Nuanced HEXACO: A Meta-Analysis of HEXACO Cross-Rater Agreement, Heritability, and Rank-Order Stability.

Most Five-Factor Model (FFM) questionnaire items contain unique variance that is partly heritable, stable, and consensually observable, demonstrates consistent associations with age and sex, and predicts life outcomes beyond higher order factors. Extending these findings to the HEXACO model, we meta-analyzed single-item cross-rater agreement, heritability, and 2-year stability using samples from six countries. We analyzed raw item scores and their residual variance and adjusted the estimates for measurement unreliability. The median cross-rater agreement, heritability, and stability estimates were, respectively, .30, .30, and .57, for raw items and .10, .16, and .39, for item residuals. Adjusted for reliability, the respective medians were .46 and .25 for cross-rater agreement, .46 and .39 for heritability, and .87 and .94 for stability. These results are strikingly consistent with FFM-based findings, providing nondismissible evidence that single items index a partly unique level of the trait hierarchy-personality nuances-with trait properties comparable to those of higher-order traits.

Test-retest reliability of the HEXACO-100-And the value of multiple measurements for assessing reliability.

Despite the widespread use of the HEXACO model as a descriptive taxonomy of personality traits, there remains limited information on the test-retest reliability of its commonly-used inventories. Studies typically report internal consistency estimates, such as alpha or omega, but there are good reasons to believe that these do not accurately assess reliability. We report 13-day test-retest correlations of the 100- and 60-item English HEXACO Personality Inventory-Revised (HEXACO-100 and HEXACO-60) domains, facets, and items. In order to test the validity of test-retest reliability, we then compare these estimates to correlations between self- and informant-reports (i.e., cross-rater agreement), a widely-used validity criterion. Median estimates of test-retest reliability were .88, .81, and .65 (N = 416) for domains, facets, and items, respectively. Facets' and items' test-retest reliabilities were highly correlated with their cross-rater agreement estimates, whereas internal consistencies were not. Overall, the HEXACO Personality Inventory-Revised demonstrates test-retest reliability similar to other contemporary measures. We recommend that short-term retest reliability should be routinely calculated to assess reliability.

A Context-Enhanced De-identification System.

Many modern entity recognition systems, including the current state-of-the-art de-identification systems, are based on bidirectional long short-term memory (biLSTM) units augmented by a conditional random field (CRF) sequence optimizer. These systems process the input sentence by sentence. This approach prevents the systems from capturing dependencies over sentence boundaries and makes accurate sentence boundary detection a prerequisite. Since sentence boundary detection can be problematic especially in clinical reports, where dependencies and co-references across sentence boundaries are abundant, these systems have clear limitations. In this study, we built a new system on the framework of one of the current state-of-the-art de-identification systems, NeuroNER, to overcome these limitations. This new system incorporates context embeddings through forward and backward n -grams without using sentence boundaries. Our context-enhanced de-identification (CEDI) system captures dependencies over sentence boundaries and bypasses the sentence boundary detection problem altogether. We enhanced this system with deep affix features and an attention mechanism to capture the pertinent parts of the input. The CEDI system outperforms NeuroNER on the 2006 i2b2 de-identification challenge dataset, the 2014 i2b2 shared task de-identification dataset, and the 2016 CEGS N-GRID de-identification dataset (p < 0.01). All datasets comprise narrative clinical reports in English but contain different note types varying from discharge summaries to psychiatric notes. Enhancing CEDI with deep affix features and the attention mechanism further increased performance.

Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest.

In this paper, we describe how we applied LBD techniques to discover lecithin cholesterol acyltransferase (LCAT) as a druggable target for cardiac arrest. We fully describe our process which includes the use of high-throughput metabolomic analysis to identify metabolites significantly related to cardiac arrest, and how we used LBD to gain insights into how these metabolites relate to cardiac arrest. These insights lead to our proposal (for the first time) of LCAT as a druggable target; the effects of which are supported by in vivo studies which were brought forth by this work. Metabolites are the end product of many biochemical pathways within the human body. Observed changes in metabolite levels are indicative of changes in these pathways, and provide valuable insights toward the cause, progression, and treatment of diseases. Following cardiac arrest, we observed changes in metabolite levels pre- and post-resuscitation. We used LBD to help discover diseases implicitly linked via these metabolites of interest. Results of LBD indicated a strong link between Fish Eye disease and cardiac arrest. Since fish eye disease is characterized by an LCAT deficiency, it began an investigation into the effects of LCAT and cardiac arrest survival. In the investigation, we found that decreased LCAT activity may increase cardiac arrest survival rates by increasing ω-3 polyunsaturated fatty acid availability in circulation. We verified the effects of ω-3 polyunsaturated fatty acids on increasing survival rate following cardiac arrest via in vivo with rat models.

Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition.

BACKGROUND: As a risk factor for many diseases, family history (FH) captures both shared genetic variations and living environments among family members. Though there are several systems focusing on FH extraction using natural language processing (NLP) techniques, the evaluation protocol of such systems has not been standardized. OBJECTIVE: The n2c2/OHNLP (National NLP Clinical Challenges/Open Health Natural Language Processing) 2019 FH extraction task aims to encourage the community efforts on a standard evaluation and system development on FH extraction from synthetic clinical narratives. METHODS: We organized the first BioCreative/OHNLP FH extraction shared task in 2018. We continued the shared task in 2019 in collaboration with the n2c2 and OHNLP consortium, and organized the 2019 n2c2/OHNLP FH extraction track. The shared task comprises 2 subtasks. Subtask 1 focuses on identifying family member entities and clinical observations (diseases), and subtask 2 expects the association of the living status, side of the family, and clinical observations with family members to be extracted. Subtask 2 is an end-to-end task which is based on the result of subtask 1. We manually curated the first deidentified clinical narrative from FH sections of clinical notes at Mayo Clinic Rochester, the content of which is highly relevant to patients' FH. RESULTS: A total of 17 teams from all over the world participated in the n2c2/OHNLP FH extraction shared task, where 38 runs were submitted for subtask 1 and 21 runs were submitted for subtask 2. For subtask 1, the top 3 runs were generated by Harbin Institute of Technology, ezDI, Inc., and The Medical University of South Carolina with F1 scores of 0.8745, 0.8225, and 0.8130, respectively. For subtask 2, the top 3 runs were from Harbin Institute of Technology, ezDI, Inc., and University of Florida with F1 scores of 0.681, 0.6586, and 0.6544, respectively. The workshop was held in conjunction with the AMIA 2019 Fall Symposium. CONCLUSIONS: A wide variety of methods were used by different teams in both tasks, such as Bidirectional Encoder Representations from Transformers, convolutional neural network, bidirectional long short-term memory, conditional random field, support vector machine, and rule-based strategies. System performances show that relation extraction from FH is a more challenging task when compared to entity identification task.

Differentiation of burn wounds in an in vivo porcine model using terahertz spectroscopy.

The accuracy of current burn triage techniques has remained between 50-70%. Accordingly, there is a significant clinical need for the quantitative and accurate assessment of partial-thickness burn injuries. Porcine skin represents the closest animal model to human skin, and is often used in surgical skin grafting procedures. In this study, we used a standardized in vivo porcine burn model to obtain terahertz (THz) point-spectroscopy measurements from burns with various severities. We then extracted two reflection hyperspectral parameters, namely spectral area under the curve between approximately 0.1 and 0.9 THz (-10 dB bandwidth in each spectrum), and spectral slope, to characterize each burn. Using a linear combination of these two parameters, we accurately classified deep partial- and superficial partial-thickness burns (p = 0.0159), compared to vimentin immunohistochemistry as the gold standard for burn depth determination.

The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview.

BACKGROUND: Semantic textual similarity is a common task in the general English domain to assess the degree to which the underlying semantics of 2 text segments are equivalent to each other. Clinical Semantic Textual Similarity (ClinicalSTS) is the semantic textual similarity task in the clinical domain that attempts to measure the degree of semantic equivalence between 2 snippets of clinical text. Due to the frequent use of templates in the Electronic Health Record system, a large amount of redundant text exists in clinical notes, making ClinicalSTS crucial for the secondary use of clinical text in downstream clinical natural language processing applications, such as clinical text summarization, clinical semantics extraction, and clinical information retrieval. OBJECTIVE: Our objective was to release ClinicalSTS data sets and to motivate natural language processing and biomedical informatics communities to tackle semantic text similarity tasks in the clinical domain. METHODS: We organized the first BioCreative/OHNLP ClinicalSTS shared task in 2018 by making available a real-world ClinicalSTS data set. We continued the shared task in 2019 in collaboration with National NLP Clinical Challenges (n2c2) and the Open Health Natural Language Processing (OHNLP) consortium and organized the 2019 n2c2/OHNLP ClinicalSTS track. We released a larger ClinicalSTS data set comprising 1642 clinical sentence pairs, including 1068 pairs from the 2018 shared task and 1006 new pairs from 2 electronic health record systems, GE and Epic. We released 80% (1642/2054) of the data to participating teams to develop and fine-tune the semantic textual similarity systems and used the remaining 20% (412/2054) as blind testing to evaluate their systems. The workshop was held in conjunction with the American Medical Informatics Association 2019 Annual Symposium. RESULTS: Of the 78 international teams that signed on to the n2c2/OHNLP ClinicalSTS shared task, 33 produced a total of 87 valid system submissions. The top 3 systems were generated by IBM Research, the National Center for Biotechnology Information, and the University of Florida, with Pearson correlations of r=.9010, r=.8967, and r=.8864, respectively. Most top-performing systems used state-of-the-art neural language models, such as BERT and XLNet, and state-of-the-art training schemas in deep learning, such as pretraining and fine-tuning schema, and multitask learning. Overall, the participating systems performed better on the Epic sentence pairs than on the GE sentence pairs, despite a much larger portion of the training data being GE sentence pairs. CONCLUSIONS: The 2019 n2c2/OHNLP ClinicalSTS shared task focused on computing semantic similarity for clinical text sentences generated from clinical notes in the real world. It attracted a large number of international teams. The ClinicalSTS shared task could continue to serve as a venue for researchers in natural language processing and medical informatics communities to develop and improve semantic textual similarity techniques for clinical text.

The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records.

OBJECTIVE: The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task track 3, focused on medical concept normalization (MCN) in clinical records. This track aimed to assess the state of the art in identifying and matching salient medical concepts to a controlled vocabulary. In this paper, we describe the task, describe the data set used, compare the participating systems, present results, identify the strengths and limitations of the current state of the art, and identify directions for future research. MATERIALS AND METHODS: Participating teams were provided with narrative discharge summaries in which text spans corresponding to medical concepts were identified. This paper refers to these text spans as mentions. Teams were tasked with normalizing these mentions to concepts, represented by concept unique identifiers, within the Unified Medical Language System. Submitted systems represented 4 broad categories of approaches: cascading dictionary matching, cosine distance, deep learning, and retrieve-and-rank systems. Disambiguation modules were common across all approaches. RESULTS: A total of 33 teams participated in the MCN task. The best-performing team achieved an accuracy of 0.8526. The median and mean performances among all teams were 0.7733 and 0.7426, respectively. CONCLUSIONS: Overall performance among the top 10 teams was high. However, several mention types were challenging for all teams. These included mentions requiring disambiguation of misspelled words, acronyms, abbreviations, and mentions with more than 1 possible semantic type. Also challenging were complex mentions of long, multi-word terms that may require new ways of extracting and representing mention meaning, the use of domain knowledge, parse trees, or hand-crafted rules.

2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.

OBJECTIVE: This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records and evaluated 3 tasks: concept extraction, relation classification, and end-to-end systems. We perform an analysis of the results to identify the state of the art in these tasks, learn from it, and build on it. MATERIALS AND METHODS: For all tasks, teams were given raw text of narrative discharge summaries, and in all the tasks, participants proposed deep learning-based methods with hand-designed features. In the concept extraction task, participants used sequence labelling models (bidirectional long short-term memory being the most popular), whereas in the relation classification task, they also experimented with instance-based classifiers (namely support vector machines and rules). Ensemble methods were also popular. RESULTS: A total of 28 teams participated in task 1, with 21 teams in tasks 2 and 3. The best performing systems set a high performance bar with F1 scores of 0.9418 for concept extraction, 0.9630 for relation classification, and 0.8905 for end-to-end. However, the results were much lower for concepts and relations of Reasons and ADEs. These were often missed because local context is insufficient to identify them. CONCLUSIONS: This challenge shows that clinical concept extraction and relation classification systems have a high performance for many concept types, but significant improvement is still required for ADEs and Reasons. Incorporating the larger context or outside knowledge will likely improve the performance of future systems.

Indirect association and ranking hypotheses for literature based discovery.

BACKGROUND: Literature Based Discovery (LBD) produces more potential hypotheses than can be manually reviewed, making automatically ranking these hypotheses critical. In this paper, we introduce the indirect association measures of Linking Term Association (LTA), Minimum Weight Association (MWA), and Shared B to C Set Association (SBC), and compare them to Linking Set Association (LSA), concept embeddings vector cosine, Linking Term Count (LTC), and direct co-occurrence vector cosine. Our proposed indirect association measures extend traditional association measures to quantify indirect rather than direct associations while preserving valuable statistical properties. RESULTS: We perform a comparison between several different hypothesis ranking methods for LBD, and compare them against our proposed indirect association measures. We intrinsically evaluate each method's performance using its ability to estimate semantic relatedness on standard evaluation datasets. We extrinsically evaluate each method's ability to rank hypotheses in LBD using a time-slicing dataset based on co-occurrence information, and another time-slicing dataset based on SemRep extracted-relationships. Precision and recall curves are generated by ranking term pairs and applying a threshold at each rank. CONCLUSIONS: Results differ depending on the evaluation methods and datasets, but it is unclear if this is a result of biases in the evaluation datasets or if one method is truly better than another. We conclude that LTC and SBC are the best suited methods for hypothesis ranking in LBD, but there is value in having a variety of methods to choose from.

A Literature Based Discovery Visualization System with Hierarchical Clustering and Linking Set Associations.

Literature Based discovery (LBD) seeks to find information implicit in text, but never explicitly stated. In this work, we develop a method of visually summarizing LBD output in an automatically generated tree structure. This structure promotes a comprehensive understanding of LBD output as a whole, and encourages the user to explore branches of the hierarchy they find most interesting or surprising. This novel visualization system requires the development and integration of automatic functional group discovery, set associations, and linking set associations. Specifically, we perform hierarchical clustering on the potential discoveries generated by an LBD system to create a tree of potential hypotheses. We weight the tree by developing set association measures, and extending them to linking set association measures. This weighted tree is displayed in an interactive visual environment, and validated by replicating the historic Raynaud's Disease - fish oil discovery.

Association measures for estimating semantic similarity and relatedness between biomedical concepts.

Association measures quantify the observed likelihood a term pair co-occurs versus their predicted co-occurrence together if by chance. This is based both on the terms' individual occurrence frequencies, and their mutual co-occurrence frequencies. One application of association scores is estimating semantic relatedness, which is critical for many natural language processing applications, such as clustering of biomedical and clinical documents and the development of biomedical terminologies and ontololgies. In this paper we propose a method of generating association scores between biomedical concepts to estimate semantic relatedness. We use co-occurrence statistics between Unified Medical Language System (UMLS) concepts to account for lexical variation at the synonymous level, and introduce a process of concept expansion that exploits hierarchical information from the UMLS to account for lexical variation at the hyponymous level. State of the art results are achieved on several standard evaluation datasets, and an in depth analysis of hyper-parameters is presented.

Vector representations of multi-word terms for semantic relatedness.

This paper presents a comparison between several multi-word term aggregation methods of distributional context vectors applied to the task of semantic similarity and relatedness in the biomedical domain. We compare the multi-word term aggregation methods of summation of component word vectors, mean of component word vectors, direct construction of compound term vectors using the compoundify tool, and direct construction of concept vectors using the MetaMap tool. Dimensionality reduction is critical when constructing high quality distributional context vectors, so these baseline co-occurrence vectors are compared against dimensionality reduced vectors created using singular value decomposition (SVD), and word2vec word embeddings using continuous bag of words (CBOW), and skip-gram models. We also find optimal vector dimensionalities for the vectors produced by these techniques. Our results show that none of the tested multi-word term aggregation methods is statistically significantly better than any other. This allows flexibility when choosing a multi-word term aggregation method, and means expensive corpora preprocessing may be avoided. Results are shown with several standard evaluation datasets, and state of the results are achieved.

Literature Based Discovery: Models, methods, and trends.

OBJECTIVES: This paper provides an introduction and overview of literature based discovery (LBD) in the biomedical domain. It introduces the reader to modern and historical LBD models, key system components, evaluation methodologies, and current trends. After completion, the reader will be familiar with the challenges and methodologies of LBD. The reader will be capable of distinguishing between recent LBD systems and publications, and be capable of designing an LBD system for a specific application. TARGET AUDIENCE: From biomedical researchers curious about LBD, to someone looking to design an LBD system, to an LBD expert trying to catch up on trends in the field. The reader need not be familiar with LBD, but knowledge of biomedical text processing tools is helpful. SCOPE: This paper describes a unifying framework for LBD systems. Within this framework, different models and methods are presented to both distinguish and show overlap between systems. Topics include term and document representation, system components, and an overview of models including co-occurrence models, semantic models, and distributional models. Other topics include uninformative term filtering, term ranking, results display, system evaluation, an overview of the application areas of drug development, drug repurposing, and adverse drug event prediction, and challenges and future directions. A timeline showing contributions to LBD, and a table summarizing the works of several authors is provided. Topics are presented from a high level perspective. References are given if more detailed analysis is required.

Effects of oral administration of manganese on the kidneys and urinary bladder of Sprague-Dawley rats.

The purpose of this study was to investigate the effect of oral administration of manganese acetate on the kidneys and urinary bladder of Sprague-Dawley (SD) rats. Male and female SD rats (150 to 175 g), 6 weeks old, were administered varying doses of manganese acetate for 63 days by oral gavage. At the end of 63 days, 50% of the animals were sacrificed and kidney tissue was isolated and fixed for histopathological studies (study A). The remaining 50% were cross-mated and dosing ceased. Animals were sacrificed after 2 weeks (study B). Male treated animals were noted to have viscous, gritty urine in the urinary bladder, and the high-dose groups had urinary bladder stones (uroliths). Histopathologically, the most striking lesions were observed in the kidneys and prostate glands of male animals. Mild-to-moderate tubulointerstitial nephritis with tubular proteineous and glomerulosclerosis was observed in animals of all treatment groups. Urolithiasis in the urinary bladder was confirmed in 33% to 66% of treated animals. Female animals did not show a significant difference above controls in renal tissues. Results of this study suggest that male rats are more sensitive to the effects of high levels of manganese given orally than female rats and that the genitourinary structures represent target organs of toxicity.

Role of the maternal acute phase response and tumor necrosis factor alpha in the developmental toxicity of lipopolysaccharide in the CD-1 mouse.

The acute phase response (APR) functions to reset metabolic homeostasis following infectious, toxic, or traumatic insult. TNF-alpha, a putative mediator of the APR, has been associated with fetal death in rodents and preterm labor and delivery in humans. We hypothesized that physiologic changes associated with the maternal APR may play a role in adverse embryo/fetal outcome. Pregnant CD-1 mice injected i.p. with lipopolysaccharide (LPS), a model inducer of the APR, on gestation day (gd) 9 showed a dose-related increase in embryo death on gd 10. Histology indicated placental infarct and necrosis. Maternal serum TNF-alpha levels, measured by ELISA following administration of 0.05 mg/kg LPS on gd 9, were found to increase significantly and peak within 1 to 1.5 h. Pretreatment with 0.01 mg/kg LPS on gd 8 ameliorated embryotoxicity of the 0.05 mg/kg LPS treatment on gd 9 and also eliminated the increase in serum TNF-alpha. Direct LPS exposure in whole embryo culture was nontoxic. These data support a maternally mediated mechanism of LPS embryolethality, and suggest that TNF-alpha may be an important mediator of this developmental toxicity.

Altered hox gene expression and cellular pathogenesis of 5-aza-2'-deoxycytidine-induced murine hindlimb dysmorphogenesis.

The DNA demethylating agent, 5-aza-2'-deoxycytidine (d-AZA), elicits temporally related morphological defects and altered gene expression in mouse hindlimbs. Segmental formation of limb regions (stylopod, zeugopod. and autopod) is partially dependent on Hox gene activation. The objective of this study was to understand the role of altered expression of key hox genes in the early pathogenesis of d-AZA-induced hindlimb defects in mice. Semiquantitative RT-PCR was used to analyze hox gene expression (Hox C-11 and Hox A and D homologs, paralogs 9-13). Untreated and treated fore and hindlimb buds were collected 12 and 24 hours after IP injection (1 mg/kg) of d-AZA at 9 am on gestational (GD) 10 and processed for RT-PCR. Additional pregnant mice were treated similarly and whole embryos collected 12 and 24 hours posttreatment and processed for histopathological analysis. No changes in hox gene expression were detected in the forelimb tissue. There was a 2-fold down-regulation of hoxA-11 and C-11 in the 12-hour hindlimb bud tissue. No changes in the HoxD series were detected in the hindlimb bud tissue. The 12- and 24-hour untreated mice exhibited some of the morphological features consistent with physiological apoptosis. Most tissues of the treated mice exhibited cellular changes consistent with cell death associated with the cytotoxicity of the compound. The data reported supports the hypothesis that altered gene expression and not cytotoxicity alone is associated with d-AZA-induced hindlimb dysmorphogenesis.