Relationship Inference From The EHR (RIFTEHR) is an automated algorithm for identifying relatedness between patients in an institution's Electronic Health Records.
Original Authors: Fernanda Polubriaginof and Nicholas Tatonetti
This Version: Farhad Ghamsari
http://riftehr.tatonettilab.org
Remember to always respect patient privacy.
- Fully Python, no dependencies on SQL or Julia
- Much, much faster, thanks to vectorization of functions
Patient Demographics Table is a comma delimited file with the following headers. Each of these values corresponds to the patient:
- MRN, FirstName, LastName, PhoneNumber, Zipcode
Emergency Contacts Table is a comma delimited file with the following headers. MRN_1 corresponds to the MRN of the patient. (It is the link to the Patient Demographics Table.) The rest of the values correspond to the Patient's Emergency Contact. EC_Relationship refers to the relationship between Patient and EC. (If EC_Relationship is Parent, then the EC is the Patient's Parent.)
- MRN_1, EC_LastName, EC_FirstName, EC_PhoneNumber, EC_Zipcode, EC_Relationship
- Go to Step 0 >
preprocess.py>process_phones():- Remove any additional phone numbers that are recurrent in your data set. For example, our team had to remove the Northwestern University main line as it was a common placeholder for emergency contact's phone numbers.
- See
relation_map.csv. The input_relation column contains emergency contact relationships as they appear in your dataset, and the output_relations column is what they should map to, as required by the RIFTEHR program. - In Step 1 >
match_in_batches.py>find_matches()I felt that searching by a single data element for a possible match was too nonspecific, and opted to leave it commented out. You may experiment with it by uncommenting the section.
Should you have any questions, comments, suggestions, please don't hesitate to reach out: