Ying Shen

An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription

BACKGROUNDnThe available antibiotic decision-making systems were developed from a physicians perspective. However, because infectious diseases are common, many patients desire access to knowledge via a search engine. Although the use of antibiotics should, in principle, be subject to a doctors advice, many patients take them without authorization, and some people cannot easily or rapidly consult a doctor. In such cases, a reliable antibiotic prescription support system is needed.nnnMETHODS AND RESULTSnThis study describes the construction and optimization of the sensitivity and specificity of a decision support system named IDDAP, which is based on ontologies for infectious disease diagnosis and antibiotic therapy. The ontology for this system was constructed by collecting existing ontologies associated with infectious diseases, syndromes, bacteria and drugs into the ontologys hierarchical conceptual schema. First, IDDAP identifies a potential infectious disease based on a patients self-described disease state. Then, the system searches for and proposes an appropriate antibiotic therapy specifically adapted to the patient based on factors such as the patients body temperature, infection sites, symptoms/signs, complications, antibacterial spectrum, contraindications, drug-drug interactions between the proposed therapy and previously prescribed medication, and the route of therapy administration. The constructed domain ontology contains 1,267,004 classes, 7,608,725 axioms, and 1,266,993 members of SubClassOf that pertain to infectious diseases, bacteria, syndromes, anti-bacterial drugs and other relevant components. The system includes 507 infectious diseases and their therapy methods in combination with 332 different infection sites, 936 relevant symptoms of the digestive, reproductive, neurological and other systems, 371 types of complications, 838,407 types of bacteria, 341 types of antibiotics, 1504 pairs of reaction rates (antibacterial spectrum) between antibiotics and bacteria, 431 pairs of drug interaction relationships and 86 pairs of antibiotic-specific population contraindicated relationships. Compared with the existing infectious disease-relevant ontologies in the field of knowledge comprehension, this ontology is more complete. Analysis of IDDAPs performance in terms of classifiers based on receiver operating characteristic (ROC) curve results (89.91%) revealed IDDAPs advantages when combined with our ontology.nnnCONCLUSIONS AND SIGNIFICANCEnThis study attempted to bridge the patient/caregiver gap by building a sophisticated application that uses artificial intelligence and machine learning computational techniques to perform data-driven decision-making at the point of primary care. The first level of decision-making is conducted by the IDDAP and provides the patient with a first-line therapy. Patients can then make a subjective judgment, and if any questions arise, should consult a physician for subsequent decisions, particularly in complicated cases or in cases in which the necessary information is not yet available in the knowledge base.

Knowledge-aware Attentive Neural Network for Ranking Question Answer Pairs

Ranking question answer pairs has attracted increasing attention recently due to its broad applications such as information retrieval and question answering (QA). Significant progresses have been made by deep neural networks. However, background information and hidden relations beyond the context, which play crucial roles in human text comprehension, have received little attention in recent deep neural networks that achieve the state of the art in ranking QA pairs. In the paper, we propose KABLSTM, a Knowledge-aware Attentive Bidirectional Long Short-Term Memory, which leverages external knowledge from knowledge graphs (KG) to enrich the representational learning of QA sentences. Specifically, we develop a context-knowledge interactive learning architecture, in which a context-guided attentive convolutional neural network (CNN) is designed to integrate knowledge embeddings into sentence representations. Besides, a knowledge-aware attention mechanism is presented to attend interrelations between each segments of QA pairs. KABLSTM is evaluated on two widely-used benchmark QA datasets: WikiQA and TREC QA. Experiment results demonstrate that KABLSTM has robust superiority over competitors and sets state-of-the-art.

Feature-enhanced attention network for target-dependent sentiment classification

Abstract In this paper, we propose a Feature-enhanced Attention Network to improve the performance of target-dependent Sentiment classification (FANS). Specifically, we first learn the feature-enhanced word representations by leveraging the unigram features, part of speech features and word position features. Second, we develop an multi-view co-attention network to learn a better multi-view sentiment-aware and target-specific sentence representation via interactively modeling the context words, target words and sentiment words. We conduct experiments to verify the effectiveness of our model on two real-world datasets in both English and Chinese. The experimental results demonstrate that FANS has robust superiority over competitors and sets state-of-the-art.

MedSim: A Novel Semantic Similarity Measure in Bio-medical Knowledge Graphs

We present MedSim, a novel semantic SIMilarity method based on public well-established bio-MEDical knowledge graphs (KGs) and large-scale corpus, to study the therapeutic substitution of antibiotics. Besides hierarchy and corpus of KGs, MedSim further interprets medicine characteristics by constructing multi-dimensional medicine-specific feature vectors. Dataset of 528 antibiotic pairs scored by doctors is applied for evaluation and MedSim has produced statistically significant improvement over other semantic similarity methods. Furthermore, some promising applications of MedSim in drug substitution and drug abuse prevention are presented in case study.

Ontology Evaluation with Path-based Text-aware Entropy Computation

With the rising importance of knowledge exchange, ontologies have become a key technology in the development of shared knowledge models for semantic-driven applications, such as knowledge interchange and semantic integration. Significant progress has been made in the use of entropy to measure the predictability and redundancy of knowledge bases, particularly ontologies. However, the current entropy applications used to evaluate ontologies consider only single-point connectivity rather than path connectivity, assign equal weights to each entity and path, and assume that vertices are static. To address these deficiencies, the present study proposes a Path-based Text-aware Entropy Computation method, PTEC, by considering the path information between different vertices and the textual information within the path to calculate the connectivity path of the whole network and the different weights between various nodes. Information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. An experimental evaluation of three real-world ontologies is performed based on ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of our method. Experimental results demonstrate that PTEC can effectively evaluate ontologies, particularly those in the medical field.

Cross-domain Aspect/Sentiment-aware Abstractive Review Summarization

This study takes the lead to study the aspect/sentiment-aware abstractive review summarization in domain adaptation scenario. The proposed model CASAS (neural attentive model for Cross-domain Aspect/Sentiment-aware Abstractive review Summarization) leverages domain classification task, working on datasets of both source and target domains, to recognize the domain information of texts and transfer knowledge from source domains to target domains. The extensive experiments on Amazon reviews demonstrate that CASAS outperforms the compared methods in both out-of-domain and in-domain setups.

An event summarizing algorithm based on the timeline relevance model in Sina Weibo

Dear editor, Depicting superior punctuality and originality, Weibo has become increasingly critical and influential in China for online information acquisition and sharing. However, very few research has studied Weibo to investigate event summarizing even though most of the published Weibos are event-driven. Besides, we observe that the existing methods are unsuitable for processing tweets (short-texts with no obvious contextual relationships) although extensive researches have successfully extracted summaries from single-longdocuments [1–5]. Thus, effectively summarizing the antecedents and consequences of events from massive tweets is still considered to be challenging. In this study, a timeline relevance model was initially established to estimate the popular period of the event. Further, a summarizing algorithm was designed to mine and summarize the related events. Finally, the experimental results depict the superiority of our summarizing algorithm. Our study can help users to quickly identify and grasp the event that is related to the topic in a limited time frame. Furthermore, these methods can also be applied to other situations, such as multiple short-text summarization and event detection.

EAPB: entropy-aware path-based metric for ontology quality

BackgroundEntropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path.ResultsWe propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes. The information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. EAPB is analytically evaluated against the state-of-the-art criteria. We have performed empirical analysis on real-world medical ontologies and a synthetic ontology based on the following three aspects: ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of the proposed metric. The experimental results show that the proposed EAPB can effectively evaluate ontologies, especially those in the medical informatics field.ConclusionsWe leverage path information and textual information to enrich the network representational learning and aid in entropy computation. The analytics and assessments of semantic web can benefit from the structure information but also the text information. We believe that EAPB is helpful for managing ontology development and evaluation projects. Our results are reproducible and we will release the source code and ontology of this work after publication. (Source code and ontology: https://github.com/AnonymousResearcher1/ontologyEvaluate).

EPICE an emotion fuzzy vectorial space for time modeling in medical decision

EPICE is a decision support system in medicine several knowledge bases that must cooperate together. The model takes into account the emotions of the patient and the physician involved in the care relationship during the evolution of the disease of the patient. The knowledge of clinical pictures are depicted with an object oriented time clinical model while the care relationship : the emotions of the patient and the caregivers are based on a psychological model. Both models rely on a fuzzy vectorial space that avoids fuzzification and denazification steps of the rule-based approaches and allows time modeling. We propose a fuzzy vectorial space to model the emotion felt by the patient during his care course and the relationship with the caregivers.

Attention-Aware Path-Based Relation Extraction for Medical Knowledge Graph.

The task of entity relation extraction discovers new relation facts and enables broader applications of knowledge graph. Distant supervision is widely adopted for relation extraction, which requires large amounts of texts containing entity pairs as training data. However, in some specific domains such as medical-related applications, entity pairs that have certain relations might not appear together, thus it is difficult to meet the requirement for distantly supervised relation extraction. In the light of this challenge, we propose a novel path-based model to discover new entity relation facts. Instead of finding texts for relation extraction, the proposed method extracts path-only information for entity pairs from the current knowledge graph. For each pair of entities, multiple paths can be extracted, and some of them are more useful for relation extraction than others. In order to capture this observation, we employ attention mechanism to assign different weights for different paths, which highlights the useful paths for entity relation extraction. To demonstrate the effectiveness of the proposed method, we conduct various experiments on a large-scale medical knowledge graph. Compared with the state-of-the-art relation extraction methods using the structure of knowledge graph, the proposed method significantly improves the accuracy of extracted relation facts and achieves the best performance.