ArticleJournal of affective disorders2020
What do patients learn about psychotropic medications on the web? A natural language processing study.
Article in Journal of affective disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Sentiment analysis in medication adherence: using ruled-based and artificial intelligence-driven algorithms to understand patient medication experiences.International journal of clinical pharmacy · 2025Article
- Sentiment Analysis of Online Reviews for Selective Serotonin Reuptake Inhibitors and Serotonin-Norepinephrine Reuptake Inhibitors.Pharmacy (Basel, Switzerland) · 2021Article
- Distribution of agitation and related symptoms among hospitalized patients using a scalable natural language processing method.General hospital psychiatryArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundLow rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information about common psychiatric medications in terms of positive and negative sentiment.
methodsWe applied a natural language processing tool to score the sentiment expressed in web search results for 51 psychotropic medications across 3 drug classes (antidepressants, antipsychotics, and mood stabilizers), as a means of seeing if articles referencing these medications were generally positive or generally negative in tone. We compared between medications of the same class, and across medication classes.
resultsAcross 12,733 web search results, significant within-class differences in positive (antidepressants: F(24,2682) = 2.97, p < 0.001; antipsychotics: F(16,4029) = 3.25, p < 0.001; mood stabilizers: F(8,2371) = 6.88, p < 0.001) and negative sentiment (antidepressants: F(24,6282) = 11.17, p < 0.001; antipsychotics: F(16, 4029) = 12.13, p < 0.001; mood stabilizers: F(8, 2371) = 13.28, p < 0.001) were identified. Among these were significantly greater negative sentiment for the antidepressants sertraline, duloxetine, venlafaxine, and paroxetine, and for the antipsychotics, quetiapine and risperidone. Conversely, lithium preparations and valproate exhibited less negative sentiment than other mood stabilizing medications. LIMITATIONS: While these results provide a novel means of comparing medications, the present analyses cannot be linked to individual patient consumption of this information, or to its influence on their future clinical interactions.
conclusionsOverall, a subset of psychotropic medications were associated with significantly more negative sentiment. Characterizing these differences may allow clinicians to anticipate patient willingness to initiate or continue medications.
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