  Post-market medical device surveillance is a challenge facing manufacturers,
regulatory agencies, and health care providers. Electronic health records are
valuable sources of real world evidence to assess device safety and track
device-related patient outcomes over time. However, distilling this evidence
remains challenging, as information is fractured across clinical notes and
structured records. Modern machine learning methods for machine reading promise
to unlock increasingly complex information from text, but face barriers due to
their reliance on large and expensive hand-labeled training sets. To address
these challenges, we developed and validated state-of-the-art deep learning
methods that identify patient outcomes from clinical notes without requiring
hand-labeled training data. Using hip replacements as a test case, our methods
accurately extracted implant details and reports of complications and pain from
electronic health records with up to 96.3% precision, 98.5% recall, and 97.4%
F1, improved classification performance by 12.7- 53.0% over rule-based methods,
and detected over 6 times as many complication events compared to using
structured data alone. Using these events to assess complication-free
survivorship of different implant systems, we found significant variation
between implants, including for risk of revision surgery, which could not be
detected using coded data alone. Patients with revision surgeries had more hip
pain mentions in the post-hip replacement, pre-revision period compared to
patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs.
3.23; t = 5.14; p < 0.001). Some implant models were associated with higher or
lower rates of hip pain mentions. Our methods complement existing surveillance
mechanisms by requiring orders of magnitude less hand-labeled training data,
offering a scalable solution for national medical device surveillance.
