Systems and methods for computing with private healthcare data

A de-identification method comprising:

• receiving a plurality of data sets, wherein the plurality of data sets comprises:
• a first data set, wherein the first data set comprises a labeled data set for one or more entity types;
• and
• a second data set, wherein the training data set comprises an unlabeled data set for the one or more entity types;
• determining one machine-learning model from a plurality of machine-learning models for each of one or more entity types;
• fine-tuning the determined machine-learning model for each of the one or more entity types, wherein fine-tuning the determined machine-learning model comprises:
• creating a plurality of training data sets, wherein the plurality of training data sets comprises:
• a first training data set, wherein the first training data set comprises the first data set;
• and
• a second training data set, wherein the second training data set comprises the second data set;
• training the determined machine-learning model using the first training data set;
• validating the trained machine-learning model, wherein validating the trained machine learning model further comprises:
• generating a recall score for each entity type of the one or more entity types;
• comparing the recall score to a threshold for the recall score for each entity type of the one or more entity types;
• and
• updating the trained machine-learning model using the second training data set as a function of the validation;
• and
• obfuscating the second data set using the fine-tuned machine-learning model.