Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection.

Journal: PloS one
Published Date: Jan 1, 2017

Abstract

OBJECTIVES: Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models.

Authors

  • Ghulam Mujtaba
    Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia; Department of Computer Science, Sukkur Institute of Business Administration, Sukkur, Pakistan.
  • Liyana Shuib
    Department of Information System, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Ram Gopal Raj
    Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia. Electronic address: ramdr@um.edu.my.
  • Retnagowri Rajandram
    Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
  • Khairunisa Shaikh
    Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Department of Community Medicine, Shaheed Mohtarma Benazir Bhutto Medical University, Larkana, Pakistan.
  • Mohammed Ali Al-Garadi
    Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.

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