Causal discovery from medical textual data.
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.
Mani
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Center for Biomedical Informatics, Intelligent Systems Program, University of Pittsburgh, USA. mani@cbmi.upmc.edu
Cooper
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GF
D000465:Algorithms; D001499:Bayes Theorem; D015984:Causality; D006801:Humans; D016247:Information Storage and Retrieval; D007362:Intensive Care Units; D008390:Markov Chains; D008499:Medical Records / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data