Natural Language Processing (NLP) systems often make use of machine learning techniques that are unfamiliar to end-users who are interested in analyzing clinical records. Although NLP has been widely used in extracting information from clinical text, current systems generally do not support model revision based on feedback from domain experts. We present a prototype tool that allows end users to visualize and review the outputs of an NLP system that extracts binary variables from clinical text. Our tool combines multiple visualizations to help the users understand these results and make any necessary corrections, thus forming a feedback loop and helping improve the accuracy of the NLP models. We have tested our prototype in a formative think-aloud user study with clinicians and researchers involved in colonoscopy research. Results from semi-structured interviews and a System Usability Scale (SUS) analysis show that the users are able to quickly start refining NLP models, despite having very little or no experience with machine learning. Observations from these sessions suggest revisions to the interface to better support review workflow and interpretation of results.
a lot is a bit colloquial. Say there has been a significant body of work, or many efforts have developed... There have been many efforts to develop user-centric tools for machine learning and NLP making it easier for the end users to build models. a bit awkward, but ok D'Avolio et. al. @cite_2 have described a prototype that combines several existing tools such as Knowtator @cite_9 for creating text annotations, and cTAKES @cite_14 for deriving NLP features, within a common user interface that can be used to configure the machine learning algorithms and export their results. Our present work complements this effort, focusing instead on facilitating expert review of NLP results and provision of feedback regarding the accuracy and completeness of details extracted from NLP data.
@cite_9 @cite_14 @cite_2
2126344780 2096252540 2149791639 2411480514
Objective Despite at least 40 years of promising empirical performance, very few clinical natural language processing (NLP) or information extraction systems currently contribute to medical science or care. The authors address this gap by reducing the need for custom software and rules development with a graphical user interface-driven, highly generalizable approach to concept-level retrieval. @PARASPLIT Materials and methods A ‘learn by example’ approach combines features derived from open-source NLP pipelines with open-source machine learning classifiers to automatically and iteratively evaluate top-performing configurations. The Fourth i2b2 VA Shared Task Challenge's concept extraction task provided the data sets and metrics used to evaluate performance. @PARASPLIT Results Top F-measure scores for each of the tasks were medical problems (0.83), treatments (0.82), and tests (0.83). Recall lagged precision in all experiments. Precision was near or above 0.90 in all tasks. @PARASPLIT Discussion With no customization for the tasks and less than 5 min of end-user time to configure and launch each experiment, the average F-measure was 0.83, one point behind the mean F-measure of the 22 entrants in the competition. Strong precision scores indicate the potential of applying the approach for more specific clinical information extraction tasks. There was not one best configuration, supporting an iterative approach to model creation. @PARASPLIT Conclusion Acceptable levels of performance can be achieved using fully automated and generalizable approaches to concept-level information extraction. The described implementation and related documentation is available for download. This issue of JAMIA focuses on natural language processing (NLP) techniques for clinical-text information extraction. Several articles are offshoots of the yearly ‘Informatics for Integrating Biology and the Bedside’ (i2b2) (http: www.i2b2.org) NLP shared-task challenge, introduced by ( see page 552 )1 and co-sponsored by the Veteran's Administration for the last 2 years. This shared task follows long-running challenge evaluations in other fields, such as the Message Understanding Conference (MUC) for information extraction,2 TREC3 for text information retrieval, and CASP4 for protein structure prediction. Shared tasks in the clinical domain are recent and include annual i2b2 Challenges that began in 2006, a challenge for multi-label classification of radiology reports sponsored by Cincinnati Children's Hospital in 2007,5 a 2011 Cincinnati Children's Hospital challenge on suicide notes,6 and the 2011 TREC information retrieval shared task involving retrieval of clinical cases from narrative records.7 @PARASPLIT Although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications for clinical text has been slow and lags behind progress in the general NLP domain. There are several barriers to NLP development in the clinical domain, and shared tasks like the i2b2 VA Challenge address some of these barriers. Nevertheless, many barriers remain and unless the community takes a more active role in developing novel approaches for addressing the barriers, advancement and innovation will continue to be slow. @PARASPLIT Historically, there have been substantial barriers to NLP development in the clinical domain. These barriers are not unique to the clinical domain: they also occur in the fields of software engineering and general NLP. @PARASPLIT ### Lack of access to shared data @PARASPLIT Because of concerns regarding patient privacy and worry about revealing unfavorable institutional practices, hospitals and clinics have been extremely reluctant to allow access to clinical data for researchers from outside … @PARASPLIT Correspondence to Dr Wendy W Chapman, Department of Biomedical Informatics, University of California San Diego, 9500 Gilman Dr, Bldg 2 #0728, La Jolla, California, USA; wwchapman at ucsd.edu Supervised approaches to NLP tasks rely on high-quality data annotations, which typically result from expensive manual labelling procedures. For some tasks, however, the subjectivity of human judgements might reduce the usefulness of the annotation for real-world applications. In Machine Translation (MT) Quality Estimation (QE), for instance, using humanannotated data to train a binary classifier that discriminates between good (useful for a post-editor) and bad translations is not trivial. Focusing on this binary task, we show that subjective human judgements can be effectively replaced with an automatic annotation procedure. To this aim, we compare binary classifiers trained on different data: the human-annotated dataset from the 7 th Workshop on Statistical Machine Translation (WMT-12), and an automatically labelled version of the same corpus. Our results show that human labels are less suitable for the task. Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by machine learned systems is the limited availability of human-annotated data. (2015) seek to solve this problem by creating over a million training examples by pairing CNN and Daily Mail news articles with their summarized bullet points, and show that a neural network can then be trained to give good performance on this task. In this paper, we conduct a thorough examination of this new reading comprehension task. Our primary aim is to understand what depth of language understanding is required to do well on this task. We approach this from one side by doing a careful hand-analysis of a small subset of the problems and from the other by showing that simple, carefully designed systems can obtain accuracies of 73.6 and 76.6 on these two datasets, exceeding current state-of-the-art results by 7-10 and approaching what we believe is the ceiling for performance on this task.