Development of an algorithm using natural language processing to identify metastatic breast cancer patients from clinical notes

This study was conducted to determine if contextual understanding of metastatatic status can be extracted automatically from physician notes to identify patients with metastatic breast cancer (MBC). Contextual understanding of the notes is important to resolve issues such as a) local vs distal metastasis b) statements involving family history of metastasis or physician instructing the patient to look for certain signs of metastasis c) text indicating suspicion of metastasis or absence of metastasis d) indirect utterances, e.g. cancer has spread to the bone. e) corrections to previous findings. We used a set of breast cancer patients from ConcertAI real world oncology dataset that includes data from CancerLinQ Discovery to build & validate a set of NLP algorithms.This study showed that metastatic status & site of metastasis can be reliably extracted automatically from clinical notes using deep learning techniques. This information will be valuable for clinical trial matching, outcomes research and other applications.

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