Identification of related information of interest across free text documents

2011 ◽  
Author(s):  
James R. Johnson ◽  
Anita Miller ◽  
Latifur Khan ◽  
Bhavani Thuraisingham ◽  
Murat Kantarcioglu
Keyword(s):  
Free Text ◽  
Text Documents ◽  
Related Information

scholarly journals Representation of Information about Family Relatives as Structured Data in Electronic Health Records

2014 ◽  
Vol 05 (02) ◽  
pp. 349-367 ◽  
Author(s):  
Y. Lu ◽  
C.J. Vitale ◽  
P.L. Mar ◽  
F. Chang ◽  
N. Dhopeshwarkar ◽  
...  
Keyword(s):  
Family History ◽  
Structured Data ◽  
Free Text ◽  
Snomed Ct ◽  
Family History Information ◽  
Text Documents ◽  
Health Records ◽  
Relative Information ◽  
History Information ◽  
Electronic Health

SummaryBackground: The ability to manage and leverage family history information in the electronic health record (EHR) is crucial to delivering high-quality clinical care.Objectives: We aimed to evaluate existing standards in representing relative information, examine this information documented in EHRs, and develop a natural language processing (NLP) application to extract relative information from free-text clinical documents.Methods: We reviewed a random sample of 100 admission notes and 100 discharge summaries of 198 patients, and also reviewed the structured entries for these patients in an EHR system’s family history module. We investigated the two standards used by Stage 2 of Meaningful Use (SNOMED CT and HL7 Family History Standard) and identified coverage gaps of each standard in coding relative information. Finally, we evaluated the performance of the MTERMS NLP system in identifying relative information from free-text documents.Results: The structure and content of SNOMED CT and HL7 for representing relative information are different in several ways. Both terminologies have high coverage to represent local relative concepts built in an ambulatory EHR system, but gaps in key concept coverage were detected; coverage rates for relative information in free-text clinical documents were 95.2% and 98.6%, respectively. Compared to structured entries, richer family history information was only available in free-text documents. Using a comprehensive lexicon that included concepts and terms of relative information from different sources, we expanded the MTERMS NLP system to extract and encode relative information in clinical documents and achieved a corresponding precision of 100% and recall of 97.4%.Conclusions: Comprehensive assessment and user guidance are critical to adopting standards into EHR systems in a meaningful way. A significant portion of patients’ family history information is only documented in free-text clinical documents and NLP can be used to extract this information.Citation: Zhou L, Lu Y, Vitale CJ, Mar PL, Chang F, Dhopeshwarkar N, Rocha RA. Representation of information about family relatives as structured data in electronic health records. Appl Clin Inf 2014; 5: 349–367 http://dx.doi.org/10.4338/ACI-2013-10-RA-0080

A Comprehensive Framework Towards Information Sharing Between Government Agencies

2009 ◽  
pp. 43-59
Author(s):  
Akhilesh Bajaj ◽  
Sudha Ram
Keyword(s):  
Information Sharing ◽  
Data Storage ◽  
Heterogeneous Data ◽  
Information Storage ◽  
Government Agencies ◽  
Free Text ◽  
Text Documents ◽  
Shared Information ◽  
Share Data ◽  
Reducing Costs

Recently, there has been increased interest in sharing digitized information between government agencies, with the goals of improving security, reducing costs, and offering better quality service to users of government services. The bulk of previous work in interagency information sharing has focused largely on the sharing of structured information among heterogeneous data sources, whereas government agencies need to share data with varying degrees of structure ranging from free text documents to relational data. In this work, we explore the different technologies available to share information. Specifically, our framework discusses the optional data storage mechanisms required to support a Service Oriented Architecture (SOA). We compare XML document, free text search engine, and relational database technologies and analyze the pros and cons of each approach. We explore these options along the dimensions of information definition, information storage, the access to this information, and finally the maintenance of shared information.

scholarly journals Communication through the electronic health record: frequency and implications of free text orders

JAMIA Open ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 154-159
Author(s):  
Swaminathan Kandaswamy ◽  
Aaron Z Hettinger ◽  
Daniel J Hoffman ◽  
Raj M Ratwani ◽  
Jenna Marquard
Keyword(s):  
Data Entry ◽  
Clinical Decision ◽  
Error Rates ◽  
Asynchronous Communication ◽  
Free Text ◽  
Future Studies ◽  
Safety Issues ◽  
Inaccurate Information ◽  
Related Information ◽  
Medication Information

Abstract Communication for non-medication order (CNMO) is a type of free text communication order providers use for asynchronous communication about patient care. The objective of this study was to understand the extent to which non-medication orders are being used for medication-related communication. We analyzed a sample of 26 524 CNMOs placed in 6 hospitals. A total of 42% of non-medication orders contained medication information. There was large variation in the usage of CNMOs across hospitals, provider settings, and provider types. The use of CNMOs for communicating medication-related information may result in delayed or missed medications, receiving medications that should have been discontinued, or important clinical decision being made based on inaccurate information. Future studies should quantify the implications of these data entry patterns on actual medication error rates and resultant safety issues.

scholarly journals Automatic Extraction of Lung Cancer Staging Information from Chinese Computed Tomography Reports: Deep Learning Approach (Preprint)

2021 ◽  
Author(s):  
Danqing Hu ◽  
Shaolei Li ◽  
Yuhong Wang ◽  
Huanyao Zhang ◽  
Nan Wu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Staging ◽  
Tnm Staging ◽  
Experimental Results ◽  
Entity Recognition ◽  
Clinical Staging ◽  
Free Text ◽  
Rule Based ◽  
Related Information

BACKGROUND Lung cancer is the leading cause of cancer death worldwide. Clinical staging of lung cancer plays a crucial role in treatment decision making and prognosis evaluation. However, in clinical practice, about one-half of the clinical stages of lung cancer patients are inconsistent with their pathological stages. As one of the most important diagnostic modalities for staging, chest computed tomography reports a wealth of information about cancer staging, but the free-text nature of the reports obstructs their computerized utilization. OBJECTIVE In this paper, we aim to automatically extract the staging-related information from CT reports to support accurate clinical staging. METHODS In this study, we developed an information extraction system to extract the staging-related information from CT reports. The system consisted of three parts, i.e., named entity recognition (NER), relation classification (RC), and question reasoning (QR). We first summarized 22 questions about lung cancer staging based on the TNM staging guideline. And then, two state-of-the-art NER algorithms were implemented to recognize the entities of interest. Next, we presented a novel RC method using the relation constraints to classify the relations between entities. Finally, a rule-based QR module was established to answer all questions by reasoning the results of NER and RC. RESULTS We evaluated the developed IE system on a clinical dataset containing 392 chest CT reports collected from the Department of Thoracic Surgery II of Peking University Cancer Hospital. The experimental results show that the Bi-LSTM-CRF outperforms the ID-CNN-CRF for the NER task with 77.27% and 89.96% macro F1 scores under the exact and inexact matching scheme, respectively. For the RC task, the proposed method, i.e., Attention-Bi-LSTM with relation constraints, achieves the best performances with 96.53% micro F1 score and 98.27% macro F1 score in comparison with CNN-MF and Attention-Bi-LSTM. Moreover, the rule-based QR module can correctly answer the staging questions by reasoning the extracted results of NER and RC, which achieves 93.56% macro F1 score and 94.73% micro F1 score for all 22 questions. CONCLUSIONS We conclude that the developed IE system can effectively and accurately extract the information about lung cancer staging from the CT reports. Experimental results show that the extracted results have great potential for further utilization in stage verification and prediction to facilitate accurate clinical staging.

Mining Free Text for Structure

Data Mining ◽  
2011 ◽  
pp. 278-300
Author(s):  
Vladimir A. Kulyukin ◽  
Robin Burke
Keyword(s):  
Structural Organization ◽  
Knowledge Engineering ◽  
Knowledge Bases ◽  
Free Text ◽  
Learning Approaches ◽  
Structural Components ◽  
Text Documents ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Ultimate Objective

Knowledge of the structural organization of information in documents can be of significant assistance to information systems that use documents as their knowledge bases. In particular, such knowledge is of use to information retrieval systems that retrieve documents in response to user queries. This chapter presents an approach to mining free-text documents for structure that is qualitative in nature. It complements the statistical and machine-learning approaches, insomuch as the structural organization of information in documents is discovered through mining free text for content markers left behind by document writers. The ultimate objective is to find scalable data mining (DM) solutions for free-text documents in exchange for modest knowledge-engineering requirements. The problem of mining free text for structure is addressed in the context of finding structural components of files of frequently asked questions (FAQs) associated with many USENET newsgroups. The chapter describes a system that mines FAQs for structural components. The chapter concludes with an outline of possible future trends in the structural mining of free text.

A Comprehensive Framework Towards Information Sharing Between Government Agencies

2009 ◽  
pp. 1723-1740
Author(s):  
Akhilesh Bajaj ◽  
Sudha Ram
Keyword(s):  
Information Sharing ◽  
Data Storage ◽  
Heterogeneous Data ◽  
Information Storage ◽  
Government Agencies ◽  
Free Text ◽  
Text Documents ◽  
Shared Information ◽  
Share Data ◽  
Reducing Costs

Recently, there has been increased interest in sharing digitized information between government agencies, with the goals of improving security, reducing costs, and offering better quality service to users of government services. The bulk of previous work in interagency information sharing has focused largely on the sharing of structured information among heterogeneous data sources, whereas government agencies need to share data with varying degrees of structure ranging from free text documents to relational data. In this work, we explore the different technologies available to share information. Specifically, our framework discusses the optional data storage mechanisms required to support a Service Oriented Architecture (SOA). We compare XML document, free text search engine, and relational database technologies and analyze the pros and cons of each approach. We explore these options along the dimensions of information definition, information storage, the access to this information, and finally the maintenance of shared information.

scholarly journals Ontology-based Document Spanning Systems for Information Extraction

2020 ◽  
Vol 14 (01) ◽  
pp. 3-26
Author(s):  
Domenico Lembo ◽  
Federico Maria Scafoglieri
Keyword(s):  
Information Extraction ◽  
Relational Databases ◽  
Description Logics ◽  
Data Access ◽  
Data Interpretation ◽  
Semantic Integration ◽  
Free Text ◽  
Text Documents ◽  
Other Information ◽  
Data Layer

Information Extraction (IE) is the task of automatically organizing in a structured form data extracted from free text documents. In several contexts, it is often desirable that the extracted data are then organized according to an ontology, which provides a formal and conceptual representation of the domain of interest. Ontologies allow for a better data interpretation, as well as for their semantic integration with other information, as in Ontology-based Data Access (OBDA), a popular declarative framework for data management where an ontology is connected to a data layer through mappings. However, the data layer considered so far in OBDA has consisted essentially of relational databases, and how to declaratively couple an ontology with unstructured data sources is still unexplored. By leveraging the recent study on document spanners for rule-based IE by Fagin et al., in this paper, we propose a new framework that allows to map text documents to ontologies, in the spirit of OBDA. We investigate the problem of answering conjunctive queries in this framework. For ontologies specified in the Description Logics [Formula: see text] and [Formula: see text], we show that the problem is polynomial in the size of the underlying documents. We also provide algorithms to solve query answering by rewriting the input query on the basis of the ontology and its mapping toward the source documents. Through these techniques, we pursue a virtual approach, similar to that typically adopted in OBDA, which allows us to answer a query without having to first populate the entire ontology. Interestingly, for [Formula: see text], both the spanners used in the mapping and the one computed by the rewriting algorithm belong to the same class of expressiveness. This holds also for [Formula: see text], modulo some limitations on the form of the mapping. These results say that in these cases our framework can be easily implemented by decoupling ontology management and document access, which can be delegated to an external IE system able to process the extraction rules we use in the mapping.

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