Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision

• URL: http://arxiv.org/abs/2203.10442v1
• Date: Sun, 20 Mar 2022 03:42:03 GMT
• Title: Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision
• Authors: Sam Preston, Mu Wei, Rajesh Rao, Robert Tinn, Naoto Usuyama, Michael Lucas, Roshanthi Weerasinghe, Soohee Lee, Brian Piening, Paul Tittel, Naveen Valluri, Tristan Naumann, Carlo Bifulco, Hoifung Poon
• Abstract summary: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Traditional rule-based systems are vulnerable to the prevalent linguistic variations and ambiguities in clinical text. We propose leveraging patient-level supervision from medical registries, which are often readily available and capture key patient information.
• Score: 10.929271646369887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and time-consuming. Developing natural language processing (NLP) methods for structuring RWD is thus essential for scaling real-world evidence generation. Materials and Methods: Traditional rule-based systems are vulnerable to the prevalent linguistic variations and ambiguities in clinical text, and prior applications of machine-learning methods typically require sentence-level or report-level labeled examples that are hard to produce at scale. We propose leveraging patient-level supervision from medical registries, which are often readily available and capture key patient information, for general RWD applications. To combat the lack of sentence-level or report-level annotations, we explore advanced deep-learning methods by combining domain-specific pretraining, recurrent neural networks, and hierarchical attention. Results: We conduct an extensive study on 135,107 patients from the cancer registry of a large integrated delivery network (IDN) comprising healthcare systems in five western US states. Our deep learning methods attain test AUROC of 94-99% for key tumor attributes and comparable performance on held-out data from separate health systems and states. Discussion and Conclusion: Ablation results demonstrate clear superiority of these advanced deep-learning methods over prior approaches. Error analysis shows that our NLP system sometimes even corrects errors in registrar labels. We also conduct a preliminary investigation in accelerating registry curation and general RWD structuring via assisted curation for over 1.2 million cancer patients in this healthcare network.

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