Feichen Shen

Clinical information extraction applications: A literature review

BACKGROUND With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text. OBJECTIVES In this literature review, we present a review of recent published research on clinical information extraction (IE) applications. METHODS A literature search was conducted for articles published from January 2009 to September 2016 based on Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and ACM Digital Library. RESULTS A total of 1917 publications were identified for title and abstract screening. Of these publications, 263 articles were selected and discussed in this review in terms of publication venues and data sources, clinical IE tools, methods, and applications in the areas of disease- and drug-related studies, and clinical workflow optimizations. CONCLUSIONS Clinical IE has been used for a wide range of applications, however, there is a considerable gap between clinical studies using EHR data and studies using clinical IE. This study enabled us to gain a more concrete understanding of the gap and to provide potential solutions to bridge this gap.

Exploring the pharmacogenomics knowledge base (PharmGKB) for repositioning breast cancer drugs by leveraging Web ontology language (OWL) and cheminformatics approaches.

Computational drug repositioning leverages computational technology and high volume of biomedical data to identify new indications for existing drugs. Since it does not require costly experiments that have a high risk of failure, it has attracted increasing interest from diverse fields such as biomedical, pharmaceutical, and informatics areas. In this study, we used pharmacogenomics data generated from pharmacogenomics studies, applied informatics and Semantic Web technologies to address the drug repositioning problem. Specifically, we explored PharmGKB to identify pharmacogenomics related associations as pharmacogenomics profiles for US Food and Drug Administration (FDA) approved breast cancer drugs. We then converted and represented these profiles in Semantic Web notations, which support automated semantic inference. We successfully evaluated the performance and efficacy of the breast cancer drug pharmacogenomics profiles by case studies. Our results demonstrate that combination of pharmacogenomics data and Semantic Web technology/Cheminformatics approaches yields better performance of new indication and possible adverse effects prediction for breast cancer drugs.

A pervasive framework for real-time activity patterns of mobile users

Given the rise of ubiquitous computing and communication devices like biosensors, smart watch, and smartphones, real-time online systems can provide users with a wide range of supports including monitoring daily activities and retrieving of personal data. User activity pattern can give an abstraction and summarization about physical behavior for certain group of people. However, one of the biggest challenges in this topic that we are facing today is the big data problem associated with large, complex, and dynamic data. In addition, as the demand for the integration and analysis of dynamic data as well as static historical data from different sources has been growing steadily, smartphones with limited capacity and computing abilities can hardly manage and process such a huge task. To address these above issues, a new framework has to be used to assist in the process, analysis, and integration of big data for a mobile platform. In this paper, I propose a distributed cloud based pervasive framework to help do complicated computing for a mobile platform. The framework has the ability to collect, process, analyze, and integrate different types of data from different sources by using state-of-the-art technologies. The purpose of this framework is to provide an intelligent and efficient approach to analyze and combine new incoming data with historical data to build and refine a solid user activity pattern.

Knowledge Discovery from Biomedical Ontologies in Cross Domains.

In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies.

PEMAR: A pervasive middleware for activity recognition with smart phones

The growing affordability of smart phones and mobile devices has only added to this trend by encouraging prolonged durations of inactivity. In this paper, we present a middleware, called the Pervasive Middleware for Activity Recognition (PEMAR) that aims to increase the level of physical activity by creating a middleware for active games on mobile devices. For the PEMAR application, we present a human centered and adaptive approach that recognizes and learns human activities continuously by employing an activity library. The activity models in the library will be annotated with patterns of human activities and their contexts for general usage of activity models. This will be beneficial to many pervasive applications in terms of the availability of the accurate activity models as well as the reduction of burden for gesture training. The PEMAR middleware is composed of the following: (1) semantic models for human activity, (2) activity analysis, (3) activity recognition, (4) adaptation of motion models, and (5) motion based game applications. We evaluate the proposed PEMAR model in terms of its recognition accuracy and performance. In addition, we demonstrate the usage of the middleware through interactive activity gaming applications.

Using semantic web technologies for quality measure phenotyping algorithm representation and automatic execution on EHR data

In this paper, we introduce our efforts on an application of semantic web technologies to phenotyping algorithms in Electronic Health Records (EHR) data for the purpose of facilitating the reasoning and inferring processes of some patients groups in an intelligent manner.

An integrative computational approach to identify disease-specific networks from PubMed literature information

A huge amount of association relationships among biological entities (e.g., diseases, drugs, and genes) are scattered in biomedical literature. How to extract and analyze such heterogeneous data still remains a challenging task for most researchers in the biomedical field. Natural language processing (NLP) has the potential in extracting associations among biological entities from literature. However, association information extracted through NLP can be large, noisy, and redundant which poses significant challenges to biomedical researchers to use such information. To address this challenge, we propose a computational framework to facilitate the use of NLP results. We apply Latent Dirichlet Allocation (LDA) to discover topics based on associations. The networks extracted from each topic provide a disease-specific network for downstream bioinformatics analysis of associations for each topic. We illustrated the framework through the construction of disease-specific networks from Semantic MEDLINE, an NLP-generated association database, followed by the analysis of network properties, such as hub nodes and degree distribution. The results demonstrate that (1) LDA-based approach can group related diseases into the same disease topic; (2) the disease-specific association network follows the scale-free network property, in which hub nodes are enriched in related diseases, genes and drugs.

Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery

In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.

Phenotyping on EHR data using OWL and semantic web technologies

Objective: In this paper, we introduce our efforts on using semantic web technologies to execute phenotyping algorithms on Electronic Health Records (EHR) data.

Systematic identification of latent disease-gene associations from PubMed articles

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.