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Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository

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JOURNAL OF BIOMEDICAL INFORMATICS 2017年 69卷 259-266页

作者： Wang, Liqin Haug, Peter J. Del Fiol, Guilherme Univ Utah Dept Biomed Informat 421 Wakara WaySuite 140 Salt Lake City UT 84108 USA Intermt Healthcare Homer Warner Res Ctr 5121 South Cottonwood St Murray UT 84107 USA

Objective: Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance: and it would be expensive to ask human experts to manually review a large number of associations. We propose that machine-learning-based classification can be used to separate the signal from the noise, and to provide a feasible approach to create and maintain disease-specific vocabularies. Method: We initially focused on disease-medication associations for the purpose of simplicity. For a disease of interest, we extracted potentially treatment-related drug concepts from biomedical literature citations and from a local clinical data repository. Each concept was associated with multiple measures of relevance (i.e., features) such as frequency of occurrence. For the machine purpose of learning, we formed nine datasets for three diseases with each disease having two single-source datasets and one from the combination of previous two datasets. All the datasets were labeled using existing reference standards. Thereafter, we conducted two experiments: (1) to test if adding features from the clinical data repository would improve the performance of classification achieved using features from the biomedical literature only, and (2) to determine if classifier(s) trained with known medication-disease data sets would be generalizable to new disease(s). Results: Simple logistic regression and LogitBoost were two classifiers identified as the preferred models separately for the biomedical-literature datasets and combined datasets. The performance of the classification using combined features provided significant improvem

关键词： Machine learning Classification Biomedical literature clinical data repository Ontology Disease-specific vocabulary

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An Exploratory Study of a NoSQL database for a clinical data repository 8th

An Exploratory Study of a NoSQL Database for a Clinical Data...

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World Conference on Information Systems and Technologies (WorldCIST)

作者： Hak, Francini Guimaraes, Tiago Abelha, Antonio Santos, Manuel Univ Minho Algoritmi Res Ctr Braga Portugal

ISBN: (纸本)9783030456962;9783030456979

The need to implement a distributed clinical data repository (CDR) at a healthcare facility, rose in large part due to the high volume of data and the discrepancy of their sources. Over the years, Relational database Management Systems (RDBMS) began to present difficulties in responding to the needs of various organizations when it comes to manipulating a large amount of data and to its scalability. Therefore, it was necessary to explore other techniques to choose the appropriate technology to build the CDR. In this way, NoSQL emerged as a new type of database that is quite useful to work with multiple and different types of data. In addition, NoSQL introduces a number of user-friendly features such as a distributed, scalable, elastic and also fault tolerant system. In this way, Oracle NoSQL database was the NoSQL solution chosen to develop this case study, using the key-value storage. This article was motivated to propose a CDR architecture based on Oracle NoSQL database functionalities. A one-single node database was deployed for better comprehension, in order to enhance their features for future implementation.

关键词： clinical data repository Key-value NoSQL Oracle NoSQL

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An Implementation of clinical data repository with openEHR Approach: From data Modeling to Architecture 24

An Implementation of Clinical Data Repository with openEHR A...

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24th Australian National Health Informatics Conference (HIC)

作者： Min, Lingtong Liu, Juanjian Lu, Xudong Duan, Huilong Qiao, Qingzhi Zhejiang Univ Coll Biomed Engn & Instrument Sci Hangzhou Zhejiang Peoples R China Shanxi Dayi Hosp Dept Informat Technol Taiyuan Shanxi Peoples R China

ISBN: (纸本)9781614996668;9781614996651

Since electronic healthcare records are widely implemented in the hospitals, the explosion of the clinical data brings great opportunities to secondary use. clinical data repository (CDR) plays an important role in clinical data using, including healthcare service, research and management. With evolving of clinical knowledge, it is a challenge for CDR to meet more and more requirements with adaptability and flexibility. In order to overcome this challenge, this paper proposed a solution that implemented a CDR with openEHR approach from data model to implementation. This study modelled archetypes according actual CDR requirements and implemented a CDR system in a tertiary hospital. Furthermore, this study developed data applications to facilitate healthcare service and research based on the CDR. The results of CDR system and data application demonstrate that openEHR approach could meet the continually evolving need of systems.

关键词： clinical data repository openEHR data model implementation

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Case Study: Applying OpenEHR Archetypes to a clinical data repository in a Chinese Hospital 15

Case Study: Applying OpenEHR Archetypes to a Clinical Data R...

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15th World Congress on Health and Biomedical Informatics (MEDINFO)

作者： Min, Lingtong Wang, Li Lu, Xudong Duan, Huilong Zhejiang Univ Coll Biomed Engn & Instrument Sci Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781614995647;9781614995630

openEHR is a flexible and scalable modeling methodology for clinical information and has been widely adopted in Europe and Australia. Due to the reasons of differences in clinical process and management, there are few research projects involving openEHR in China. To investigate the feasibility of openEHR methodology for clinical information modelling in China, this paper carries out a case study to apply openEHR archetypes to clinical data repository (CDR) in a Chinese hospital. The results show that a set of 26 archetypes are found to cover all the concepts used in the CDR. Of all these, 9 (34.6%) are reused without change, 10 are modified and/or extended, and 7 are newly defined. The reasons for modification, extension and newly definition have been discussed, including granularity of archetype, metadata-level versus data-level modelling, and the representation of relationships between archetypes.

关键词： Archetype openEHR clinical data repository Chinese hospital

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Multisite Semiautomated clinical data repository for Duplication 15q Syndrome: Study Protocol and Early Uses

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JMIR RESEARCH PROTOCOLS 2017年第10期6卷 e194页

作者： Ajayi, Oluwaseun Jessica Smith, Ebony Jeannae Viangteeravat, Teeradache Huang, Eunice Y. Nagisetty, Naga Satya V. Rao Urraca, Nora Lusk, Laina Finucane, Brenda Arkilo, Dimitrios Young, Jennifer Jeste, Shafali Thibert, Ronald Reiter, Lawrence T. Le Bonheur Childrens Hosp Childrens Fdn Res Inst Biomed Informat Core Memphis TN USA Univ Tennessee Hlth Sci Ctr Dept Pediat Memphis TN USA Univ Tennessee Hlth Sci Ctr Dept Surg Memphis TN USA Le Bonheur Childrens Hosp Pediat Clin Res Unit Memphis TN USA Geisinger Hlth Syst Autism & Dev Med Inst Lewisburg PA USA Childrens Hosp & Clin Minnesota Minnesota Epilepsy Grp St Paul MN USA Univ Calif Los Angeles Ctr Autism Res & Treatment Dept Neurol Los Angeles CA 90095 USA Harvard Med Sch Massachusetts Gen Hosp Dept Neurol Boston MA USA Dup15q Alliance Highland Pk IL USA Univ Tennessee Hlth Sci Ctr Dept Neurol Link 415855 Monroe Ave Memphis TN 38163 USA Dup15q Alliance Fayetteville NY USA

Background: Chromosome 15q11.2-q13.1 duplication syndrome (Dup15q syndrome) is a rare disorder caused by duplications of chromosome 15q11.2-q13.1, resulting in a wide range of developmental disabilities in affected individuals. The Dup15q Alliance is an organization that provides family support and promotes research to improve the quality of life of patients living with Dup15q syndrome. Because of the low prevalence of this condition, the establishment of a single research repository would have been difficult and more time consuming without collaboration across multiple institutions. Objective: The goal of this project is to establish a national deidentified database with clinical and survey information on individuals diagnosed with Dup15q syndrome. Methods: The development of a multiclinic site repository for clinical and survey data on individuals with Dup15q syndrome was initiated and supported by the Dup15q Alliance. Using collaborative workflows, communication protocols, and stakeholder engagement tools, a comprehensive database of patient-centered information was built. Results: We successfully established a self-report populating, centralized repository for Dup15q syndrome research. This repository also resulted in the development of standardized instruments that can be used for other studies relating to developmental disorders. By standardizing the data collection instruments, it allows us integrate our data with other national databases, such as the National database for Autism Research. A substantial portion of the data collected from the questionnaires was facilitated through direct engagement of participants and their families. This allowed for a more complete set of information to be collected with a minimal turnaround time. Conclusions: We developed a repository that can efficiently be mined for shared clinical phenotypes observed at multiple clinic sites and used as a springboard for future clinical and basic research studies.

关键词： clinical data repository Dup15q syndrome patient-centered clinical phenotypes cohort discovery

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Automated population of an i2b2 clinical data warehouse from an openEHR-based data repository

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JOURNAL OF BIOMEDICAL INFORMATICS 2016年 63卷 277-294页

作者： Haarbrandt, Birger Tute, Erik Marschollek, Michael Tech Univ Carolo Wilhelmina Braunschweig Peter L Reichertz Inst Med Informat Inst Technol Carl Neuberg Str 1 D-30625 Hannover Germany Hannover Med Sch Carl Neuberg Str 1 D-30625 Hannover Germany

Background: Detailed clinical Model (DCM) approaches have recently seen wider adoption. More specifically, openEHR-based application systems are now used in production in several countries, serving diverse fields of application such as health information exchange, clinical registries and electronic medical record systems. However, approaches to efficiently provide openEHR data to researchers for secondary use have not yet been investigated or established. Methods: We developed an approach to automatically load openEHR data instances into the open source clinical data warehouse i2b2. We evaluated query capabilities and the performance of this approach in the context of the Hanover Medical School Translational Research Framework (HaMSTR), an openEHR-based data repository. Results: Automated creation of i2b2 ontologies from archetypes and templates and the integration of openEHR data instances from 903 patients of a paediatric intensive care unit has been achieved. In total, it took an average of similar to 2527 s to create 2.311.624 facts from 141.917 XML documents. Using the imported data, we conducted sample queries to compare the performance with two openEHR systems and to investigate if this representation of data is feasible to support cohort identification and record level data extraction. Discussion: We found the automated population of an i2b2 clinical data warehouse to be a feasible approach to make openEHR data instances available for secondary use. Such an approach can facilitate timely provision of clinical data to researchers. It complements analytics based on the Archetype Query Language by allowing querying on both, legacy clinical data sources and openEHR data instances at the same time and by providing an easy-to-use query interface. However, due to different levels of expressiveness in the data models, not all semantics could be preserved during the ETL process. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Secondary use openEHR i2b2 Detailed clinical models Archetypes clinical information systems data warehouse clinical data repository Healthcare analytics

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data mining and clinical data repositories: Insights from a 667,000 patient data set

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COMPUTERS IN BIOLOGY AND MEDICINE 2006年第12期36卷 1351-1377页

作者： Mullins, Irene M. Siadaty, Mir S. Lyman, Jason Scully, Ken Garrett, Carleton T. Miller, W. Greg Muller, Rudy Robson, Barry Apte, Chid Weiss, Sholom Rigoutsos, Isidore Platt, Daniel Cohen, Simona Knaus, William A. Univ Virginia Hlth Syst Dept Publ Hlth Sci Charlottesville VA USA Virginia Commonwealth Univ Dept Pathol Richmond VA USA IBM Corp Thomas J Watson Res Ctr IBM Life Sci Yorktown Hts NY 10598 USA IBM Res Haifa Israel

clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as health care systems integrate patient information for research and utilization objectives. To investigate the potential value of searching these databases for novel insights, we applied a new data mining approach, HealthMiner (R), to a large cohort of 667,000 inpatient and outpatient digital records from an academic medical system. HealthMiner (R) approaches knowledge discovery using three unsupervised methods: CliniMiner (R), Predictive Analysis, and Pattern Discovery. The initial results from this study suggest that these approaches have the potential to expand research capabilities through identification of potentially novel clinical disease associations. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： clinical data repository complex data sets large patient cohort FANO HealthMiner (R) search tools

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An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2018年第Jul.期89卷 10-23页

作者： Wulff, Antje Haarbrandt, Birger Tute, Erik Marschollek, Michael Beerbaum, Philipp Jack, Thomas Tech Univ Carolo Wilhelmina Braunschweig Inst Technol Peter L Reichertz Inst Med Informat Carl Neuberg Str 1 D-30625 Hannover Germany Hannover Med Sch Carl Neuberg Str 1 D-30625 Hannover Germany Hannover Med Sch Dept Pediat Cardiol & Intens Care Med Hannover Germany

Background: clinical decision-support systems (CDSS) are designed to solve knowledge-intensive tasks for supporting decision-making processes. Although many approaches for designing CDSS have been proposed, due to high implementation costs, as well as the lack of interoperability features, current solutions are not well established across different institutions. Recently, the use of standardized formalisms for knowledge representation as terminologies as well as the integration of semantically enriched clinical information models, as openEHR Archetypes, and their reuse within CDSS are theoretically considered as key factors for reusable CDSS. Objective: We aim at developing and evaluating an openEHR based approach to achieve interoperability in CDSS by designing and implementing an exemplary system for automated systemic inflammatory response syndrome (SIRS) detection in pediatric intensive care. Methods: We designed an interoperable concept, which enables an easy integration of the CDSS across different institutions, by using openEHR Archetypes, terminology bindings and the Archetype Query Language (AQL). The practicability of the approach was tested by (1) implementing a prototype, which is based on an openEHR based data repository of the Hannover Medical School (HaMSTR), and (2) conducting a first pilot study. Results: We successfully designed and implemented a CDSS with interoperable knowledge bases and interfaces by reusing internationally agreed-upon Archetypes, incorporating LOINC terminology and creating AQL queries, which allowed retrieving dynamic facts in a standardized and unambiguous form. The technical capabilities of the system were evaluated by testing the prototype on 16 randomly selected patients with 129 days of stay, and comparing the results with the assessment of clinical experts (leading to a sensitivity of 1.00, a specificity of 0.94 and a Cohen's kappa of 0.92). Conclusions: We found the use of openEHR Archetypes and AQL a feasible approach

关键词： clinical decision support systems Pediatrics Health information interoperability openEHR Archetypes clinical data repository Systemic inflammatory response syndrome

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MIDTERM REPORT ON THE ARDEN-SYNTAX IN A clinical EVENT MONITOR

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COMPUTERS IN BIOLOGY AND MEDICINE 1994年第5期24卷 377-383页

作者： LUDEMANN, P AMERITECH KNOWLEDGE DATA INST MED INFORMATPALO ALTOCA 94301

In implementing a clinical event monitor (CEM), a decision-support system, we worked with an existing repository of clinical data (Keystone), fed from ancillary systems using HL7. The rules are written in the Arden Syntax, an ASTM standard for expressing medical knowledge as medical logic modules (MLMs). The Arden Syntax leaves unspecified the clinical data model and deductive database access language;we briefly describe our query language and the related medical concepts dictionary (MCD). This paper gives an overview of our implementation of the Arden Syntax, the MCD and the deductive database access language, with reasons for the major design decisions. Overall, less than a quarter of the development effort has gone into the Arden compiler and interpreter;the rest has focused on accessing the data and integrating with other systems. We feel that the Arden Syntax has proved its worth in writing rules;effort should now be focused on medical vocabularies and data models.

关键词： ARDEN SYNTAX COMPILER OPTIMIZER INTERPRETER MEDICAL LOGIC MODULE (MLM) clinical data repository EXPERT SYSTEM KNOWLEDGEBASE data MODEL DEDUCTIVE dataBASE data ACCESS LANGUAGE MEDICAL VOCABULARY

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Informatics methods to enable sharing of quantitative imaging research data

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MAGNETIC RESONANCE IMAGING 2012年第9期30卷 1249-1256页

作者： Levy, Mia A. Freymann, John B. Kirby, Justin S. Fedorov, Andriy Fennessy, Fiona M. Eschrich, Steven A. Berglund, Anders E. Fenstermacher, David A. Tan, Yongqiang Guo, Xiaotao Casavant, Thomas L. Brown, Bartley J. Braun, Terry A. Dekker, Andre Roelofs, Erik Mountz, James M. Boada, Fernando Laymon, Charles Oborski, Matt Rubin, Daniel L. Vanderbilt Univ Dept Biomed Informat & Med Div Hematol & Oncol Sch Med Nashville TN 37232 USA SAIC Frederick Inc NCI Frederick Clin Res Directorate CMRP Ft Detrick MD 21702 USA Harvard Univ Brigham & Womens Hosp Sch Med Boston MA 02115 USA Univ S Florida Coll Med H Lee Moffitt Canc Ctr & Res Inst Tampa FL 33612 USA Columbia Univ New York NY USA Univ Iowa Holden Comprehens Canc Ctr Ctr Bioinformat & Computat Biol Iowa City IA USA Maastricht Univ Med Ctr Dept Radiat Oncol MAASTRO GROW Sch Oncol & Dev Biol Maastricht Netherlands Univ Pittsburgh Pittsburgh PA USA Stanford Univ Dept Radiol & Med Biomed Informat Res Sch Med Stanford CA 94305 USA

Introduction: The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community. Methods: We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN. Results: There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network. Conclusions: As a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel imaging biomarkers. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Quantitative Imaging Network data sharing Imaging informatics Research informatics Image repository Image meta-data repository clinical data repository System architecture

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