The electrocardiogram (ECG) found many clinical applications. Recently, it was proposed as a promising technology also for biometric applications, i.e., to recognize a subject within a group of known people. For such an application, the accuracy of classical ECG clinical recordings is usually not needed, but the measurement procedure should be fast, robust and cheap. We developed an embedded wearable system for recording one-lead ECG from the wrists of a person. The system was used to record data from 10 subjects. Data were pre-processed to reduce the noise content. Then fiducial points were detected and used to train an ensemble of support vector machines to identify a person among the group. Mean classification accuracy was higher than 95 % if a single heartbeat was considered and higher than 98 % if 3 consecutive heartbeats were used, choosing by majority. The system is fast (a few seconds are needed), not invasive and can be used either standalone or together with other identification techniques to increase the safety level.
Extracting valuable knowledge from Electronic Health Records (EHR) represents a challenging task due to the presence of both structured and unstructured data, including codified fields, images and test results. Narrative text in particular contains a variety of notes which are diverse in language and detail, as well as being full of ad hoc terminology, including acronyms and jargon, which is especially challenging in non-English EHR, where there is a dearth of annotated corpora or trained case sets. This paper proposes an approach for NER and concept attribute labeling for EHR that takes into consideration the contextual words around the entity of interest to determine its sense. The approach proposes a composition method of three different NER methods, together with the analysis of the context (neighboring words) using an ensemble classification model. This contributes to disambiguate NER, as well as labeling the concept as confirmed, negated, speculative, pending or antecedent. Results show an improvement of the recall and a limited impact on precision for the NER process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.