Abstract #1888

Predicting suicidal ideation from depressive patients using autoencoder machine-learning model with brain generalized q-sampling imaging

Jun-Cheng Weng^1,2,3, Tung-Yeh Lin¹, Man Teng Cheok¹, Yuan-Hsiung Tsai⁴, Yi-Peng Eve Chang⁵, and Vincent Chin-Hung Chen^3,6

¹Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan, ²Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, ³Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan, ⁴Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, Taiwan, ⁵Department of Counseling and Clinical Psychology, Columbia University, New York City, NY, United States, ⁶School of Medicine, Chang Gung University, Taoyuan, Taiwan

It is estimated that at least one million people die by suicide every year, showing the importance of suicide prevention and detection. An autoencoder and machine learning model was employed to predict people with suicidal ideation based on their brain structural imaging. 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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