TY - GEN
T1 - APD
T2 - 22nd IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2021
AU - Park, Hyeseong
AU - Raymond Jung, Myung Won
AU - Oh, Uran
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Depression is one of the most common mental health problems, which can lead to significant mental disorders and suicidal behavior. To diagnose depression levels, patients with depressive disorders are required to complete self-assessment questionnaires. However, many depressed patients are misdiagnosed in clinical practice due to patients' missing data. In this paper, we introduce, APD, a novel data-driven approach based on autoencoder to predict the missing responses accurately. Inspired by existing autoencoder-based recommender systems, our autoencoder is based on collaborative filtering, which estimates unobserved data by cooperation with other patients' responses. Experimental results show that the proposed autoencoder-based prediction system outperforms the averaging and the linear models. We demonstrate that this model can be used to predict patients' depression status with a low error of 2.85%.
AB - Depression is one of the most common mental health problems, which can lead to significant mental disorders and suicidal behavior. To diagnose depression levels, patients with depressive disorders are required to complete self-assessment questionnaires. However, many depressed patients are misdiagnosed in clinical practice due to patients' missing data. In this paper, we introduce, APD, a novel data-driven approach based on autoencoder to predict the missing responses accurately. Inspired by existing autoencoder-based recommender systems, our autoencoder is based on collaborative filtering, which estimates unobserved data by cooperation with other patients' responses. Experimental results show that the proposed autoencoder-based prediction system outperforms the averaging and the linear models. We demonstrate that this model can be used to predict patients' depression status with a low error of 2.85%.
KW - Autoencoder
KW - Depression
KW - Psychological Assessment
KW - Surveys
KW - patients' response
UR - http://www.scopus.com/inward/record.url?scp=85123454477&partnerID=8YFLogxK
U2 - 10.1109/IRI51335.2021.00058
DO - 10.1109/IRI51335.2021.00058
M3 - Conference contribution
AN - SCOPUS:85123454477
T3 - Proceedings - 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science, IRI 2021
SP - 376
EP - 379
BT - Proceedings - 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science, IRI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 August 2021 through 12 August 2021
ER -