TY  - JOUR
PY  - 2020//
TI  - An autoencoder and machine learning model to predict suicidal ideation with brain structural imaging
JO  - Journal of clinical medicine
A1  - Weng, Jun-Cheng
A1  - Lin, Tung-Yeh
A1  - Tsai, Yuan-Hsiung
A1  - Cheok, Man Teng
A1  - Chang, Yi-Peng Eve
A1  - Chen, Vincent Chin-Hung
SP  - e658
EP  - e658
VL  - 9
IS  - 3
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2077-0383
UR  - http://dx.doi.org/10.3390/jcm9030658
ID  - ref1
ER  -