Sharing sensitive texts for scientific purposes requires appropriate techniques to protect the privacy of patients and healthcare personnel. Anonymizing textual data is particularly challenging due to the presence of diverse unstructured direct and indirect identifiers. To mitigate the risk of re-identification, this work introduces a schema of nine categories of indirect identifiers designed to account for different potential adversaries, including acquaintances, family members and medical staff. Using this schema, we annotate 100 MIMIC-III discharge summaries and propose baseline models for identifying indirect identifiers. We will release the annotation guidelines, annotation spans (6,199 annotations in total) and the corresponding MIMIC-III document IDs to support further research in this area.

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Building a machine learning model involves a lot of steps - these steps are not limited to objective guidelines and require a more elaborative approach and depth based on the complexity of the business problem. The business problem can be solved in multiple ways - you need to decide whether the machine learning solution is really needed or it can be solved with a simple heuristic? Is there already a solution that is currently serving the business problem? If so, you need to do a thorough analysis and understand its limitations and seek the machine learning solution that can best overcome them. The next step should be to compare the two solutions - does the proposed machine learning solution also come with its limitations.