Deep Learning for Cancer Symptoms Monitoring on the Basis of Electronic Health Record Unstructured Clinical Notes.

PURPOSE: Symptoms are vital outcomes for cancer clinical trials, observational research, and population-level surveillance. Patient-reported outcomes (PROs) are valuable for monitoring symptoms, yet there are many challenges to collecting PROs at scale. We sought to develop, test, and externally validate a deep learning model to extract symptoms from unstructured clinical notes in the electronic health record. METHODS: We randomly selected 1,225 outpatient progress notes from among patients treated at the Dana-Farber Cancer Institute between January 2016 and December 2019 and used 1,125 notes as our training/validation data set and 100 notes as our test data set. We evaluated the performance of 10 deep learning models for detecting 80 symptoms included in the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) framework. Model performance as compared with manual chart abstraction was assessed using standard metrics, and the highest performer was externally validated on a sample of 100 physician notes from a different clinical context. RESULTS: In our training and test data sets, 75 of the 80 candidate symptoms were identified. The ELECTRA-small model had the highest performance for symptom identification at the token level (ie, at the individual symptom level), with an F1 of 0.87 and a processing time of 3.95 seconds per note. For the 10 most common symptoms in the test data set, the F1 score ranged from 0.98 for anxious to 0.86 for fatigue. For external validation of the same symptoms, the note-level performance ranged from F1 = 0.97 for diarrhea and dizziness to F1 = 0.73 for swelling. CONCLUSION: Training a deep learning model to identify a wide range of electronic health record-documented symptoms relevant to cancer care is feasible. This approach could be used at the health system scale to complement to electronic PROs.

Consultation for total pain in high-risk obstetrics.

Palliative care (PC) consultation rarely takes place in the clinical setting of high-risk obstetrics, where 'total pain' may be undermanaged. Here, we present a case of a young woman carrying twins and hospitalised for acute abdominal pain. Workup for her pain revealed non-viable fetal tissue positioned in the uterine horn; the remaining fetus was viable. Initial attempts to control the patient's pain with strong parenteral opioids by the obstetrics team and the acute pain service failed. The PC service was consulted to assist. Applying a customary interdisciplinary approach in a novel PC clinical setting, the PC service was able to identify and attend to the patient's physical, psychosocial and spiritual pain, resulting in an overall decrease in reported pain scores, decreased opioid requirement and a plan for preservation of the viable fetus.