Narratives of electronic health records (EHRs) contain useful information that is difficult to automatically extract, index, search, or interpret. Clinical natural language processing (NLP) technologies for automatic extraction, indexing, searching, and interpretation of EHRs are in development;however, due to privacy concerns related to EHRs, such technologies are usually developed by teams that have privileged access to EHRs in a specific institution. Technologies that are tailored to a specific set of data from a given institution generate inspiring results on that data;however, they can fail to generalize to similar data from other institutions and even other departments from the same institution. Therefore, learning from these technologies and building on them becomes difficult. In order to improve NLP in EHRs, there is need for head-to-head comparison of approaches that can address a given task on the same data set. Shared-tasks provide one way of conducting systematic head-to- head comparisons. This proposal describes a series of shared-task challenges and conferences, spread over a five year period, that promote the development and evaluation of cutting edge clinical NLP systems by distributing de-identified EHRs to the broad research community, under data use agreements, so that: * the state-of-the-art in clinical NLP technologies can be identified and advanced, * a set of technologies that enable the use of the information contained in EHR narratives becomes available, and * the information from EHR narratives can be made more accessible, for example, for clinical and medical research. The scientific activities supporting the organization of the shared-task challenges are sponsored in part by Informatics for Integrating Biology and the Bedside (i2b2), grant number U54-LM008748, PI: Kohane. This proposal aims to organize a series of workshops, conference proceedings, and journal special issues that will accompany the shared-task challenges in order to disseminate the knowledge generated by the challenges.
this proposal will address two main challenges related to the use of clinical narratives for research: availability of clinical records for research and identification of the state of the art in clinical natural language processing (NLP) technologies so that we can push the state of the art forward and so that future work can build on the past. Progress in clinical NLP will improve access to electronic health records for research, and for clinical applications, benefiting healthcare and public health.
|Liu, Zengjian; Tang, Buzhou; Wang, Xiaolong et al. (2017) De-identification of clinical notes via recurrent neural network and conditional random field. J Biomed Inform 75S:S34-S42|
|Goodwin, Travis R; Maldonado, Ramon; Harabagiu, Sanda M (2017) Automatic recognition of symptom severity from psychiatric evaluation records. J Biomed Inform 75S:S71-S84|
|Dehghan, Azad; Kovacevic, Aleksandar; Karystianis, George et al. (2017) Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes. J Biomed Inform 75S:S28-S33|
|Uzuner, Özlem; Stubbs, Amber; Filannino, Michele (2017) A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry. J Biomed Inform 75S:S1-S3|
|Rios, Anthony; Kavuluru, Ramakanth (2017) Ordinal convolutional neural networks for predicting RDoC positive valence psychiatric symptom severity scores. J Biomed Inform 75S:S85-S93|
|Lee, Hee-Jin; Wu, Yonghui; Zhang, Yaoyun et al. (2017) A hybrid approach to automatic de-identification of psychiatric notes. J Biomed Inform 75S:S19-S27|
|Stubbs, Amber; Filannino, Michele; Uzuner, Özlem (2017) De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1. J Biomed Inform 75S:S4-S18|
|Liu, Yang; Gu, Yu; Nguyen, John Chu et al. (2017) Symptom severity classification with gradient tree boosting. J Biomed Inform 75S:S105-S111|
|Clark, Cheryl; Wellner, Ben; Davis, Rachel et al. (2017) Automatic classification of RDoC positive valence severity with a neural network. J Biomed Inform 75S:S120-S128|
|Posada, Jose D; Barda, Amie J; Shi, Lingyun et al. (2017) Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records. J Biomed Inform 75S:S94-S104|
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