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

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...

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Veröffentlicht in:Journal of clinical medicine Jg. 9; H. 3; S. 658
Hauptverfasser: Weng, Jun-Cheng, Lin, Tung-Yeh, Tsai, Yuan-Hsiung, Cheok, Man, Chang, Yi-Peng, Chen, Vincent
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 29.02.2020
MDPI
Schlagworte:
Algorithms
Anisotropy
Clinical medicine
Health care
Machine learning
Magnetic resonance imaging
Mental depression
Suicidal ideation
Suicide prevention
Suicides & suicide attempts
United Kingdom > UK
Taiwan
autoencoder
Suicidal ideation
machine learning
generalized q-sampling imaging
ISSN:2077-0383, 2077-0383
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Zusammenfassung: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.
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ISSN:2077-0383
2077-0383
DOI:10.3390/jcm9030658