A detailed analysis of the Arden Syntax expression grammar.

OBJECTIVE: The Arden Syntax for Medical Logic Systems is a standard for encoding and sharing medical knowledge in the form of Medical Logic Modules. To improve accessibility for clinicians, the originators of the standard deliberately designed Arden Syntax expressions to resemble natural language, and parentheses around operands are not generally required. For certain patterns of nested expressions, however, the use of parentheses is mandatory, otherwise they are not accepted by an Arden Syntax environment. In this study, we refer to such patterns as anomalies. The purpose of this paper is to investigate the extent and the circumstances of such anomalies, and to outline a solution based on an alternative grammar encoding approach. METHODS: To analyze the distribution of anomalies in nested expressions, we developed two custom-made complementary utilities. The first utility, termed parser, checks a single expression pattern against the specification-compliant grammar for syntactic correctness. The second utility, termed composer, automatically creates an extensive amount of expression patterns by permuting and nesting operators without the use of parentheses, and stores these together with the expected syntactic correctness. By means of these utilities we conducted a comprehensive analysis of anomalies by comparing the expected correctness with the actual correctness. Any detected anomalies are stored into a set of files, grouped by the respective top-level operator, for a subsequent analysis. RESULTS: The composer utility nested 165 unary, binary, or ternary operators of Arden Syntax version 2.8 to a depth of two, resulting in a set of 76,533 expression patterns, of which 18,978 (24.8%) have been identified as anomalies. An automated assessment of their practical relevance for medical knowledge encoding is infeasible. Manual screening of selected samples indicated that only a small proportion of the detected anomalies would be relevant. The cause of the anomalies lies in the encoding of the grammar. A change of the basic encoding approach with some additional customizations eliminates the anomalies. A working expression parser is included in the supplementary material. CONCLUSION: Arden Syntax expressions are affected by anomalies. Since only a small proportion of them have practical relevance and they cannot cause false calculations or clinical decisions, their practical impact is likely limited. However, they may be potential points of confusion for knowledge engineers. An alternative expression grammar, based on a different encoding approach, would not only eliminate the anomalies, but could considerably facilitate both maintenance and further development of the standard.

Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies.

BACKGROUND: Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. METHODS: We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. RESULTS: Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. CONCLUSIONS: The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach.

Infection Control Through Clinical Pipelines Built with Arden Syntax MLM Building Blocks.

Healthcare-associated infections (HAIs) may have grave consequences for patients. In the case of sepsis, the 30-day mortality rate is about 25%. HAIs cost EU member states an estimated 7 billion Euros annually. Clinical decision support tools may be useful for infection monitoring, early warning, and alerts. MONI, a tool for monitoring nosocomial infections, is used at University Hospital Vienna, but needs to be clinically and technically revised and updated. A new, completely configurable pipeline-based system for defining and processing HAI definitions was developed and validated. A network of data access points, clinical rules, and explanatory output is arranged as an inference network, a clinical pipeline as it is called, and processed in a stepwise manner. Arden-Syntax-based medical logic modules were used to implement the respective rules. The system was validated by creating a pipeline for the ECDC PN5 pneumonia rule. It was tested on a set of patient data from intensive care medicine. The results were compared with previously obtained MONI output as a suitable reference, yielding a sensitivity of 93.8% and a specificity of 99.8%. Clinical pipelines show promise as an open and configurable approach to graphically-based, human-readable, machine-executable HAI definitions.

Health Digital Twins with Clinical Decision Support.

This paper studies the feasibility of incorporating clinical decision support (CDS) into health digital twins (HDTs). A HDT is visualized in a web application, health data are stored in a FHIR-based electronic health record, and an Arden-Syntax-based CDS interpretation and alert service is connected. The prototype focuses on interoperability of these components. The study confirms the feasibility of CDS integration into HDTs and provides insight into possibilities for further expansion.

Microbiological and Virological Knowledge-Based Alert Service.

Hospital wards need immediate information about multi-resistant pathogens and contagious viruses in their hospitalized patients. An alert service configurable with Arden-Syntax-based alert definitions passing through an ontology service to complement results from microbiology and virology with high-level terms was implemented as proof of concept. Integration into the University Hospital Vienna's IT landscape is ongoing.

Arden Syntax on FHIR.

Arden Syntax, a medical knowledge representation and processing language for clinical decision support tasks supervised by Health Level Seven International (HL7), was extended with HL7's Fast Healthcare Interoperability Resources (FHIR) constructs to allow standardized data access. The new version, Arden Syntax version 3.0, was successfully balloted as part of the audited, consensus-based, iterative HL7 standards development process.

The Arden Syntax standard for clinical decision support: experiences and directions.

Arden Syntax is a widely recognized standard for representing clinical and scientific knowledge in an executable format. It has a history that reaches back until 1989 and is currently maintained by the Health Level 7 (HL7) organization. We created a production-ready development environment, compiler, rule engine and application server for Arden Syntax. Over the course of several years, we have applied this Arden - Syntax - based CDS system in a wide variety of clinical problem domains, such as hepatitis serology interpretation, monitoring of nosocomial infections or the prediction of metastatic events in melanoma patients. We found the Arden Syntax standard to be very suitable for the practical implementation of CDS systems. Among the advantages of Arden Syntax are its status as an actively developed HL7 standard, the readability of the syntax, and various syntactic features such as flexible list handling. A major challenge we encountered was the technical integration of our CDS systems in existing, heterogeneous health information systems. To address this issue, we are currently working on incorporating the HL7 standard GELLO, which provides a standardized interface and query language for accessing data in health information systems. We hope that these planned extensions of the Arden Syntax might eventually help in realizing the vision of a global, interoperable and shared library of clinical decision support knowledge.

Assessing the clinical uses of fuzzy detection results in the automated detection of CVC-related infections: a preliminary report.

Central venous catheters (CVCs) play an essential role in the care of the critically ill, but their use comes at the risk of infection. By using fuzzy set theory and logic to model clinical linguistic CVC-related infection criteria, clinical detection systems can detect borderline infections where not all infection parameters have been (fully) met, also called fuzzy results. In this paper we analyzed the clinical use of these results. We used a fuzzy-logic-based computerized infection control system for the monitoring of healthcare-associated infections to uncover fuzzy results and periods, after which we classified them, and used these classifications together with knowledge of prior CVC-related infection episodes in temporal association rule mining. As a result, we uncovered several rules which can help with the early detection of re-occurring CVC-related infections.

A data-driven living review for pharmacogenomic decision support in cancer treatment.

With drastically decreasing costs of genetic sequencing, it has become feasible to use individual genetic markers to optimize treatment selection in cancer therapy. However, it is still difficult for medical practitioners to integrate these new kinds of data into clinical routine, since available information is growing rapidly. We demonstrate how a blend of manual curation and automated data extraction and evidence synthesis can be used to generate a 'living review', a summarization of current evidence on cancer classification, corresponding genetic markers, genetic tests and treatment options that can be used by clinicians to refine treatment choices. In contrast to a classical review, this automated 'living review' offers the opportunity of automatically updating core content when available data changes, making it easier to keep an overview of the best current evidence. We discuss some of the findings we made while creating a prototype of a 'living review' for colorectal cancer pharmacotherapy.

Concise healthcare-associated infection reporting and benchmarking with minimal staff resources.

We report on intelligent information technology tools that produce fully-automated surveillance reports of high precision for 12 intensive care units (ICUs) without relevant time expenditure of infection control or ICU staff. This is accomplished by MONI-ICU, a computerized system for automated identification and continuous monitoring of ICU-associated infections, which makes surveillance data readily accessible and presents them in easily perceptible reporting format.

Towards an interoperable information infrastructure providing decision support for genomic medicine.

Genetic dispositions play a major role in individual disease risk and treatment response. Genomic medicine, in which medical decisions are refined by genetic information of particular patients, is becoming increasingly important. Here we describe our work and future visions around the creation of a distributed infrastructure for pharmacogenetic data and medical decision support, based on industry standards such as the Web Ontology Language (OWL) and the Arden Syntax.

Processing gradual information with Fuzzy Arden syntax.

The programming language Arden Syntax is especially adapted to the needs of computer-based clinical decision support. Recently, an extension of Arden Syntax, named Fuzzy Arden Syntax, was proposed by the authors. Fuzzy Arden Syntax is a conservative extension of Arden Syntax and offers special functionality to process gradual information. The background is the observation that in medicine we frequently deal with statements which are neither clearly false nor clearly true but hold to some intermediate degree. In this paper, we demonstrate under which circumstances a Medical Logic Module (a program unit written in Arden Syntax) may show unintended behavior and how the situation can easily be improved by means of the possibilities offered by Fuzzy Arden Syntax. To this end, an example from the domain of nosocomial infection control is discussed in detail.

Electronic surveillance of healthcare-associated infections with MONI-ICU--a clinical breakthrough compared to conventional surveillance systems.

Surveillance of clinical entities such as healthcare-associated infections (HCAI) by conventional techniques is a time-consuming task for highly trained experts. Such are neither available nor affordable in sufficient numbers on a permanent basis. Nevertheless, expert surveillance is a key parameter for good clinical practice, especially in intensive care medicine. MONI-ICU (monitoring of nosocomial infections in intensive care units) has been developed methodically and practically in a stepwise manner over the last 20 years and is now a reliable tool for clinical experts. It provides an almost real-time view of clinical indicators for HCAI--at the cost of almost no additional time on the part of surveillance staff or clinicians. We describe the use of this system in clinical routine and compare the results generated automatically by MONI-ICU with those generated in parallel by trained surveillance staff using patient chart reviews and other available information ("gold standard"). A total of 99 ICU patient admissions representing 1007 patient days were analyzed. MONI-ICU identified correctly the presence of an HCAI condition in 28/31 cases (sensitivity, 90.3%) and their absence in 68/68 of the non-HCAI cases (specificity, 100%), the latter meaning that MONI-ICU produced no "false alarms". The time taken for conventional surveillance at the 52 ward visits was 82.5 hours. MONI-ICU analysis of the same patient cases, including careful review of the generated results required only 12.5 hours (15.2%).

A Comparative Study of the Arden Syntax and GDL Clinical Knowledge Representation Languages.

The expressiveness of a medical knowledge representation language has significant impact on the effectiveness of a knowledge-based clinical decision support system. We assess the expressiveness of two such languages, Arden Syntax and the Guideline Definition Language. Using data extracted from both languages' specifications, we quantify expressiveness by means of language syntax and the number of supported operators. Preliminary results show that Arden Syntax is a more dynamic standard, having better readability and a higher number and more diverse operators than GDL. In contrast, GDL is a more rigid language that utilizes an underlying data model specification in the openEHR framework.

A Digital Clinical Guideline Based on HL7 Arden Syntax for the Treatment of Children with Traumatic Brain Injury.

BACKGROUND: Intracranial hypertension is a serious complication accompanying the intensive care of children with traumatic brain injury. Intensive care of these patients is based on internationally accepted guidelines and their local editions. OBJECTIVE: The goal was to develop a software system for digital implementation of the clinical protocol for the treatment of intracranial hypertension in children, based on HL7 Arden Syntax clinical decision support. METHODS: Arden Syntax, an HL7 medical knowledge representation and processing standard, was used to develop a digital version of the local guideline. RESULTS: Comparison of 37 patients given conventional treatment with a second group of 84 patients treated with the digital clinical guideline yielded statistically significant differences. CONCLUSION: The digital clinical guideline system undoubtedly improves the doctor's awareness of the patient's existing condition and potential complications of intracranial hypertension.

Exploring Methods of Implementing Arden Syntax for CDS Hooks.

BACKGROUND: Specifications for Arden Syntax lack provisions for the standardized access of clinical decision support (CDS) services. The CDS Hooks standard provides such access. OBJECTIVES: To extend an ArdenSuite reference implementation of the Arden Syntax by providing a CDS-Hooks-compatible interface. METHODS: With the use case Hepaxpert, an Arden-Syntax-based expert system for the interpretation of hepatitis serology test results, a needs analysis was performed to identify changes required in the ArdenSuite reference implementation to support the CDS Hooks API. Arden Syntax language support for CDS Hooks was also assessed. RESULTS: The needs assessment was performed in three phases: hook assessment, hook context definition, and Card definition. For the use case, the ArdenSuite was modified to include a new hook and hook context, which defines the type of CDS service as well its input parameters. Card definitions were created in the ArdenSuite. Examples of Arden Syntax support for the use case are presented for all three phases. CONCLUSION: Minor changes in the ArdenSuite made it compatible with the CDS Hooks specification.

Cerebral Autoregulation Monitoring and Online Arden Syntax Clinical Decision Support for the Treatment of Intracranial Hypertension in Children with Traumatic Brain Injury.

BACKGROUND: Intracranial hypertension is a serious complication in the intensive care of children with severe traumatic brain injury (STBI). OBJECTIVE: The goal was to create a computer system for simultaneous neuromonitoring of cerebral parameters and Arden-Syntax-based clinical decision support (CDS) in children with STBI undergoing intensive care treatment. METHODS: Intensive care of these patients is based on internationally accepted guidelines. Arden Syntax, which is an HL7 medical knowledge representation and processing standard for CDS systems, was used to develop digital algorithms for these guidelines. RESULTS: Comparison of a group of 37 conventionally treated patients with a second group (84 patients) monitored and treated with the combined CDS system yielded statistically significant differences. CONCLUSION: A combination of cerebral autoregulation monitoring with Arden-Syntax-based CDS in accordance with guidelines for the treatment of intracranial hypertension in children with STBI provides markedly better treatment outcomes. It opens up new options for the use of standards to formalize and process medical knowledge in neuromonitoring.

Data correction pre-processing for electronically stored blood culture results: implications on microbial spectrum and empiric antibiotic therapy.

BACKGROUND: The outcome of patients with bacteraemia is influenced by the initial selection of adequate antimicrobial therapy. The objective of our study was to clarify the influence of different crude data correction methods on a) microbial spectrum and ranking of pathogens, and b) cumulative antimicrobial susceptibility pattern of blood culture isolates obtained from patients from intensive care units (ICUs) using a computer based tool, MONI. METHODS: Analysis of 13 ICUs over a period of 7 years yielded 1427 microorganisms from positive results. Three different data correction methods were applied. Raw data method (RDM): Data without further correction, including all positive blood culture results. Duplicate-free method (DFM): Correction of raw data for consecutive patient's results yielding same microorganism with similar antibiogram within a two-week period. Contaminant-free method (CFM): Bacteraemia caused by possible contaminants was only assumed as true bloodstream infection, if an organism of the same species was isolated from > 2 sets of blood cultures within 5 days. RESULTS: Our study demonstrates that different approaches towards raw data correction - none (RDM), duplicate-free (DFM), and a contaminant-free method (CFM) - show different results in analysis of positive blood cultures. Regarding the spectrum of microorganisms, RDM and DFM yielded almost similar results in ranking of microorganisms, whereas using the CFM resulted in a clinically and epidemiologically more plausible spectrum. CONCLUSION: For possible skin contaminants, the proportion of microorganisms in terms of number of episodes is most influenced by the CFM, followed by the DFM. However, with exception of fusidic acid for gram-positive organisms, none of the evaluated correction methods would have changed advice for empiric therapy on the selected ICUs.

A formal logical framework for Cadiag-2.

Cadiag-2-where "Cadiag" stands for "computer-assisted diagnosis"- is an expert system based on fuzzy logic assisting in the differential diagnosis in internal medicine. With its aid, it is possible to derive from possibly vague information about a patient's symptoms, signs, laboratory test results, and clinical findings conjectures about present diseases. In this paper, we provide a mathematical formalization of the inferential mechanism of Cadiag-2. The aim is to have a formal logical calculus at hand which corresponds to the mode of operation of Cadiag-2 and which is among others needed to perform consistency checking of Cadiag-2's medical knowledge base.

Cadiag-2 and fuzzy probability logics.

We briefly describe the medical expert system Cadiag-2, developed to support the differential diagnostic process in internal medicine. We propose a propositional logic called fuzzy probability logic as a possible formalization of Cadiag-2 and indicate the way of transferring Cadiag-2's methodology into this framework.