首页|Studies from Duke University Medical Center Have Provided New Information about Machine Learning (Machine Learning Analysis of Online Patient Questions Regardin g Breast Reconstruction)
Studies from Duke University Medical Center Have Provided New Information about Machine Learning (Machine Learning Analysis of Online Patient Questions Regardin g Breast Reconstruction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting originating from Durham, North Carolina, by NewsRx correspondents, research stated, “Social media has become a dominant educational resource for breast reconstruction patients. Rather than passively c onsuming information, patients interact di- rectly with other users and healthca re professionals.” Our news editors obtained a quote from the research from Duke University Medical Center, “While online information for breast re- construction has been analyzed previously, a robust analysis of patient questions on online forums has not bee n conducted. In this study, the authors used a machine learning approach to anal yze and categorize online patient questions regarding breast reconstruction. Rea lself.com was accessed and questions pertaining to breast reconstruction were ex tracted. Data collected included the date of question, poster’s location, questi on header, question text, and available tags. Questions were analyzed and catego rized by two in- dependent reviewers. 522 preoperative questions were analyzed. Geographic analysis is displayed in Figure 1. Questions were often asked in the pre -mastectomy period (38.3%); however, patients with tissue expan ders currently in place made up 28.5%. Questions were often related to re- constructive methods (23.2%), implant selection (19.5% ), and tissue expander concerns (16.7%). Questions asked in the pos t -lumpectomy period were significantly more likely to be related to insurance/c ost and reconstructive candidacy (p <0.01). The ‘Top 6 “ p atient questions were determined by machine learning analysis, and the most comm on of which was ‘Can I get good results going direct to implant after mastectomy ?’ Conclusions: Analysis of online questions provides valuable insights and may help inform our educational approach toward our breast reconstruction patients. Our findings suggest that questions are common throughout the reconstructive pro cess and do not end after the initial consultation.”
DurhamNorth CarolinaUnited StatesN orth and Central AmericaCyborgsEmerging TechnologiesMachine LearningDuke University Medical Center
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