Customization of medical report data

Cross-sectional relatedness between sentences in breast radiology reports: development of an SVM classifier and evaluation against annotations of five breast radiologists

Introduce the notion of cross-sectional relatedness as an informational dependence relation between sentences in the conclusion section of a breast radiology report and sentences in the findings section of the same report. Assess inter-rater agreement of breast radiologists. Develop and evaluate a support vector machine (SVM) classifier for automatically detecting cross-sectional relatedness. A standard reference is manually created from 444 breast radiology reports by the first author. A subset of 37 reports is annotated by five breast radiologists. Inter-rater agreement is computed among their annotations and standard reference. Thirteen numerical features are developed to characterize pairs of sentences; the optimal feature set is sought through forward selection. Inter-rater agreement is F-measure 0.623. SVM classifier has F-measure of 0.699 in the 12-fold cross-validation protocol against standard reference. Report length does not correlate with the classifier's performance (correlation coefficient = -0.073). SVM classifier has average F-measure of 0.505 against annotations by breast radiologists. Mediocre inter-rater agreement is possibly caused by: (1) definition is insufficiently actionable, (2) fine-grained nature of cross-sectional relatedness on sentence level, instead of, for instance, on paragraph level, and (3) higher-than-average complexity of 37-report sample. SVM classifier performs better against standard reference than against breast radiologists's annotations. This is supportive of (3). SVM's performance on standard reference is satisfactory. Since optimal feature set is not breast specific, results may transfer to non-breast anatomies. Applications include a smart report viewing environment and data mining.

[Integrating information about imaging biomarkers into structured radiology reports]

Imaging biomarkers describe objective characteristics that are related to normal biological processes, diseases, or the response to treatment. They enable radiologists to incorporate into their reports data about structure, function, and tissue components. With the aim of taking maximum advantage of the quantification of medical images, we present a procedure to integrate imaging biomarkers into radiological reports, bringing the new paradigm of personal medicine closer to radiological workflow. In this manner, the results of quantification can complement traditional radiological diagnosis, improving accuracy and the evaluation of the efficacy of treatments. A more personalized, standardized, structured radiological report should include quantitative analyses to complement conventional qualitative reporting in selected cases.

Retrieval of radiology reports citing critical findings with disease-specific customization

BACKGROUND

Communication of critical results from diagnostic procedures between caregivers is a Joint Commission national patient safety goal. Evaluating critical result communication often requires manual analysis of voluminous data, especially when reviewing unstructured textual results of radiologic findings. Information retrieval (IR) tools can facilitate this process by enabling automated retrieval of radiology reports that cite critical imaging findings. However, IR tools that have been developed for one disease or imaging modality often need substantial reconfiguration before they can be utilized for another disease entity.

PURPOSE

THIS PAPER: 1) describes the process of customizing two Natural Language Processing (NLP) and Information Retrieval/Extraction applications - an open-source toolkit, A Nearly New Information Extraction system (ANNIE); and an application developed in-house, Information for Searching Content with an Ontology-Utilizing Toolkit (iSCOUT) - to illustrate the varying levels of customization required for different disease entities and; 2) evaluates each application's performance in identifying and retrieving radiology reports citing critical imaging findings for three distinct diseases, pulmonary nodule, pneumothorax, and pulmonary embolus.

RESULTS

Both applications can be utilized for retrieval. iSCOUT and ANNIE had precision values between 0.90-0.98 and recall values between 0.79 and 0.94. ANNIE had consistently higher precision but required more customization.

CONCLUSION

Understanding the customizations involved in utilizing NLP applications for various diseases will enable users to select the most suitable tool for specific tasks.

Automatically correlating clinical findings and body locations in radiology reports using MedLEE

In this paper, we describe and evaluate a system that extracts clinical findings and body locations from radiology reports and correlates them. The system uses Medical Language Extraction and Encoding System (MedLEE) to map the reports' free text to structured semantic representations of their content. A lightweight reasoning engine extracts the clinical findings and body locations from MedLEE's semantic representation and correlates them. Our study is illustrative for research in which existing natural language processing software is embedded in a larger system. We manually created a standard reference based on a corpus of neuro and breast radiology reports. The standard reference was used to evaluate the precision and recall of the proposed system and its modules. Our results indicate that the precision of our system is considerably better than its recall (82.32-91.37% vs. 35.67-45.91%). We conducted an error analysis and discuss here the practical usability of the system given its recall and precision performance.

Bridging the text-image gap: a decision support tool for real-time PACS browsing

In this paper, we introduce an ontology-based technology that bridges the gap between MR images on the one hand and knowledge sources on the other hand. The proposed technology allows the user to express interest in a body region by selecting this region on the MR image he or she is viewing with a mouse device. The proposed technology infers the intended body structure from the manual selection and searches the external knowledge source for pertinent information. This technology can be used to bridge the gap between image data in the clinical workflow and (external) knowledge sources that help to assess the case with increased certainty, accuracy, and efficiency. We evaluate an instance of the proposed technology in the neurodomain by means of a user study in which three neuroradiologists participated. The user study shows that the technology has high recall (>95%) when it comes to inferring the intended brain region from the participant's manual selection. We are confident that this helps to increase the experience of browsing external knowledge sources.

Medical imaging data reconciliation, part 2: clinical order entry and imaging report data reconciliation

Data reconciliation in medical imaging is designed to ensure the accuracy and integrity of data across multiple steps in the imaging cycle, ultimately leading to improved continuity of patient care. An integral component of this data reconciliation is tied to the steps of clinical order entry and radiology report creation. The clinical data presented at order entry by the referring clinician influence a number of important imaging decisions, including examination selection, protocol design, image acquisition and processing, and interpretation of the imaging data set. The subsequent data derived from the radiology report have a profound impact on diagnosis, treatment, and overall clinical management. As a result, the reconciliation of clinical order entry and radiology report data affect health care delivery and in many respects are dependent on each other for optimal outcomes. The creation of a standardized reconciliation database that proactively records, tracks, analyzes, and provides feedback to radiologists and clinicians offers the potential to improve the quality and efficiency of patient care, while providing objective accountability measures for individual and institutional health care providers.

Medical imaging data reconciliation, part 1: innovation opportunity

The concept of data reconciliation has been well described in the industrial and pharmaceutical literature and is designed to ensure the integrity, reliability, and accuracy of sequential data in a multistep process. This concept can similarly be applied to the delivery of medical imaging services, which begins with order entry and ends with reporting and communication. The ultimate goal of medical imaging data reconciliation is to ensure continuity of care, extending across multiple service providers and imaging and information system technologies. The longitudinal analysis of standardized data across the imaging continuum presents an opportunity to improve communication between service providers, education and training, workflow, and clinical outcomes. In addition, the derived data can in turn be used to create data-driven best-practice guidelines and computerized decision support tools.