rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Prediction of sepsis using artificial neural network and optimal brain surgeon
  name:
    name part: Rahman, Mohammed Ashikur
    role:
      role term: creator
    role:
      role term: author
  name:
    name part: Adamu Abubakar Ibrahim
    role:
      role term: degree supervisor
  name:
    name part: Afidalina Tumia
    role:
      role term: degree supervisor
  name:
    name part: Roslina Othman
    role:
      role term: degree supervisor
  name:
    name part: Azrina Md. Ralib
    role:
      role term: degree supervisor
  genre: Theses, IIUM local
  origin info:
    place:
      place term: my
    date issued: 2021
  language:
    language term: eng
  physical description:
    extent: xiv, 187 leaves :| illustrations ; 30 cm.
  abstract: Sepsis is a severe threat to global health. Approximately, the mortality rate of sepsis in the Intensive Care Unit (ICU) is 42%. In 2017, 11 million sepsis-related deaths were reported among 49 million cases, 20% of all-cause of deaths worldwide. Detection and prediction of sepsis in earlier stage allow patients to get earlier care and better results, but sepsis is often unknown until the late stages. Substantial bodies of research studies on sepsis prediction have mainly focused on rules-based severity scores, which are transparent and straightforward; unfortunately, they have imperfect sensitivity and specificity in identifying and predicting sepsis. Typically, various sepsis predictions approach that would allow for predicting in an earlier stage, which can reduce the mortality rate and treatment cost. So, machine learning algorithms can be a choice for predicting sepsis. Therefore, this current thesis identified the features influencing early sepsis prediction and examining the features impelling the clinical severity scores used for the prediction of sepsis. The thesis also developed a hybrid optimal brain surgeon algorithm for sepsis prediction and tested the proposed algorithm's accuracy. The research methodology adopted for this thesis is an experimental simulation. The datasets used in this research were adopted from MIMIC-III, which comes with vast electronic health records. A systematic literature review was performed, and significant features of the MIMIC-III dataset for sepsis prediction were obtained by applying Automatic Backward Elimination (ABE) algorithm, Generalized Linear Model (GLM), and Correlation Matrix (CM). After that, the research built a hybrid-sepsis prediction model using machine learning techniques to train and test with selected features for model selection. Then Optimal Brain Surgeon (OBS) algorithm was used to simplify the architecture of the neural network for making an explainable deep learning-based sepsis prediction model. This …
  note: by Mohammed Ashikur Rahman
  note: Abstracts in English and Arabic.
  note: "A thesis submitted in fulfilment of the requirement for the degree of Doctor of Philosophy in Education." --On title page.
  note: Thesis (Ph.D)--International Islamic University Malaysia, 2021.
  note: Includes bibliographical references (leaves 143-162).
  subject:
    geographic code: a-my---
  subject:
    topic: Medical informatics
  subject:
    topic: Artificial intelligence
    topic: Medical applications
  subject:
    topic: Brain
    topic: Computer interfaces
  classification: R859.7.A78
  identifier: http://studentrepo.iium.edu.my/handle/123456789/10991
  record info:
    record content source: UIAM
    record creation date: 220421
    language of cataloging:
      language term: eng