Supporting diagnostics and decision making in healthcare by modular methods of computational linguistics

We propose a new framework for development of modular computational methods to support processes of healthcare and health education in diverse settings. Motivated by an evaluation by The National Institute for Health and Welfare in Finland the proposed framework aims to address challenges of analyzing knowledge concerning healthcare services and patient records with computational linguistics. The framework aims to promote implementing personalized care in diagnostics, decision making, patient engagement and self-care. We describe some analysis methods of computational linguistics, natural language processing, statistics, algorithms and data mining. We have built a prototype program enabling representing and modifying health-related knowledge structures for purposes of prevention, diagnosis and care. For 25 most common diagnosis names we have identified dependencies of core symptom concepts in a conceptual co-occurrence network of 57 679 unique conceptual links about healthcare guidelines.Peer reviewe

Development and validation of the ACE tool: Assessing medical trainees' competency in evidence based medicine

BACKGROUND: While a variety of instruments have been developed to assess knowledge and skills in evidence based medicine (EBM), few assess all aspects of EBM - including knowledge, skills attitudes and behaviour - or have been psychometrically evaluated. The aim of this study was to develop and validate an instrument that evaluates medical trainees’ competency in EBM across knowledge, skills and attitude. METHODS: The ‘Assessing Competency in EBM’ (ACE) tool was developed by the authors, with content and face validity assessed by expert opinion. A cross-sectional sample of 342 medical trainees representing ‘novice’, ‘intermediate’ and ‘advanced’ EBM trainees were recruited to complete the ACE tool. Construct validity, item difficulty, internal reliability and item discrimination were analysed. RESULTS: We recruited 98 EBM-novice, 108 EBM-intermediate and 136 EBM-advanced participants. A statistically significant difference in the total ACE score was observed and corresponded to the level of training: on a 0-15-point test, the mean ACE scores were 8.6 for EBM-novice; 9.5 for EBM-intermediate; and 10.4 for EBM-advanced (p < 0.0001). Individual item discrimination was excellent (Item Discrimination Index ranging from 0.37 to 0.84), with internal reliability consistent across all but three items (Item Total Correlations were all positive ranging from 0.14 to 0.20). CONCLUSION: The 15-item ACE tool is a reliable and valid instrument to assess medical trainees’ competency in EBM. The ACE tool provides a novel assessment that measures user performance across the four main steps of EBM. To provide a complete suite of instruments to assess EBM competency across various patient scenarios, future refinement of the ACE instrument should include further scenarios across harm, diagnosis and prognosis

Derivation of diagnostic models based on formalized process knowledge

© IFAC.Industrial systems are vulnerable to faults. Early and accurate detection and diagnosis in production systems can minimize down-time, increase the safety of the plant operation, and reduce manufacturing costs. Knowledge- and model-based approaches to automated fault detection and diagnosis have been demonstrated to be suitable for fault cause analysis within a broad range of industrial processes and research case studies. However, the implementation of these methods demands a complex and error-prone development phase, especially due to the extensive efforts required during the derivation of models and their respective validation. In an effort to reduce such modeling complexity, this paper presents a structured causal modeling approach to supporting the derivation of diagnostic models based on formalized process knowledge. The method described herein exploits the Formalized Process Description Guideline VDI/VDE 3682 to establish causal relations among key-process variables, develops an extension of the Signed Digraph model combined with the use of fuzzy set theory to allow more accurate causality descriptions, and proposes a representation of the resulting diagnostic model in CAEX/AutomationML targeting dynamic data access, portability, and seamless information exchange

Providing decision support for the condition-based maintenance of circuit breakers through data mining of trip coil current signatures

The focus of this paper centers on the condition assessment of 11kV-33kV distribution circuit breakers from the analysis of their trip coil current signatures captured using an innovative condition monitoring technology developed by others. Using available expert knowledge in conjunction with a structured process of data mining, thresholds associated with features representing each stage of a circuit breaker's operation may be defined and used to characterize varying states of circuit breaker condition. Knowledge and understanding of satisfactory and unsatisfactory breaker condition can be gained and made explicit from the analysis of captured trip signature data and subsequently used to form the basis of condition assessment and diagnostic rules implemented in a decision support system, used to inform condition-based decisions affecting circuit breaker maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of SP Power System's in-service circuit breakers. This knowledge then forms the basis of a decision support system for the condition assessment of these circuit breakers during routine trip testing