Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation

• Published in: JAMIA open Vol. 3; no. 3; pp. 431 - 438
• Main Authors: Odisho, Anobel Y, Park, Briton, Altieri, Nicholas, DeNero, John, Cooperberg, Matthew R, Carroll, Peter R, Yu, Bin
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01-10-2020
• Subjects: Computational linguistics; Health aspects; Language processing; Machine learning; Medical research; Medicine, Experimental; Natural language interfaces; Neural networks; Prostate cancer; Research and Applications; California; information extraction; prostate cancer; cancer; pathology; natural language processing; machine learning
• ISSN: 2574-2531; 2574-2531
• DOI: 10.1093/jamiaopen/ooaa029

Cancer is a leading cause of death, but much of the diagnostic information is stored as unstructured data in pathology reports. We aim to improve uncertainty estimates of machine learning-based pathology parsers and evaluate performance in low data settings.
Our data comes from the Urologic Outcomes Database at UCSF which includes 3232 annotated prostate cancer pathology reports from 2001 to 2018. We approach 17 separate information extraction tasks, involving a wide range of pathologic features. To handle the diverse range of fields, we required 2 statistical models, a document classification method for pathologic features with a small set of possible values and a token extraction method for pathologic features with a large set of values. For each model, we used isotonic calibration to improve the model's estimates of its likelihood of being correct.
Our best document classifier method, a convolutional neural network, achieves a weighted F1 score of 0.97 averaged over 12 fields and our best extraction method achieves an accuracy of 0.93 averaged over 5 fields. The performance saturates as a function of dataset size with as few as 128 data points. Furthermore, while our document classifier methods have reliable uncertainty estimates, our extraction-based methods do not, but after isotonic calibration, expected calibration error drops to below 0.03 for all extraction fields.
We find that when applying machine learning to pathology parsing, large datasets may not always be needed, and that calibration methods can improve the reliability of uncertainty estimates.