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’ 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’ ratings also point to new evidence that postural modification treatment is effective and that surgery is harmful to a large proportion of patients.
|Title of host publication||Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings|
|Editors||Annette [surname]ten Teije, Christian Popow, Lucia Sacchi, John H. Holmes|
|Number of pages||11|
|State||Published - 2017|
|Event||16th Conference on Artificial Intelligence in Medicine, AIME 2017 - Vienna, Austria|
Duration: 21 Jun 2017 → 24 Jun 2017
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||16th Conference on Artificial Intelligence in Medicine, AIME 2017|
|Period||21/06/17 → 24/06/17|
Bibliographical notePublisher Copyright:
© Springer International Publishing AG 2017.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science (all)