing longitudinal Ecological Momentary Assessments of depression, lifestyle data from wearables and neurocognitive assessments for 14 mild to moderately depressed participants over one month. We develop classification- and regression-based DL models to predict participants’ current mood scores—a discrete score given to a participant based on the severity of their depressive symptoms. The models are trained inside eight different evolutionary-algorithm-based optimisation schemes that optimise the model parameters for a maximum predictive performance. A five-fold cross-validation scheme is used to verify the DL model’s predictive performance against 10 classical ML-based models, with a model error as low as 6% for some participants. We use the best model from the optimisation process to extract indicators, using SHAP, ALE and Anchors from explainable AI literature to explain why certain predictions are made and how they affect mood. These feature insights can assist health professionals in incorporating personalised interventions into a depressed individual’s treatment regimen.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

Chatterjee,

Mishra,

Sundram

et al. 2023

Sensors

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show abstract

“…However, a novel method called SHapley Additive exPlanations (SHAP) [ 18 ] is known for interpreting ML models and is based on game theory. Studies have shown that SHAP tools can effectively quantify and visually elucidate nonlinear associations and complex interactions among predictors in ML models [ 16 , 17 , 19 , 20 ]. However, the applicability of these tools to sequence-dependent models such as LSTM remains debatable [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG

Kawamura,

Sato,

Shimokawa

et al. 2024

Sensors

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Understanding the association between subjective emotional experiences and physiological signals is of practical and theoretical significance. Previous psychophysiological studies have shown a linear relationship between dynamic emotional valence experiences and facial electromyography (EMG) activities. However, whether and how subjective emotional valence dynamics relate to facial EMG changes nonlinearly remains unknown. To investigate this issue, we re-analyzed the data of two previous studies that measured dynamic valence ratings and facial EMG of the corrugator supercilii and zygomatic major muscles from 50 participants who viewed emotional film clips. We employed multilinear regression analyses and two nonlinear machine learning (ML) models: random forest and long short-term memory. In cross-validation, these ML models outperformed linear regression in terms of the mean squared error and correlation coefficient. Interpretation of the random forest model using the SHapley Additive exPlanation tool revealed nonlinear and interactive associations between several EMG features and subjective valence dynamics. These findings suggest that nonlinear ML models can better fit the relationship between subjective emotional valence dynamics and facial EMG than conventional linear models and highlight a nonlinear and complex relationship. The findings encourage emotion sensing using facial EMG and offer insight into the subjective–physiological association.

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A comprehensive review of explainable AI for disease diagnosis

Biswas

2024

Array

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Explainable machine-learning algorithms to differentiate bipolar disorder from major depressive disorder using self-reported symptoms, vital signs, and blood-based markers

Cited by 5 publications

References 44 publications

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG

A comprehensive review of explainable AI for disease diagnosis

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