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Open AccessArticle

An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging

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Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 33302, Taiwan
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Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan
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Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
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School of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan 33302, Taiwan
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Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
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Department of Counseling and Clinical Psychology, Columbia University, New York City, NY 10027, USA
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(3), 658; https://doi.org/10.3390/jcm9030658
Received: 16 January 2020 / Revised: 18 February 2020 / Accepted: 26 February 2020 / Published: 29 February 2020
(This article belongs to the Section Psychiatry)
It is estimated that at least one million people die by suicide every year, showing the importance of suicide prevention and detection. In this study, an autoencoder and machine learning model was employed to predict people with suicidal ideation based on their structural brain imaging. The subjects in our generalized q-sampling imaging (GQI) dataset consisted of three groups: 41 depressive patients with suicidal ideation (SI), 54 depressive patients without suicidal thoughts (NS), and 58 healthy controls (HC). In the GQI dataset, indices of generalized fractional anisotropy (GFA), isotropic values of the orientation distribution function (ISO), and normalized quantitative anisotropy (NQA) were separately trained in different machine learning models. A convolutional neural network (CNN)-based autoencoder model, the supervised machine learning algorithm extreme gradient boosting (XGB), and logistic regression (LR) were used to discriminate SI subjects from NS and HC subjects. After five-fold cross validation, separate data were tested to obtain the accuracy, sensitivity, specificity, and area under the curve of each result. Our results showed that the best pattern of structure across multiple brain locations can classify suicidal ideates from NS and HC with a prediction accuracy of 85%, a specificity of 100% and a sensitivity of 75%. The algorithms developed here might provide an objective tool to help identify suicidal ideation risk among depressed patients alongside clinical assessment. View Full-Text
Keywords: suicidal ideation; autoencoder; machine learning; generalized q-sampling imaging suicidal ideation; autoencoder; machine learning; generalized q-sampling imaging
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Weng, J.-C.; Lin, T.-Y.; Tsai, Y.-H.; Cheok, M.T.; Chang, Y.-P.E.; Chen, V. .-H. An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging. J. Clin. Med. 2020, 9, 658.

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