Mapping institution-specific study descriptions to RadLex Playbook entries.

The naming of imaging procedures is currently not standardized across institutions. As a result, it is a challenge to establish national registries, for instance, a national registry of dose to facilitate comparisons among different types of CT procedures. RSNA's RadLex Playbook is an effort towards addressing this gap (by introducing a unique Playbook identifier called an RPID for each procedure), and the current research focuses on semi-automatically mapping institution-specific procedure descriptions to Playbook entries to assist with this standardization effort. We discuss an algorithm we have developed to facilitate the mapping process which first extracts RadLex codes from the procedure description and then uses the definition of an RPID to determine the most suitable RPID(s) for the extracted set of RadLex codes. We also developed a tool that has three modes of operations-a single procedure mapping mode that allows a user to map a single institution-specific procedure description to a Playbook entry, a bulk mode to process large number of descriptions, and an exploratory mode that assists a user to better understand how the selection of values for various Playbook attributes affects the resulting RPID. We validate our algorithms using 166 production CT procedure descriptions and discuss how the tool can be used by administrators to map institution-specific procedure descriptions to RPIDs.

An ontology-based similarity measure for biomedical data-application to radiology reports.

BACKGROUND: Determining similarity between two individual concepts or two sets of concepts extracted from a free text document is important for various aspects of biomedicine, for instance, to find prior clinical reports for a patient that are relevant to the current clinical context. Using simple concept matching techniques, such as lexicon based comparisons, is typically not sufficient to determine an accurate measure of similarity. METHODS: In this study, we tested an enhancement to the standard document vector cosine similarity model in which ontological parent-child (is-a) relationships are exploited. For a given concept, we define a semantic vector consisting of all parent concepts and their corresponding weights as determined by the shortest distance between the concept and parent after accounting for all possible paths. Similarity between the two concepts is then determined by taking the cosine angle between the two corresponding vectors. To test the improvement over the non-semantic document vector cosine similarity model, we measured the similarity between groups of reports arising from similar clinical contexts, including anatomy and imaging procedure. We further applied the similarity metrics within a k-nearest-neighbor (k-NN) algorithm to classify reports based on their anatomical and procedure based groups. 2150 production CT radiology reports (952 abdomen reports and 1128 neuro reports) were used in testing with SNOMED CT, restricted to Body structure, Clinical finding and Procedure branches, as the reference ontology. RESULTS: The semantic algorithm preferentially increased the intra-class similarity over the inter-class similarity, with a 0.07 and 0.08 mean increase in the neuro-neuro and abdomen-abdomen pairs versus a 0.04 mean increase in the neuro-abdomen pairs. Using leave-one-out cross-validation in which each document was iteratively used as a test sample while excluding it from the training data, the k-NN based classification accuracy was shown in all cases to be consistently higher with the semantics based measure compared with the non-semantic case. Moreover, the accuracy remained steady even as k value was increased - for the two anatomy related classes accuracy for k=41 was 93.1% with semantics compared to 86.7% without semantics. Similarly, for the eight imaging procedures related classes, accuracy (for k=41) with semantics was 63.8% compared to 60.2% without semantics. At the same k, accuracy improved significantly to 82.8% and 77.4% respectively when procedures were logically grouped together into four classes (such as ignoring contrast information in the imaging procedure description). Similar results were seen at other k-values. CONCLUSIONS: The addition of semantic context into the document vector space model improves the ability of the cosine similarity to differentiate between radiology reports of different anatomical and image procedure-based classes. This effect can be leveraged for document classification tasks, which suggests its potential applicability for biomedical information retrieval.

A novel hands-free carotid ultrasound detects low-flow cardiac output in a swine model of pulseless electrical activity arrest.

OBJECTIVE: To determine if a hands-free, noninvasive Doppler ultrasound device can reliably detect low-flow cardiac output by measuring carotid artery blood flow velocities. We compared the ability of observers to detect carotid artery flow velocity differences between pseudo-pulseless electrical activity (PEA) and true-PEA cardiac arrest. METHODS: Five swine were instrumented with aortic (Ao) and right atrial pressure-transducing catheters. The Doppler ultrasound device was adhered to the neck over the carotid artery. Continuous electrocardiogram, pressure readings, and Doppler signal were recorded. Each swine underwent multiple episodes of fibrillation and resuscitation. Episodes of true-PEA and pseudo-PEA were retrospectively identified from all resuscitation attempts by examination of electrocardiogram and Ao waveforms. The sensitivity and specificity of the device to detect pseudo-PEA was obtained using observers blinded to Ao waveform recordings. RESULTS: There was good interobserver reliability related to identification of pseudo- and true-PEA (κ = 0.873). The observers blinded to Ao waveform recordings agreed on 8 of the 9 episodes of pseudo-PEA, whereas 4 false positives of 26 true-PEA events were reported (sensitivity, 0.89; specificity, 0.85). The Doppler device was able to detect carotid flow velocity over a wide range of Ao blood pressures. CONCLUSIONS: This hands-free, noninvasive Doppler ultrasound device can reliably differentiate pseudo-PEA from true-PEA during resuscitation from cardiac arrest, detecting pressure gradient changes of less than 5 mm Hg through to normotension. This device distinguishes conditions of no cardiac output from low cardiac output and may have applications for use during resuscitation from various etiologies of arrest and shock.

An automated carotid pulse assessment approach using Doppler ultrasound.

During cardiac arrest emergencies, lay rescuers are required to manually check the patient's carotid pulse after the delivery of defibrillation shocks to assess the cardiac resuscitation progress of the patient. As a more automated way of monitoring the resuscitation progress, a new Doppler-ultrasound-based carotid pulse assessment approach is presented in this paper. The method works by analyzing the temporal aperiodicity of Doppler shifts seen in the ultrasound echoes returned from the patient's carotid arteries. As a quantitative investigation with this method, we derived a new measure called the pulselessness indicator to assess whether a carotid pulse is absent based on the given Doppler information. To study the performance of the new carotid pulse checking method, we built a multi-channel CW Doppler prototype device to acquire Doppler data in vivo during cardiac arrest experiments conducted on five different swines and computed pulselessness indicator estimates with these data. Our results indicated that the Doppler-based pulse checking approach has good sensitivity and specificity: it had a pulselessness detection rate greater than 0.9 for a given false alarm rate of 0.05. As a further analysis, the prototype device was applied to other experiments where the swine had suffered cardiac arrest for over five minutes. It showed a consistent assessment performance on the monitoring of the swine's resuscitation progress after defibrillation and chest compressions.