Health-AIM: An artificial intelligence approach for inference with clinical health datasets
Anjur, Vikram Sriram
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https://hdl.handle.net/2142/108322
Description
Title
Health-AIM: An artificial intelligence approach for inference with clinical health datasets
Author(s)
Anjur, Vikram Sriram
Issue Date
2020-05-11
Director of Research (if dissertation) or Advisor (if thesis)
Iyer, Ravishankar K
Arnold, Paul
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2020-08-27T00:51:26Z
Keyword(s)
data science, artificial intelligence, inference, classification, feature extraction, data imputation, machine learning, statistics, data preprocessing, probabilistic graphical models, clinical health prediction
Abstract
In the clinical health domain, artificial intelligence (AI) models need to use data to make reliable decisions regarding patient trajectories and treatments. Incorrect decisions can lead to strain on both caregiver and patient, and clinical datasets often have low volume, high dimensionality, and many missing values. This thesis presents Health-AIM, a Python platform designed to address these challenges by leveraging well-known AI techniques to make effective inferences using clinical health datasets. Health-AIM introduces a four-step workflow consisting of data preprocessing, feature extraction, inferencing, and results visualization, with comprehensive functionalities to handle a variety of scenarios in each step.
Health-AIM supports the usage of custom probabilistic graphical models and of classifiers from Python’s sklearn library. Naïve Bayes networks and Bayesian networks specified by edge lists can be dynamically constructed and trained using Health-AIM. Accepting labeled patient data and user-parameters as input, Health-AIM enables runtime selection of data imputation, feature selection, classification, and testing methods with a single function call. Thus, different inferences can be performed rapidly in succession through simple adjustment of parameters. A real-life case study on prediction of postoperative survival in patients with metastatic epidural spinal cord compression is used to illustrate the various functionalities of Health-AIM.
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