Causal discovery from medical textual data. Academic Article uri icon

start page

  • 542

end page

  • 546

abstract

  • Medical records usually incorporate investigative reports, historical notes, patient encounters or discharge summaries as textual data. This study focused on learning causal relationships from intensive care unit (ICU) discharge summaries of 1611 patients. Identification of the causal factors of clinical conditions and outcomes can help us formulate better management, prevention and control strategies for the improvement of health care. For causal discovery we applied the Local Causal Discovery (LCD) algorithm, which uses the framework of causal Bayesian Networks to represent causal relationships among model variables. LCD takes as input a dataset and outputs causes of the form variable Y causally influences variable Z. Using the words that occur in the discharge summaries as attributes for input, LCD output 8 purported causal relationships. The relationships ranked as most probable subjectively appear to be most causally plausible.

date/time value

  • 2000

PubMed Identifier

  • 11079942

volume

number

keywords

  • Algorithms
  • Bayes Theorem
  • Causality
  • Humans
  • Information Storage and Retrieval
  • Intensive Care Units
  • Markov Chains
  • Medical Records
  • Patient Discharge