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

BACKGROUND: 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. OBJECTIVES: 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. METHODS: 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. RESULTS: 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 5 min. CONCLUSION: 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.

Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores.

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.

openEHR-to-FHIR: Converting openEHR Compositions to Fast Healthcare Interoperability Resources (FHIR) for the German Corona Consensus Dataset (GECCO).

The German Corona Consensus (GECCO) established a uniform dataset in FHIR format for exchanging and sharing interoperable COVID-19 patient specific data between health information systems (HIS) for universities. For sharing the COVID-19 information with other locations that use openEHR, the data are to be converted in FHIR format. In this paper, we introduce our solution through a web-tool named "openEHR-to-FHIR" that converts compositions from an openEHR repository and stores in their respective GECCO FHIR profiles. The tool provides a REST web service for ad hoc conversion of openEHR compositions to FHIR profiles.

Transformation of microbiology data into a standardised data representation using OpenEHR.

The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems.

Heart Failure in Ischemic Stroke: Relevance for Acute Care and Outcome.

Background and Purpose- Heart failure (HF) in patients with acute ischemic stroke constitutes the source of various detrimental pathophysiologic mechanisms including prothrombotic and proinflammatory states, worsening of cerebral tissue oxygenation, and hemodynamic impairment. In addition, HF might affect the safety and efficacy of the acute recanalization stroke therapies. Methods- Patients treated with intravenous recombinant tissue-type plasminogen activator or mechanical recanalization at a universitary stroke center were included into a prospective registry. Patients received cardiological evaluation, including echocardiography, during acute care. Functional outcome was assessed after 90 days by structured telephone interviews. Safety and efficacy of intravenous thrombolysis and mechanical thrombectomy were investigated among patients with HF and compared with patients with normal cardiac function after propensity score matching. Results- One thousand two hundred nine patients were included. HF was present in 378 patients (31%) and an independent predictor of unfavorable functional outcome. Recanalization rates were equal among patients with HF after intravenous thrombolysis and after mechanical recanalization or combined treatment. The rate of secondary intracranial hemorrhage was not different (7% versus 8%; P=0.909 after thrombolysis and 15% versus 20%, P=0.364 after mechanical recanalization or combined therapy). Early mortality within 48 hours after admission was equal (<1.5% in both groups). Conclusions- In this real-world cohort of patients with stroke, HF was an independent predictor of unfavorable functional long-term outcome, while the safety and efficacy of intravenous thrombolysis and mechanical recanalization appeared unaffected.

A Report on Archetype Modelling in a Nationwide Data Infrastructure Project.

BACKGROUND: The nationwide data infrastructure project HiGHmed strives for achieving semantic interoperability through the use of openEHR archetypes. Therefore, a knowledge governance framework defining collaborative modelling processes has been established. For long-sustained success and the creation of high-quality archetypes, continuous monitoring is vital. OBJECTIVES: To present an update on archetype modelling and governance framework establishment in HiGHmed. METHODS: Qualitative and quantitative analyses of the progress in establishing modelling groups, roles and users, realizing modelling workflows, and modelling archetypes. RESULTS: Currently, 25 modellers and 17 domain experts are participating. 79 archetypes have been identified, from which 69 are pre-existing and internationally published; completion rates of review rounds are satisfying but improvable. CONCLUSIONS: The governance framework is valuable to make the activities manageable and to accelerate modelling. Combined with highly engaged data stewards and clinicians, a reasonable number of archetypes have already been developed.

Simultaneous monitoring of different vitrification solution components permeating into tissues.

Cryopreservation can be used for long-term preservation of tissues and organs. It relies on using complex mixtures of cryoprotective agents (CPAs) to reduce the damaging effects of freezing, but care should be taken to avoid toxic effects of CPAs themselves. In order to rationally design cryopreservation strategies for tissues, it is important to precisely determine permeation kinetics of the protectants that are used to ensure maximum permeation, while minimizing the exposure time and toxicity effects. This is particularly challenging with protectant solutions consisting of multiple components each with different physical properties and diffusing at a different rate. In this study, we show that an attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) setup can be used to simultaneously monitor diffusion of multiple components in a mixture into tissues in real time. Diffusion studies were done with decellularized heart valves using a sucrose-DMSO mixture as well as vitrification solution VS83. To assess diffusion kinetics of different solutes in mixtures, the increase in specific infrared absorbance bands was monitored during diffusion through the tissue. Solute specific wavenumber ranges were selected, and the calculated area was assumed to be proportional to the CPA concentration in the tissue. A diffusion equation based on Fick's second law of diffusion fitted the experimental data quite well, and clear differences in permeation rates were observed among the different mixture components dependent on molecular size and physical properties.