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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Conference (R13)
Project #
4R13LM011411-05
Application #
9130882
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2012-09-01
Project End
2017-08-31
Budget Start
2016-09-01
Budget End
2017-08-31
Support Year
5
Fiscal Year
2016
Total Cost
$20,000
Indirect Cost
Name
State University of New York at Albany
Department
Type
Schools of Arts and Sciences
DUNS #
152652822
City
Albany
State
NY
Country
United States
Zip Code
12222
Karystianis, George; Nevado, Alejo J; Kim, Chi-Hun et al. (2018) Automatic mining of symptom severity from psychiatric evaluation notes. Int J Methods Psychiatr Res 27:
Singh, Vivek Kumar; Shrivastava, Utkarsh; Bouayad, Lina et al. (2018) Machine learning for psychiatric patient triaging: an investigation of cascading classifiers. J Am Med Inform Assoc 25:1481-1487
Zhang, Yaoyun; Zhang, Olivia; Wu, Yonghui et al. (2017) Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge. J Biomed Inform 75S:S129-S137
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
Filannino, Michele; Stubbs, Amber; Uzuner, Özlem (2017) Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2. J Biomed Inform 75S:S62-S70
Dai, Hong-Jie; Su, Emily Chia-Yu; Uddin, Mohy et al. (2017) Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients. J Biomed Inform 75S:S149-S159
Tran, Tung; Kavuluru, Ramakanth (2017) Predicting mental conditions based on ""history of present illness"" in psychiatric notes with deep neural networks. J Biomed Inform 75S:S138-S148
Jiang, Zhipeng; Zhao, Chao; He, Bin et al. (2017) De-identification of medical records using conditional random fields and long short-term memory networks. J Biomed Inform 75S:S43-S53
Scheurwegs, Elyne; Sushil, Madhumita; Tulkens, Stéphan et al. (2017) Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports. J Biomed Inform 75S:S112-S119
Bui, Duy Duc An; Wyatt, Mathew; Cimino, James J (2017) The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge. J Biomed Inform 75S:S54-S61

Showing the most recent 10 out of 77 publications