Maximum Credibility Voting (MCV) An Integrative Approach for Accurate Diagnosis of Major Depressive Disorder from Clinically Readily Available Data

Yu Shimizu, Junichiro Yoshimoto, Masahiro Takamura, Go Okada, Tomoya Matsumoto, Manabu Fuchikami, Satoshi Okada, Shigeru Morinobu, Yasumasa Okamoto, Shigeto Yamawaki, Kenji Doya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Diagnosis of Major Depressive Disorder (MDD) is currently a lengthy procedure, due to the low diagnostic accuracy of clinically readily available biomarkers. We integrate predictions from multiple datasets based on a credibility parameter defined on the probabilistic distributions of individual sparse prediction models. We demonstrate by means of structural and resting-state functional Magnetic Resonance Imaging and blood markers obtained from 62 treatment naive MDD patients (age 40.63±9.28, 36 female, HRSD 20.03±4.94) and 66 controls without mental disease history (age 35.52±12.91, 30 female), that our method called Maximum Credibility Voting (MCV) significantly increases diagnostic accuracy from about 65% average classification accuracy of individual biomarker models to 80% (accuracy after integration of the models). Classification results from different combinations of the available datasets validate the method's stability with respect to redundant or contradictory predictions. By definition, MCV is applicable to any desired data and compatible with missing values, ensuring continued improvement of diagnostic accuracy and patient comfort as new data acquisition methods and markers emerge.

Original languageEnglish
Title of host publication2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1023-1032
Number of pages10
ISBN (Electronic)9789881476883
Publication statusPublished - 07-12-2020
Externally publishedYes
Event2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
Duration: 07-12-202010-12-2020

Publication series

Name2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

Conference

Conference2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
Country/TerritoryNew Zealand
CityVirtual, Auckland
Period07-12-2010-12-20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Signal Processing
  • Decision Sciences (miscellaneous)
  • Instrumentation

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