Natural Language Processing Approaches for Early Detection of Cancer Recurrence in Oncology Patients

The customer, one of the largest healthcare providers in the United States, wanted an efficient solution to flag cancer recurrence as it occurs in oncology patients. To improve their oncology health care, it was necessary that clinical identification be tailored to patients’ unique treatment profile and expected trajectory.

Solution

Quantiphi created a predictive classification model capable of tagging patient encounters as related or unrelated to cancer recurrence for oncology patients. Included in this model were both structured and unstructured Electronic Health Record (EHR) data such as the patients’ medical profiles, physician notes, pathology reports, lab reports, etc. to flag the patient feature associated with tagging the encounter as a recurrence.

A scalable data aggregation and augmentation pipeline was developed on Google’s BigQuery and Dataproc, as well as a patient timeline to represent when features of the dataset occurred and the time of occurrence relative to cancer recurrence. Finally, Natural Language Processing techniques were incorporated, such as keyword search and TF-IDF, to structure the text data for use within the predictive model.

Result

Aggregates data to establish a representation of patient history and flag recurrence as it occurs
Automated tagging of patient encounters for cancer registry abstraction
Helps physicians deliver life-saving assistance and targeted treatment to high-risk cancer patients