@inproceedings{474299339a62409f8158688df6ff31b4,
title = "Is crowdsourcing patient-reported outcomes the future of evidence-based medicine?: A case study of back pain",
abstract = "Evidence is lacking for patient-reported effectiveness of treatments for most medical conditions and specifically for lower back pain. In this paper, we examined a consumer-based social network that collects patients{\textquoteright} treatment ratings as a potential source of evidence. Acknowledging the potential biases of this data set, we used propensity score matching and generalized linear regression to account for confounding variables. To evaluate validity, we compared results obtained by analyzing the patient reported data to results of evidence-based studies. Overall, there was agreement on the relationship between back pain and being obese. In addition, there was agreement about which treatments were effective or had no benefit. The patients{\textquoteright} ratings also point to new evidence that postural modification treatment is effective and that surgery is harmful to a large proportion of patients.",
author = "Mor Peleg and Leung, {Tiffany I.} and Manisha Desai and Michel Dumontier",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th Conference on Artificial Intelligence in Medicine, AIME 2017 ; Conference date: 21-06-2017 Through 24-06-2017",
year = "2017",
doi = "https://doi.org/10.1007/978-3-319-59758-4_27",
language = "American English",
isbn = "9783319597577",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "245--255",
editor = "{[surname]ten Teije}, Annette and Christian Popow and Lucia Sacchi and Holmes, {John H.}",
booktitle = "Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings",
address = "Germany",
}