Converting semi-structured clinical medical records into information and knowledge

Proceedings of the 21st International Conference on Data Engineering, ICDE 2005, pp. 1647765.Clinical medical records contain a wealth of information, largely in free-textual form. Thus, means to extract structured information from free-text records becomes an important research endeavor. In this paper, we propose and implement an information extraction system that extracts three types of information — numeric values, medical terms and categorical value — from semi-structured patient records. Three approaches are proposed to solve the problems posed by each of the three types of values, respectively, and very good performance (precision and recall) is achieved. A novel link-grammar based approach was invented to associate feature and number in a sentence, and extremely high accuracy was achieved. A simple but efficient approach, using POS-based pattern and domain ontology, was adopted to extract medical terms of interest. Finally, an NLPbased feature extraction method coupled with an ID3- based decision tree is used to classify and extract categorical cases. This preliminary approach to categorical fields has, so far, proven to be quite effective

Lines-of-inquiry and sources of evidence in work-based research

There is synergy between the investigative practices of police detectives and social scientists, including work-based researchers. They both develop lines-of-inquiry and draw on multiple sources of evidence in order to make inferences about people, trends and phenomena. However, the principles associated with lines-of-inquiry and sources of evidence have not so far been examined in relation to work-based research methods, which are often unexplored or ill-defined in the published literature. We explore this gap by examining the various direct and indirect lines-of-inquiry and the main sources of primary and secondary evidence used in work-based research, which is especially relevant because some work-based researchers are also police detectives. Clearer understanding of these intersections will be useful in emerging professional contexts where the work-based researcher, the detective, and the social scientist cohere in the one person and their research project. The case we examined was a Professional Studies programme at a university in Australia, which has many police detectives doing work-based research, and from their experience we conclude there is synergy between work-based research and lines of enquiry. Specifically, in the context of research methods, we identify seven sources of evidence: 1) creative, unstructured, and semi-structured interviews; 2) structured interviews; 3) consensus group methods; 4) surveys; 5) documentation and archives; 6) direct observations and participant observations; and 7) physical or cultural artefacts, and show their methodological features related to data and method type, reliability, validity, and types of analysis, along with their respective advantages and disadvantages. This study thereby unpacks and isolates those characteristics of work-based research which are relevant to a growing body of literature related to the messy, co-produced and wicked problems of private companies, government agencies, and non-government organisations and the research methods used to investigate them

Automated Transformation of Semi-Structured Text Elements

Interconnected systems, such as electronic health records (EHR), considerably improved the handling and processing of health information while keeping the costs at a controlled level. Since the EHR virtually stores all data in digitized form, personal medical documents are easily and swiftly available when needed. However, multiple formats and differences in the health documents managed by various health care providers severely reduce the efficiency of the data sharing process. This paper presents a rule-based transformation system that converts semi-structured (annotated) text into standardized formats, such as HL7 CDA. It identifies relevant information in the input document by analyzing its structure as well as its content and inserts the required elements into corresponding reusable CDA templates, where the templates are selected according to the CDA document type-specific requirements

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Natural Language Processing – Finding the Missing Link for Oncologic Data, 2022

Oncology like most medical specialties, is undergoing a data revolution at the center of which lie vast and growing amounts of clinical data in unstructured, semi-structured and structed formats. Artificial intelligence approaches are widely employed in research endeavors in an attempt to harness electronic medical records data to advance patient outcomes. The use of clinical oncologic data, although collected on large scale, particularly with the increased implementation of electronic medical records, remains limited due to missing, incorrect or manually entered data in registries and the lack of resource allocation to data curation in real world settings. Natural Language Processing (NLP) may provide an avenue to extract data from electronic medical records and as a result has grown considerably in medicine to be employed for documentation, outcome analysis, phenotyping and clinical trial eligibility. Barriers to NLP persist with inability to aggregate findings across studies due to use of different methods and significant heterogeneity at all levels with important parameters such as patient comorbidities and performance status lacking implementation in AI approaches. The goal of this review is to provide an updated overview of natural language processing (NLP) and the current state of its application in oncology for clinicians and researchers that wish to implement NLP to augment registries and/or advance research projects

An ontology to standardize research output of nutritional epidemiology : from paper-based standards to linked content

Background: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. Methods: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. Results: Ontologies for food and nutrition (n = 37), disease and specific population (n = 100), data description (n = 21), research description (n = 35), and supplementary (meta) data description (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. Conclusion: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology