The increasing availability of electronic health records (EHRs) creates opportunities for automated extraction of information from clinical text. We hypothesized that natural language processing (NLP) could substantially reduce the burden of manual abstraction in studies examining outcomes, like cancer recurrence, that are documented in unstructured clinical text, such as progress notes, radiology reports, and pathology reports. We developed an NLP-based system using open-source software to process electronic clinical notes from 1995 to 2012 for women with early-stage incident breast cancers to identify whether and when recurrences were diagnosed. We developed and evaluated the system using clinical notes from 1,472 patients receiving EHR-documented care in an integrated health care system in the Pacific Northwest. A separate study provided the patient-level reference standard for recurrence status and date. The NLP-based system correctly identified 92% of recurrences and estimated diagnosis dates within 30 days for 88% of these. Specificity was 96%. The NLP-based system overlooked 5 of 65 recurrences, 4 because electronic documents were unavailable. The NLP-based system identified 5 other recurrences incorrectly classified as nonrecurrent in the reference standard. If used in similar cohorts, NLP could reduce by 90% the number of EHR charts abstracted to identify confirmed breast cancer recurrence cases at a rate comparable to traditional abstraction.

A review of published work in clinical natural language processing (NLP) may suggest that the negation detection task has been “solved.” This work proposes that an optimizable solution does not equal a generalizable solution. We introduce a new machine learning-based Polarity Module for detecting negation in clinical text, and extensively compare its performance across domains. Using four manually annotated corpora of clinical text, we show that negation detection performance suffers when there is no in-domain development (for manual methods) or training data (for machine learning-based methods). Various factors (e.g., annotation guidelines, named entity characteristics, the amount of data, and lexical and syntactic context) play a role in making generalizability difficult, but none completely explains the phenomenon. Furthermore, generalizability remains challenging because it is unclear whether to use a single source for accurate data, combine all sources into a single model, or apply domain adaptation methods. The most reliable means to improve negation detection is to manually annotate in-domain training data (or, perhaps, manually modify rules); this is a strategy for optimizing performance, rather than generalizing it. These results suggest a direction for future work in domain-adaptive and task-adaptive methods for clinical NLP.

The MedXN system (http://sourceforge.net/projects/ohnlp/files/MedXN/) was able to extract comprehensive medication information with high accuracy and demonstrated good normalization capability to RxCUI as long as explicit evidence existed. More sophisticated inference rules might result in further improvements to specific RxCUI assignments for incomplete medication descriptions.

BackgroundExtracting medication information from clinical records has many potential applications, and recently published research, systems, and competitions reflect an interest therein. Much of the early extraction work involved rules and lexicons, but more recently machine learning has been applied to the task.MethodsWe present a hybrid system consisting of two parts. The first part, field detection, uses a cascade of statistical classifiers to identify medication-related named entities. The second part uses simple heuristics to link those entities into medication events.ResultsThe system achieved performance that is comparable to other approaches to the same task. This performance is further improved by adding features that reference external medication name lists.ConclusionsThis study demonstrates that our hybrid approach outperforms purely statistical or rule-based systems. The study also shows that a cascade of classifiers works better than a single classifier in extracting medication information. The system is available as is upon request from the first author.