Journal of biomedical informatics
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Automated extraction of protein-protein interactions (PPIs) from biomedical literatures is an important topic of biomedical text mining. In this paper, we propose an approach based on neighborhood hash graph kernel for this task. ⋯ We evaluate the proposed approach on five publicly available PPI corpora and perform detailed comparisons with other approaches. The experimental result shows that our approach is comparable to the state-of-the-art PPI extraction system and much faster than all-path graph kernel approach on all five PPI corpora.
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There are different approaches for repurposing clinical data collected in the Electronic Healthcare Record (EHR) for use in clinical research. Semantic integration of "siloed" applications across domain boundaries is the raison d'être of the standards-based profiles developed by the Integrating the Healthcare Enterprise (IHE) initiative - an initiative by healthcare professionals and industry promoting the coordinated use of established standards such as DICOM and HL7 to address specific clinical needs in support of optimal patient care. In particular, the combination of two IHE profiles - the integration profile "Retrieve Form for Data Capture" (RFD), and the IHE content profile "Clinical Research Document" (CRD) - offers a straightforward approach to repurposing EHR data by enabling the pre-population of the case report forms (eCRF) used for clinical research data capture by Clinical Data Management Systems (CDMS) with previously collected EHR data. ⋯ Single-source data entry in the clinical care context for use in the clinical research context - a process enabled through the use of the EHR as single point of data entry, can - if demonstrated to be a viable strategy - not only significantly reduce data collection efforts while simultaneously increasing data collection accuracy secondary to elimination of transcription or double-entry errors between the two contexts but also ensure that all the clinical data for a given patient, irrespective of the data collection context, are available in the EHR for decision support and treatment planning. The RE-USE approach used mapping algorithms to identify semantic coherence between clinical care and clinical research data elements and pre-populate eCRFs. The RE-USE project utilized SNOMED International v.3.5 as its "pivot reference terminology" to support EHR-to-eCRF mapping, a decision that likely enhanced the "recall" of the mapping algorithms. The RE-USE results demonstrate the difficult challenges involved in semantic integration between the clinical care and clinical research contexts.
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In critical care environments such as the emergency department (ED), many activities and decisions are not planned. In this study, we developed a new methodology for systematically studying what are these unplanned activities and decisions. This methodology expands the traditional naturalistic decision making (NDM) frameworks by explicitly identifying the role of environmental factors in decision making. ⋯ The empirical data provide important insight to the complexity of the ED environment by highlighting adaptive behavior in this intricate milieu. Our results show that half of decisions in the ED we studied are not planned, rather decisions are opportunistic decision (34%) or influenced by interruptions or distractions (21%). What impacts these unplanned decisions have on the quality, safety, and efficiency in the ED environment are important research topics for future investigation.
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An important problem in the Intensive Care is how to predict on a given day of stay the eventual hospital mortality for a specific patient. A recent approach to solve this problem suggested the use of frequent temporal sequences (FTSs) as predictors. Methods following this approach were evaluated in the past by inducing a model from a training set and validating the prognostic performance on an independent test set. ⋯ Using inductive methods of prognostic models based on temporal sequence discovery within the bootstrap procedure is however novel at least in predictive models in the Intensive Care. Our results of applying the bootstrap-based evaluative procedure demonstrate the superiority of the FTS-based inductive method over the traditional method in terms of discrimination as well as accuracy. In addition we illustrate the insights gained by the analyst into the discovered FTSs from the bootstrap samples.
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The purpose of this study is to evaluate the usability of emergency department (ED) software prototypes developed for Tablet personal computers (Tablet PCs) in order to keep electronic health records (EHRs) of patients errorless and accessible through mobile technologies. In order to serve this purpose, two alternative prototypes were developed for Tablet PCs: Mobile Emergency Department Software (MEDS) and Mobile Emergency Department Software Iconic (MEDSI) among which the user might choose the more appropriate one for ED operations based on a usability analysis involving the target users. ⋯ The study provides two important contributions to the extant literature. First, it addresses a topic and methodology that serves potentially interesting to the biomedical informatics community. Drawing on good background information and appropriate context, it involves various aspects of usability testing. Another contribution of the study lies in its examination of two different prototypes during the design phase involving the target users.