- Assessing data quality and the variability of source data verification auditing methods in clinical research settings. [Journal Article]
- JBJ Biomed Inform 2018 May 18
- CONCLUSIONS: A wide range of SDV auditing methods are reported in the published literature though no uniform SDV auditing method could be determined for "best practice" in clinical trials. Published audit methodology articles are warranted for the development of a standardised SDV auditing method to monitor data quality in clinical research settings.
- New JBI policy emphasizes substantive, practical methodological innovations for biomedical privacy and security papers. [Editorial]
- JBJ Biomed Inform 2018 May 17
- A Detailed Analysis of the Arden Syntax Expression Grammar. [Journal Article]
- JBJ Biomed Inform 2018 May 15
- CONCLUSIONS: Arden Syntax expressions are affected by anomalies. Since only a small proportion of them have practical relevance and they cannot cause false calculations or clinical decisions, their practical impact is likely limited. However, they may be potential points of confusion for knowledge engineers. An alternative expression grammar, based on a different encoding approach, would not only eliminate the anomalies, but could considerably facilitate both maintenance and further development of the standard.
- Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity. [Journal Article]
- JBJ Biomed Inform 2018 May 12
- Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-co...
Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases.
- Exploiting Semantic Patterns over Biomedical Knowledge Graphs for Predicting Treatment and Causative Relations. [Journal Article]
- JBJ Biomed Inform 2018 May 12
- CONCLUSIONS: We employed semantic graph patterns connecting pairs of candidate entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.
- Detecting pathway biomarkers of diabetic progression with differential entropy. [Journal Article]
- JBJ Biomed Inform 2018 May 12
- Gene expression profiling techniques measure the transcriptional dynamics of thousands of genes in parallel manners. The available high-throughput transcriptomic datasets provide unprecedented opport...
Gene expression profiling techniques measure the transcriptional dynamics of thousands of genes in parallel manners. The available high-throughput transcriptomic datasets provide unprecedented opportunities of detecting biomarkers or signatures of complex diseases such as diabetes. In this work, we propose a computational method based on differential entropy to identify diabetic pathway biomarkers in rats from gene expression profiling data. We first collect the knowledgebase-documented pathways and map them with the corresponding gene expressions in control and disease samples, respectively. The pathway entropies are defined to evaluate their dysfunction-related activities and implications during the development and progression of type 2 diabetes. We rank these pathways via their differential status of entropy dynamics in the time series. The pathway biomarkers are then screened out by their classification ability of distinguishing diabetes from controls. The comparative studies with the other alternative methods demonstrate the effectiveness and advantage of our proposed strategy of biomarker identification. The classification performances on independent datasets further validate the diagnosis applicability of these identified pathway biomarkers. The functional enrichment analyses of these pathway biomarkers also indicate the pathogenesis of diabetes.
- Simulation of Patient Flow in Multiple Healthcare Units using Process and Data Mining Techniques for Model Identification. [Journal Article]
- JBJ Biomed Inform 2018 May 10
- CONCLUSIONS: The proposed approach, methods, and solutions provide a conceptual, methodological, and programming framework for the implementation of a simulation of complex and diverse scenarios within a flow of patients for different purposes: decision making, training, management optimization, and others.
- Usability Evaluation of a Medication Reconciliation Tool: Embedding Safety Probes to Assess Users' Detection of Medication Discrepancies. [Journal Article]
- JBJ Biomed Inform 2018 May 08
- CONCLUSIONS: Overall, detection of medication discrepancies was low. Findings indicate that more advanced interface designs are warranted. Future research is needed on how technologies can be designed to better aid HCPs' and patients' detection of medication discrepancies.This is one of the first studies to evaluate the usability of a collaborative medication reconciliation tool and assess HCPs' and patients' detection of medication discrepancies. Results demonstrate that embedded safety probes can enhance standard usability methods by measuring additional, clinically-focused usability outcomes. The novel safety probes we used may serve as an initial, standard set for future medication reconciliation research. More prevalent use of safety probes could strengthen usability research for a variety of health information technologies.
- An effective neural model extracting document level chemical-induced disease relations from biomedical literature. [Journal Article]
- JBJ Biomed Inform 2018 May 07
- Since identifying relations between chemicals and diseases (CDR) are important for biomedical research and healthcare, the challenge proposed by BioCreative V requires automatically mining causal rel...
Since identifying relations between chemicals and diseases (CDR) are important for biomedical research and healthcare, the challenge proposed by BioCreative V requires automatically mining causal relationships between chemicals and diseases which may span sentence boundaries. Although most systems explore feature engineering and knowledge bases to recognize document level CDR relations, feature learning automatically is limited only in a sentence. In this work, we proposed an effective model that automatically learns document level semantic representations to extract chemical-induced disease (CID) relations from articles by combining advantages of convolutional neural network and recurrent neural network. First, to purposefully collect contexts, candidate entities existing in multiple sentences of an article were masked to make the model have ability to discern candidate entities and general terms. Next, considering the contiguity and temporality among associated sentences as well as the topic of an article, a hierarchical network architecture was designed at the document level to capture semantic information of different types of text segments in an article. Finally, a softmax classifier performed the CID recognition. Experimental results on the CDR corpus show that the proposed model achieves a good overall performance compared with other state-of-the-art methods. Although only using two types of embedding vectors, our approach can perform well for recognizing not only intra-sentential but also inter-sentential CID relations.
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- Classification of Forensic Autopsy Reports through Conceptual Graph-based Document Representation Model. [Journal Article]
- JBJ Biomed Inform 2018 May 05
- Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative foren...
Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD.