Rule-based Information Extraction Research Articles

Rule-Based Information Extraction from Multi-format Resumes for Automated Classification

Rule-Based Information Extraction from Multi-format Resumes for Automated Classification

Text analysis framework for identifying mutations among non-small cell lung cancer patients from laboratory data

BackgroundLaboratory data can provide great value to support research aimed at reducing the incidence, prolonging survival and enhancing outcomes of cancer. Data is characterized by the information it carries and the format it holds. Data captured in Alberta’s biomarker laboratory repository is free text, cluttered and rouge. Such data format limits its utility and prohibits broader adoption and research development. Text analysis for information extraction of unstructured data can change this and lead to more complete analyses. Previous work on extracting relevant information from free text, unstructured data employed Natural Language Processing (NLP), Machine Learning (ML), rule-based Information Extraction (IE) methods, or a hybrid combination between them.MethodsIn our study, text analysis was performed on Alberta Precision Laboratories data which consisted of 95,854 entries from the Southern Alberta Dataset (SAD) and 6944 entries from the Northern Alberta Dataset (NAD). The data covers all of Alberta and is completely population-based. Our proposed framework is built around rule-based IE methods. It incorporates topics such as Syntax and Lexical analyses to achieve deterministic extraction of data from biomarker laboratory data (i.e., Epidermal Growth Factor Receptor (EGFR) test results). Lexical analysis compromises of data cleaning and pre-processing, Rich Text Format text conversion into readable plain text format, and normalization and tokenization of text. The framework then passes the text into the Syntax analysis stage which includes the rule-based method of extracting relevant data. Rule-based patterns of the test result are identified, and a Context Free Grammar then generates the rules of information extraction. Finally, the results are linked with the Alberta Cancer Registry to support real-world cancer research studies.ResultsOf the original 5512 entries in the SAD dataset and 5017 entries in the NAD dataset which were filtered for EGFR, the framework yielded 5129 and 3388 extracted EGFR test results from the SAD and NAD datasets, respectively. An accuracy of 97.5% was achieved on a random sample of 362 tests.ConclusionsWe presented a text analysis framework to extract specific information from unstructured clinical data. Our proposed framework has shown that it can successfully extract relevant information from EGFR test results.

FedFSA: Hybrid and federated framework for functional status ascertainment across institutions

IntroductionPatients' functional status assesses their independence in performing activities of daily living, including basic ADLs (bADL), and more complex instrumental activities (iADL). Existing studies have discovered that patients’ functional status is a strong predictor of health outcomes, particularly in older adults. Depite their usefulness, much of the functional status information is stored in electronic health records (EHRs) in either semi-structured or free text formats. This indicates the pressing need to leverage computational approaches such as natural language processing (NLP) to accelerate the curation of functional status information. In this study, we introduced FedFSA, a hybrid and federated NLP framework designed to extract functional status information from EHRs across multiple healthcare institutions. MethodsFedFSA consists of four major components: 1) individual sites (clients) with their private local data, 2) a rule-based information extraction (IE) framework for ADL extraction, 3) a BERT model for functional status impairment classification, and 4) a concept normalizer. The framework was implemented using the OHNLP Backbone for rule-based IE and open-source Flower and PyTorch library for federated BERT components. For gold standard data generation, we carried out corpus annotation to identify functional status-related expressions based on ICF definitions. Four healthcare institutions were included in the study. To assess FedFSA, we evaluated the performance of category- and institution-specific ADL extraction across different experimental designs. ResultsADL extraction performance ranges from an F1-score of 0.907 to 0.986 for bADL and 0.825 to 0.951 for iADL across the four healthcare sites. The performance for ADL extraction with impairment ranges from an F1-score of 0.722 to 0.954 for bADL and 0.674 to 0.813 for iADL across four healthcare sites. For category-specific ADL extraction, laundry and transferring yielded relatively high performance, while dressing, medication, bathing, and continence achieved moderate-high performance. Conversely, food preparation and toileting showed low performance. ConclusionNLP performance varied across ADL categories and healthcare sites. Federated learning using a FedFSA framework performed higher than non-federated learning for impaired ADL extraction at all healthcare sites. Our study demonstrated the potential of the federated learning framework in functional status extraction and impairment classification in EHRs, exemplifying the importance of a large-scale, multi-institutional collaborative development effort.

Semantic rule-based information extraction for meteorological reports

Semantic rule-based information extraction for meteorological reports

Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts

Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy. In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language. The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts. We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5min. We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.

Evaluating Patients' Experiences with Healthcare Services: Extracting Domain and Language-Specific Information from Free-Text Narratives.

Evaluating patients’ experience and satisfaction often calls for analyses of free-text data. Language and domain-specific information extraction can reduce costly manual preprocessing and enable the analysis of extensive collections of experience-based narratives. The research aims were to (1) elicit free-text narratives about experiences with health services of international students in Poland, (2) develop domain- and language-specific algorithms for the extraction of information relevant for the evaluation of quality and safety of health services, and (3) test the performance of information extraction algorithms’ on questions about the patients’ experiences with health services. The materials were free-text narratives about health clinic encounters produced by English-speaking foreigners recalling their experiences (n = 104) in healthcare facilities in Poland. A linguistic analysis of the text collection led to constructing a semantic–syntactic lexicon and a set of lexical-syntactic frames. These were further used to develop rule-based information extraction algorithms in the form of Python scripts. The extraction algorithms generated text classifications according to predefined queries. In addition, the narratives were classified by human readers. The algorithm-based and the human readers’ classifications were highly correlated and significant (p < 0.01), indicating an excellent performance of the automatic query algorithms. The study results demonstrate that domain-specific and language-specific information extraction from free-text narratives can be used as an efficient and low-cost method for evaluating patient experiences and satisfaction with health services and built into software solutions for the quality evaluation in health care.

Rule-Based Information Extraction from Free-Text Pathology Reports Reveals Trends in South African Female Breast Cancer Molecular Subtypes and Ki67 Expression

Clinical information on molecular subtypes and the Ki67 index is critical for breast cancer (BC) prognosis and personalised treatment plan. Extracting such information into structured data is essential for research, auditing, and cancer incidence reporting and underpins the potential for automated decision support. Herewith, we developed a rule-based natural language processing algorithm that retrieved and extracted important BC parameters from free-text pathology reports towards exploring molecular subtypes and Ki67-proliferation trends. We considered malignant BC pathology reports with different free-text narrative attributes from the South African National Health Laboratory Service. The reports were preprocessed and parsed through the algorithm. Parameters extracted by the algorithm were validated against manually extracted parameters. For all parameters extracted, we obtained accurate annotations of 83-100%, 93-100%, 91-100%, and 92-100% precision, recall, F1-score, and kappa, respectively. There was a significant trend in the proportion of each molecular subtype by patient age, histologic type, grade, Ki67, and race. The findings also showed significant association in the Ki67 trend with hormone receptors, human epidermal growth factors, age, grade, and race. Our approach bridges the gap between data availability and actionable knowledge and provides a framework that could be adapted and reused in other cancers and beyond cancer studies. Information extracted from these reports showed interesting trends that may be exploited for BC screening and treatment resources in South Africa. Finally, this study strongly encourages the implementation of a synoptic style pathology report in South Africa.

Automated Identification and Measurement Extraction of Pancreatic Cystic Lesions from Free-Text Radiology Reports Using Natural Language Processing.

To automatically identify a cohort of patients with pancreatic cystic lesions (PCLs) and extract PCL measurements from historical CT and MRI reports using natural language processing (NLP) and a question answering system. Institutional review board approval was obtained for this retrospective Health Insurance Portability and Accountability Act-compliant study, and the requirement to obtain informed consent was waived. A cohort of free-text CT and MRI reports generated between January 1991 and July 2019 that covered the pancreatic region were identified. A PCL identification model was developed by modifying a rule-based information extraction model; measurement extraction was performed using a state-of-the-art question answering system. The system's performance was evaluated against radiologists' annotations. For this study, 430 426 free-text radiology reports from 199 783 unique patients were identified. The NLP model for identifying PCL was applied to 1000 test samples. The interobserver agreement between the model and two radiologists was almost perfect (Fleiss κ = 0.951), and the false-positive rate and true-positive rate were 3.0% and 98.2%, respectively, against consensus of radiologists' annotations as ground truths. The overall accuracy and Lin concordance correlation coefficient for measurement extraction were 0.958 and 0.874, respectively, against radiologists' annotations as ground truths. An NLP-based system was developed that identifies patients with PCLs and extracts measurements from a large single-institution archive of free-text radiology reports. This approach may prove valuable to study the natural history and potential risks of PCLs and can be applied to many other use cases.Keywords: Informatics, Abdomen/GI, Pancreas, Cysts, Computer Applications-General (Informatics), Named Entity Recognition Supplemental material is available for this article. © RSNA, 2022See also commentary by Horii in this issue.

Ontology-based Document Spanning Systems for Information Extraction

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.

Efficient Enumeration Algorithms for Regular Document Spanners

Regular expressions and automata models with capture variables are core tools in rule-based information extraction. These formalisms, also called regular document spanners , use regular languages to locate the data that a user wants to extract from a text document and then store this data into variables. Since document spanners can easily generate large outputs, it is important to have efficient evaluation algorithms that can generate the extracted data in a quick succession, and with relatively little precomputation time. Toward this goal, we present a practical evaluation algorithm that allows output-linear delay enumeration of a spanner’s result after a precomputation phase that is linear in the document. Although the algorithm assumes that the spanner is specified in a syntactic variant of variable-set automata, we also study how it can be applied when the spanner is specified by general variable-set automata, regex formulas, or spanner algebras. Finally, we study the related problem of counting the number of outputs of a document spanner and provide a fine-grained analysis of the classes of document spanners that support efficient enumeration of their results.

Abstract 165: Automated Stroke-Related Information Extraction From Diagnostic Imaging Reports Using Natural Language Processing

Introduction: Diagnostic imaging reports contain important data for stroke surveillance and clinical research but converting a large amount of free-text data into structured data with manual chart abstraction is resource-intensive. We determined the accuracy of CHARTextract, a natural language processing (NLP) tool, to extract relevant stroke-related attributes from full reports of computed tomograms (CT), CT angiograms (CTA), and CT perfusion (CTP) performed at a tertiary stroke centre. Methods: We manually extracted data from full reports of 1,320 consecutive CT/CTA/CTP performed between October 2017 and January 2019 in patients presenting with acute stroke. Trained chart abstractors collected data on the presence of anterior proximal occlusion, basilar occlusion, distal intracranial occlusion, established ischemia, haemorrhage, the laterality of these lesions, and ASPECT scores, all of which were used as a reference standard. Reports were then randomly split into a training set (n= 921) and validation set (n= 399). We used CHARTextract to extract the same attributes by creating rule-based information extraction pipelines. The rules were human-defined and created through an iterative process in the training sample and then validated in the validation set. Results: The prevalence of anterior proximal occlusion was 12.3% in the dataset (n=86 left, n=72 right, and n=4 bilateral). In the training sample, CHARTextract identified this attribute with an overall accuracy of 97.3% (PPV 84.1% and NPV 99.4%, sensitivity 95.5% and specificity 97.5%). In the validation set, the overall accuracy was 95.2% (PPV 76.3% and NPV 98.5%, sensitivity 90.0% and specificity 96.0%). Conclusions: We showed that CHARTextract can identify the presence of anterior proximal vessel occlusion with high accuracy, suggesting that NLP can be used to automate the process of data collection for stroke research. We will present the accuracy of CHARTextract for the remaining neurological attributes at ISC 2020.

A Rule-based Information Extraction System

Designing intelligent expert systems capable of answering different human queries is a challenging and emerging area of research. A huge amount of web data is available online and majority of which are in the form of unstructured documents covering articles, online news, corporate reports, medical records, social media communication data, etc. A user in need of certain information has to assess all the relevant documents to obtain the exact answer of their queries which is a time consuming and tedious work. Also, sometimes it becomes quite difficult to obtain the exact information from a list of documents quickly as and when required unless the whole document is read. This paper presents a rule-based information extraction system for unstructured web data that access the document contents quickly and provides the relevant answers to the user queries in a structured format. A number of tests were conducted to determine the overall performance of the proposed model and the results obtained in all the experiments performed shows the effectiveness of the model in providing required answers to different user queries quickly.

Exploration of Artificial Intelligence Use with ARIES in Multiple Myeloma Research.

Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.

Solving Arithmetic Word Problems by Object Oriented Modeling and Query-Based Information Processing

The paper presents an Object Oriented Analysis and Design (OOAD) approach to modeling, reasoning and a database query based approach to processing and solving addition-subtraction (Add-Sub) type arithmetic Mathematical Word Problems (MWP) of elementary school level. The system identifies and extracts the key entities in a word problem like owners, items and their attributes and quantities, verbs, from all the input sentences, using a rule based Information Extraction (IE) approach based on Semantic Role Labeling (SRL) technique. These information are then stored in predefined templates which are further modeled to represent an MWP in the object-oriented paradigm and processed using query based approach to generate the answer. These kind of applications are based on Natural Language Processing (NLP), Natural Language Understanding (NLU) and Artificial Intelligence (AI), and can be used as intelligent dynamic mathematical tutoring tools as part of E-Learning systems, Learning Management Systems, on-line education, etc. The proposed object oriented mathematical word problem solver can solve arithmetic MWPs involving only addition-subtraction operations and it has produced an accuracy of 94.35% on a subset of the AI2 arithmetic questions dataset.

Towards Semantic Interoperability in Health Data Management Facilitating Process Mining

As an evidence-based business process analysis method, process mining can be used to investigate variations in delivery of care. Existing approaches are only based on one data source. A variety of data sources means different domain languages and understanding, special processes workflows in various organizations, varying documentation with different goals and different designations and varying use of coding systems. This article describes a modular, rule-based information extraction algorithm based on CDA and compares it to a proprietary healthcare reference model approach and a resource-based extraction of healthcare data using the new standard FHIR. All three approaches can be used to derive models to extract clinical and patient pathways. Similarities and differences according to interoperability and process mining tasks are described. It is concluded that standards-based approaches allow for more interoperability and can be used for a wide range of systems to provide process insight, thus facilitating better healthcare management across institutional boundaries.

Content and Context: Two-Pronged Bootstrapped Learning for Regex-Formatted Entity Extraction

Regular expressions are an important building block of rule-based information extraction systems. Regexes can encode rules to recognize instances of simple entities which can then feed into the identification of more complex cross-entity relationships. Manually crafting a regex that recognizes all possible instances of an entity is difficult since an entity can manifest in a variety of different forms. Thus, the problem of automatically generalizing manually crafted seed regexes to improve the recall of IE systems has attracted research attention. In this paper, we propose a bootstrapped approach to improve the recall for extraction of regex-formatted entities, with the only source of supervision being the seed regex. Our approach starts from a manually authored high precision seed regex for the entity of interest, and uses the matches of the seed regex and the context around these matches to identify more instances of the entity. These are then used to identify a set of diverse, high recall regexes that are representative of this entity. Through an empirical evaluation over multiple real world document corpora, we illustrate the effectiveness of our approach.

Rule based Text Extraction from a Bibliographic Database

&lt;p&gt;The emergent concept of ‘ Big Data’ has shifted the paradigm from information retrieval to information extraction techniques. The information extraction techniques enables corpus analysis to draw useful interpretations and its possible applications. Selection of appropriate information extraction technique depends upon the type of data being dealt with and its possible applications. In an R&amp;amp;D environment, the published information is considered as an authenticated benchmark to study and analyse the growth pattern in that field of science, medicine, business. A rule based information extraction process, on the selected data extracted from a bibliographic database of published R&amp;amp;D papers is proposed in this paper. Aim of the study is to build up a database on relevant concepts, cleaning of retrieved data and automate the process of information retrieval in the local database. For this purpose, a concept based ‘subject profiles’ in the area of advanced semiconductors as well as the rules for text extraction from metadata retrieved from the bibliographic database was developed. This subset was used as an input to the knowledge domain to support R&amp;amp;D in the area of ‘advanced semiconductor materials and devices’ and provide information services on Intranet. Study found that concept based pattern matching on the datasets downloaded yielded better results as compared to the results by using the controlled vocabulary of the source database .&lt;/p&gt;

Name identification and extraction with formal concept analysis

One of the applications of the Formal concept analysis (FCA) is the ability to extract structured information from textual documents. Typically, one can define a set of attributes that will characterize the objects. Consequently, these defined objects will be extracted by standard FCA algorithms. In this paper, we describe how FCA identifies and extracts personal names as units of thought similar to the decoding of text sequences by Viterbi algorithm as used with Hidden Markov Models. We further exhibit how FCA mimics the thought process that goes into a rule-based information extraction system. We then observe that the formal approach of FCA combined with already established computational techniques such as bottom up intersection algorithm avoids the difficulties associated with hand coding and maintenance of rule-based systems.

A high-precision rule-based extraction system for expanding geospatial metadata in GenBank records.

The metadata reflecting the location of the infected host (LOIH) of virus sequences in GenBank often lacks specificity. This work seeks to enhance this metadata by extracting more specific geographic information from related full-text articles and mapping them to their latitude/longitudes using knowledge derived from external geographical databases. We developed a rule-based information extraction framework for linking GenBank records to the latitude/longitudes of the LOIH. Our system first extracts existing geospatial metadata from GenBank records and attempts to improve it by seeking additional, relevant geographic information from text and tables in related full-text PubMed Central articles. The final extracted locations of the records, based on data assimilated from these sources, are then disambiguated and mapped to their respective geo-coordinates. We evaluated our approach on a manually annotated dataset comprising of 5728 GenBank records for the influenza A virus. We found the precision, recall, and f-measure of our system for linking GenBank records to the latitude/longitudes of their LOIH to be 0.832, 0.967, and 0.894, respectively. Our system had a high level of accuracy for linking GenBank records to the geo-coordinates of the LOIH. However, it can be further improved by expanding our database of geospatial data, incorporating spell correction, and enhancing the rules used for extraction. Our system performs reasonably well for linking GenBank records for the influenza A virus to the geo-coordinates of their LOIH based on record metadata and information extracted from related full-text articles.

A novel approach for automatic extraction of semantic data about football transfer in sport news

Purpose – The purpose of this paper is to propose an automatic method to generate semantic annotations of football transfer in the news. The current automatic news integration systems on the Web are constantly faced with the challenge of diversity, heterogeneity of sources. The approaches for information representation and storage based on syntax have some certain limitations in news searching, sorting, organizing and linking it appropriately. The models of semantic representation are promising to be the key to solving these problems. Design/methodology/approach – The approach of the author leverages Semantic Web technologies to improve the performance of detection of hidden annotations in the news. The paper proposes an automatic method to generate semantic annotations based on named entity recognition and rule-based information extraction. The authors have built a domain ontology and knowledge base integrated with the knowledge and information management (KIM) platform to implement the former task (name...