Leveraging Natural language Processing and Machine Learning Techniques to find Frailty Deficits from Clinical Dataset

• Author / Creator

  Bazrafkan, Mehrnoosh

• Introduction Frailty is a syndrome that is often associated with aging. It
  can be identified through specific frailty scales or a comprehensive assessment
  by a healthcare provider. In Alberta, it appears that there are no specific
  billing or diagnostic codes for frailty. So, healthcare providers may use specific
  assessments or codes related to conditions such as muscle weakness or
  decreased physical activity to identify frailty. Purpose This project aims to
  leverage Natural Language Processing algorithms to extract frailty keywords
  from structured and Unstructured clinical datasets to identify frailty deficits
  and classify patients into frail and non-frail classes using Machine Learning
  algorithms. Methods The dataset included 450 patients over the age of 60,
  medical information related to diseases, and clinical frailty scales. We first
  clean medical notes using NLP techniques and removing negation terms, then
  extract keywords from clinical notes and structured datasets, and finally, we
  use resampling techniques to deal with imbalanced clinical datasets, and we
  feed these extracted keywords into machine learning classifiers to classify patients
  as frail or not frail. Results There are many different types of machine
  learning classifiers that have been used for this task, Random Forest and Decision
  Three with 0.95 performed better than LR, KNN, NB, SVM, and neural
  network models. Conclusion Natural Language Processing algorithms can
  effectively extract frailty keywords using Electronic Medical Record (EMR)
  notes. Moreover, comparing the results shows that using both structured and
  unstructured data gives better results than using only structured data.

• Subjects / Keywords

  • Medical notes
  • frailty deficits
  • Frailty prediction
  • Clinical Frailty Scale
  • Imbalanced dataset
  • resampling algorithms
  • Machine learning
  • classification
  • supervised algorithms
  • structured dataset
  • unstructured dataset
  • healthcare
  • clinical notes
  • oversampling algorithm
  • undersampling algorithms

• Graduation date

  Spring 2023

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-728d-2760

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.