  Privacy is a major concern in sharing human subject data to researchers for
secondary analyses. A simple binary consent (opt-in or not) may significantly
reduce the amount of sharable data, since many patients might only be concerned
about a few sensitive medical conditions rather than the entire medical
records. We propose event-level privacy protection, and develop a feature
ablation method to protect event-level privacy in electronic medical records.
Using a list of 13 sensitive diagnoses, we evaluate the feasibility and the
efficacy of the proposed method. As feature ablation progresses, the
identifiability of a sensitive medical condition decreases with varying speeds
on different diseases. We find that these sensitive diagnoses can be divided
into 3 categories: (1) 5 diseases have fast declining identifiability (AUC
below 0.6 with less than 400 features excluded); (2) 7 diseases with
progressively declining identifiability (AUC below 0.7 with between 200 and 700
features excluded); and (3) 1 disease with slowly declining identifiability
(AUC above 0.7 with 1000 features excluded). The fact that the majority (12 out
of 13) of the sensitive diseases fall into the first two categories suggests
the potential of the proposed feature ablation method as a solution for
event-level record privacy protection.
