APD: An Autoencoder-based Prediction Model for Depression Diagnosis

Hyeseong Park, Myung Won Raymond Jung, Uran Oh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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%.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science, IRI 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages376-379
Number of pages4
ISBN (Electronic)9781665438759
DOIs
StatePublished - 2021
Event22nd IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2021 - Virtual, Online, United States
Duration: 10 Aug 202112 Aug 2021

Publication series

NameProceedings - 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science, IRI 2021

Conference

Conference22nd IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2021
Country/TerritoryUnited States
CityVirtual, Online
Period10/08/2112/08/21

Keywords

  • Autoencoder
  • Depression
  • patients' response
  • Psychological Assessment
  • Surveys

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