A Framework for Feature Selection using Natural Language Processing for User Profile Learning for Recommendations of Healthcare Related Content

This paper presents the work done on recommendations of healthcare related journal papers by understanding the semantics of terms from the papers referred by users in past. In other words, user profiles based on user interest within the healthcare domain are constructed from the kind of journal papers read by the users. Multiple user profiles are constructed for each user based on different categories of papers read by the users. The proposed approach goes to the granular level of extrinsic and intrinsic relationship between terms and clusters highly semantically related relevant domain terms where each cluster represents a user interest area. The semantic analysis of terms is done starting from co-occurrence analysis to extract the intra-couplings between terms and then the inter-couplings are extracted from the intra-couplings and then finally clusters of highly related terms are formed. The experiments showed improved precision for the proposed approach as compared to the state-of-the-art technique with a mean reciprocal rank of 0.76.

The Explanatory Effect of a Label: Its Influence on a Category Persists Even If We Forget the Label

In this study we replicated the explanatory effect of a label which had been found by Giffin et al. (2017). In their experiments, they used vignettes describing an odd behavior of a person based on culturally specific disorders that were unfamiliar to respondents. It turned out that explanations which explain an odd behavior through a person’s tendency to behave that way (circulus vitiosus) seemed more persuasive if the disorder was given a label that was used in the explanation. We replicated these results in Experiment 1, and in a follow-up Experiment 2 we examined the familiarity with category information and the evaluation of that category over time (the delay lasted one week). We realized that the label effect persists even when people make judgments based on their recollections about a category. Furthermore, according to a content analysis of the recollections, participants in the label condition remembered more information from the vignettes but tended to forget an artificial label; however, they used other words from the disorder domain instead (like “disease” or “kleptomania”). This allowed us to suggest a new interpretation of this effect: we suppose that in the Giffin et al. (2017) experiments the label did not bring any new features to a category itself, but pointed to a relevant domain instead, so the effect appeared from the activation of areas of knowledge in semantic memory and the application of relevant schema for learning a new phenomenon.

Debugging Classical Ontologies Using Defeasible Reasoning Tools

A successful application of ontologies relies on representing as much accurate and relevant domain knowledge as possible, while maintaining logical consistency. As the successful implementation of a real-world ontology is likely to contain many concepts and intricate relationships between the concepts, it is necessary to follow a methodology for debugging and refining the ontology. Many ontology debugging approaches have been developed to help the knowledge engineer pinpoint the cause of logical inconsistencies and rectify them in a strategic way. We show that existing debugging approaches can lead to unintuitive results, which may lead the knowledge engineer to opt for deleting potentially crucial and nuanced knowledge. We provide a methodological and design foundation for weakening faulty axioms in a strategic way using defeasible reasoning tools. Our methodology draws from Rodler’s interactive ontology debugging approach and extends this approach by creating a methodology to systematically find conflict resolution recommendations. Importantly, our goal is not to convert a classical ontology to a defeasible ontology. Rather, we use the definition of exceptionality of a concept, which is central to the semantics of defeasible description logics, and the associated algorithm to determine the extent of a concept’s exceptionality (their ranking); then, starting with the statements containing the most general concepts (the least exceptional concepts) weakened versions of the original statements are constructed; this is done until all inconsistencies have been resolved.

Bayesian inference of multi-sensors impedance cardiography for detection of aortic dissection

Purpose This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients. Design/methodology/approach A 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification. Findings The Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection. Originality/value This paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.

A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top n matches from viewed sketches to a given photo. F is again used to formulate a feature map based on the cosine distance of a candidate sketch formed by C from the top n matches. A customized CNN configuration (called the discriminator D) then computes loss functions based on differences between the candidate sketch and the feature. Values of these loss functions alternately update C and F. The ensemble of these nets is trained and tested on selected datasets, including CUFS, CUFSF, and a part of the IIT photo–sketch dataset. Results of this modified U-Net are acquired by the legacy NLDA (1998) scheme of face recognition and its newer version, OpenBR (2013), which demonstrate an improvement of 5% compared with the current state of the art in its relevant domain.

Effect of a novel healthcare leadership program on leadership and emotional intelligence

The purpose of this study was to assess the effectiveness of the Feagin Leadership Program (FLP) in teaching leadership domains and emotional intelligence. An anonymous survey of 178 graduates of FLP (2011–2019) including the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) was used to assess emotional intelligence and program views. ANOVA was used to compare the difference in emotional intelligence domains between groups. Respondents reported the FLP most improved skills in communication, emotional intelligence, and team building. Medical students (18, 38.3%) and faculty/staff (5/14, 35.7%) reported the most relevant domain was emotional intelligence; residents/fellows reported the most relevant domain was teamwork (8/37, 21.6%). Respondents in residency/fellowship had the highest score in emotionality (P = .01). These results suggest that a healthcare leadership program tailored to medical trainees was effective in improving their competency in various leadership domains, and that emotional intelligence and teamwork were the most relevant components of the program.

Approaching deterministic and probabilistic truth: a unified account

The basic problem of a theory of truth approximation is defining when a theory is “close to the truth” about some relevant domain. Existing accounts of truthlikeness or verisimilitude address this problem, but are usually limited to the problem of approaching a “deterministic” truth by means of deterministic theories. A general theory of truth approximation, however, should arguably cover also cases where either the relevant theories, or “the truth”, or both, are “probabilistic” in nature. As a step forward in this direction, we first present a general characterization of both deterministic and probabilistic truth approximation; then, we introduce a new account of verisimilitude which provides a simple formal framework to deal with such issue in a unified way. The connections of our account with some other proposals in the literature are also briefly discussed.

Morphology-phonology interplay in lexical stress assignment: Ichishkiin Sɨnwit

This paper presents a novel analysis of the stress system of Ichishkiin Sɨnwit (Sahaptian). Ichishkiin Sɨnwit has been previously analyzed as a unique example of a stress system requiring a ranking of the Affix Faithfulness constraints over the Root Faithfulness constraints. I argue, however, that such idiosyncratic stress mechanisms are not necessary. Instead, I propose that accent assignment is cyclic: Underlying accent in the outermost derivational layer within the relevant domain wins. A central role in this analysis belongs to (i) the underlying specification of morphemes for accent, and to (ii) morpho-prosodic domains. The current proposal additionally offers an insight into the role of morpho-prosodic domains in the hiatus resolution strategies.

Educing knowledge from text: semantic information extraction of spatial concepts and places

A growing body of geospatial research has shifted the focus from fully structured to semistructured and unstructured content written in natural language. Natural language texts provide a wealth of knowledge about geospatial concepts, places, events, and activities that needs to be extracted and formalized to support semantic annotation, knowledge-based exploration, and semantic search. The paper presents a web-based prototype for the extraction of geospatial entities and concepts, and the subsequent semantic visualization and interactive exploration of the extraction results. A lightweight ontology anchored in natural language guides the interpretation of natural language texts and the extraction of relevant domain knowledge. The approach is applied on three heterogeneous sources which provide a wealth of spatial concepts and place names.

Structural Constraint of Osteopontin Facilitates Efficient Binding to CD44

Since the original description in 1996, the interaction between the cytokine osteopontin (OPN) and the homing receptor CD44 has been extensively studied in cancer, inflammation, bone remodeling, and various other conditions. Alternative splicing and extensive posttranslational modifications by both binding partners, as well as the possibility for lateral recruitment of additional membrane receptors or soluble co-ligands into a complex have left the exact molecular requirements for high-affinity OPN-CD44 binding unresolved. We now report that there is a moderate engagement between the unmodified molecules, which results in curved double-reciprocal plots for OPN titration, suggesting the existence of two binding sites or two binding conformations. Structural constraint of OPN, by immobilization or by addition of heparin, is required for its strong ligation of CD44. Prior literature provides evidence that heparin binding to OPN prompts the unfolding of a core element in the protein. This conformational adjustment may be essential for efficient CD44 interaction. The integrin α9β1 seems to compete with the OPN-CD44 engagement, while the integrin αVβ3 reflects additive binding, suggesting that the CD44 contact sites on OPN are downstream of the RGD motif but overlap with the SVVYGLR domain. Hyaluronate has no effect, placing the relevant domain on CD44 downstream of the N-terminus.