Dataset Information

Evaluating the state of the art in coreference resolution for electronic medical records.

ABSTRACT: The fifth i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records conducted a systematic review on resolution of noun phrase coreference in medical records. Informatics for Integrating Biology and the Bedside (i2b2) and the Veterans Affair (VA) Consortium for Healthcare Informatics Research (CHIR) partnered to organize the coreference challenge. They provided the research community with two corpora of medical records for the development and evaluation of the coreference resolution systems. These corpora contained various record types (ie, discharge summaries, pathology reports) from multiple institutions.The coreference challenge provided the community with two annotated ground truth corpora and evaluated systems on coreference resolution in two ways: first, it evaluated systems for their ability to identify mentions of concepts and to link together those mentions. Second, it evaluated the ability of the systems to link together ground truth mentions that refer to the same entity. Twenty teams representing 29 organizations and nine countries participated in the coreference challenge.The teams' system submissions showed that machine-learning and rule-based approaches worked best when augmented with external knowledge sources and coreference clues extracted from document structure. The systems performed better in coreference resolution when provided with ground truth mentions. Overall, the systems struggled in solving coreference resolution for cases that required domain knowledge.

SUBMITTER: Uzuner O

PROVIDER: S-EPMC3422835 | biostudies-literature | 2012 Sep-Oct

REPOSITORIES: biostudies-literature

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Evaluating the state of the art in coreference resolution for electronic medical records.

Uzuner Ozlem O Bodnari Andreea A Shen Shuying S Forbush Tyler T Pestian John J South Brett R BR

Journal of the American Medical Informatics Association : JAMIA 20120224 5

<h4>Background</h4>The fifth i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records conducted a systematic review on resolution of noun phrase coreference in medical records. Informatics for Integrating Biology and the Bedside (i2b2) and the Veterans Affair (VA) Consortium for Healthcare Informatics Research (CHIR) partnered to organize the coreference challenge. They provided the research community with two corpora of medical records for the development and evaluation o ...[more]

PMID: 22366294

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Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

Dataset Information

Evaluating the state of the art in coreference resolution for electronic medical records.

Publications

Evaluating the state of the art in coreference resolution for electronic medical records.

Similar Datasets

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