Doctors? attitudes toward specific medical conditions

This study uses machine learning and natural language processing tools to examine the language used by healthcare professionals on a global online forum. It contributes to an underdeveloped area of knowledge, that of physician attitudes toward their patients. Us-ing comments left by physicians on Reddit's "Medicine" subreddit (r/medicine), we test if the language from online discussions can reveal doctors' attitudes toward specific medi-cal conditions. We focus on a set of chronic conditions that usually are more stigmatized and compare them to ones well accepted by the medical community. We discovered that when comparing diseases with similar traits, doctors discussed some conditions with more negative attitudes. These results show bias does not occur only along the dimensions tra-ditionally analyzed in the economics literature of gender and race, but also along the di-mension of disease type. This is meaningful because the emotions associated with beliefs impact physicians' decision making, prescribing behavior, and quality of care. First, we run a binomial LASSO-logistic regression to compare a range of 21 diseases against myalgic en-cephalomyelitis/chronic fatigue syndrome (ME/CFS), depression, and the autoimmune dis-eases multiple sclerosis and rheumatoid arthritis. Next, we use dictionary methods to com-pare five more chronic diseases: Lyme disease, Ehlers-Danlos syndrome (EDS), Alzheimer's disease, osteoporosis, and lupus. The results show physicians discuss ME/CFS, depression, and Lyme disease with more negative language than the other diseases in the set. The results for ME/CFS included over four times more negative words than the results for de-pression. (c) 2022 The Author(s). Published by Elsevier B.V.This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ )