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Identifying Women at Risk for Polycystic Ovary Syndrome Using a Mobile Health App: Virtual Tool Functionality Assessment.

Erika Marie RodriguezDaniel ThomasAnna DruetMarija Vlajic-WheelerKevin James LaneShruthi Mahalingaiah
Published in: JMIR formative research (2020)
The first iteration of the feature outperformed the second and better predicted the probability of PCOS. Although further research is needed with a more robust sample size, this pilot study indicates the potential value for developing a screening tool to prompt high-risk subjects to seek evaluation by a medical professional.
Keyphrases
  • polycystic ovary syndrome
  • insulin resistance
  • healthcare
  • machine learning
  • deep learning
  • human health
  • adipose tissue
  • type diabetes