Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals.

Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals. AMIA Annu Symp Proc. 2018;2018:545-554 Authors: Hassanzadeh H, Kholghi M, Nguyen A, Chu K Abstract Reviewing radiology reports in emergency departments is an essential but laborious task. Timely follow-up of patients with abnormal cases in their radiology reports may dramatically affect the patient's outcome, especially if they have been discharged with a different initial diagnosis. Machine learning approaches have been devised to expedite the process and detect the cases that demand instant follow up. However, these approaches require a large amount of labeled data to train reliable predictive models. Preparing such a large dataset, which needs to be manually annotated by health professionals, is costly and time-consuming. This paper investigates a semi-supervised transfer learning framework for radiology report classification across three hospitals. The main goal is to leverage both vastly available clinical unlabeled data and already learned knowledge in order to improve a learning model where limited labeled data is available. Our experimental findings show that (1) convolutional neural networks (CNNs), while being independent of any problem-specific feature engineering, achieve significantly higher effectiveness compared to conventional supervised learning approaches, (2) leveraging unlabeled data in training a CNN-based classifier reduces the d...
Source: AMIA Annual Symposium Proceedings - Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research