Ontology driven clinical decision support for early diagnostic recommendations

Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Composite Ontology-Based Medical Diagnosis Decision Support System Framework

Current medical decision support systems have evolved from the automation of medical decision routines to improving the quality of health care services. Knowledge-based systems, compared to conventional data-driven techniques, are promising to support medical decision making. However, knowledge acquisition is usually a bottleneck in the process of developing such systemsOne possibility for acquiring medical knowledge, particularly tacit knowledge, is to use data or cases in both syntactic and semantic ways. Case-based Reasoning (CBR) methodology provides a practical way of problem solving with recalled knowledge memory of solved cases. To reduce the difficulty of knowledge acquisition, this paper proposes a design of the system framework that utilizes the simplified medical knowledge:disease-symptom ontology for prediagnosis, given patients symptoms and signs as input. In the first stage, simple pattern matching is used to gather candidate diseases in diagnosis. Following that, case-based reasoning is used to refine diagnostic decision. The case base is structured with ontological knowledge model. The case retrieval process is based on semantic similarity. The diagnostic system uses a composite knowledge base, and will allow automated diagnosis recommendation. The system framework also aims at facilitating semantic explanations to the solution derived

ODDIN: ontology-driven differential diagnosis based on logical inference and probabilistic refinements

Medical differential diagnosis (ddx) is based on the estimation of multiple distinct parameters in order to determine the most probable diagnosis. Building an intelligent medical differential diagnosis system implies using a number of knowledge based technologies which avoid ambiguity, such as ontologies rep resenting specific structured information, but also strategies such as computation of probabilities of var ious factors and logical inference, whose combination outperforms similar approaches. This paper presents ODDIN, an ontology driven medical diagnosis system which applies the aforementioned strat egies. The architecture and proof of concept implementation is described, and results of the evaluation are discussed.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project SONAR (TSI-340000-2007-212), GODO2 (TSI-020100-2008-564) and SONAR2 (TSI-020100-2008-665), under the PIBES project of the Spanish Committee of Education & Science (TEC2006-12365-C02-01) and the MID-CBR project of the Spanish Committee of Education & Science (TIN2006-15140-C03-02).Publicad

Semantic web system for differential diagnosis recommendations

There is a growing realization that healthcare is a knowledge-intensive field. The ability to capture and leverage semantics via inference or query processing is crucial for supporting the various required processes in both primary (e.g. disease diagnosis) and long term care (e.g. predictive and preventive diagnosis). Given the wide canvas and the relatively frequent knowledge changes that occur in this area, we need to take advantage of the new trends in Semantic Web technologies. In particular, the power of ontologies allows us to share medical research and provide suitable support to physician's practices. There is also a need to integrate these technologies within the currently used healthcare practices. In particular the use of semantic web technologies is highly demanded within the clinicians' differential diagnosis process and the clinical pathways disease management procedures as well as to aid the predictive/preventative measures used by healthcare professionals

Data integration in eHealth: a domain/disease specific roadmap

The paper documents a series of data integration workshops held in 2006 at the UK National e-Science Centre, summarizing a range of the problem/solution scenarios in multi-site and multi-scale data integration with six HealthGrid projects using schizophrenia as a domain-specific test case. It outlines emerging strategies, recommendations and objectives for collaboration on shared ontology-building and harmonization of data for multi-site trials in this domain

System upgrade: realising the vision for UK education

A report summarising the findings of the TEL programme in the wider context of technology-enhanced learning and offering recommendations for future strategy in the area was launched on 13th June at the House of Lords to a group of policymakers, technologists and practitioners chaired by Lord Knight. The report – a major outcome of the programme – is written by TEL director Professor Richard Noss and a team of experts in various fields of technology-enhanced learning. The report features the programme’s 12 recommendations for using technology-enhanced learning to upgrade UK education

Virtual Coaching and Deliberate Practice to Enhance Medical Students\u27 Clinical Reasoning during Oral Case Presentations

ABSTRACT: Introduction Oral case presentations (OP) provide an opportunity for medical students to practice clinical reasoning and communication skills, and for faculty to provide assessment. Specific teaching strategies are needed to improve students’ OP skills. Objective To compare the effectiveness of Virtual Coaching (VC) to Small Group (SG) discussion or Traditional Feedback (TF/control) in improving clinical reasoning during OP, using a validated PBEAR (Problem Representation, Background Evidence, Analysis, Recommendation) tool. Design/Methods Students from two medical schools were randomly assigned to three groups during their inpatient pediatric clerkship. All completed an eLearning module about using illness scripts to promote clinical reasoning and presenting in the PBEAR format. TF/control students completed online “Aquifer” cases; VC students recorded abstracted data from the same cases with on-line faculty feedback and self-reflection; SG students attended faculty facilitated discussions of the same cases. Students were video recorded presenting pre- and post-curriculum cases. Reviewers blinded to assignment groups rated pre and post videos with the PBEAR OP tool. Results The overall score and sub-scale scores improved for all groups. VC students significantly improved in the Analysis subscale compared to SG or controls. Students rated the SG teaching sessions as more enjoyable and effective in improving their clinical reasoning and presentation skills. Conclusions A blended learning curriculum using VC significantly improved students’ clinical reasoning as assessed by the Analysis subscale

Ontology-based clinical decision support system applied on diabetes

Master's thesis Information- and communication technology IKT590 - University of Agder 2017Medical diagnosis is a multi-step process which is complex as it requires the consideration of many factors. Additionally, the accuracy of diagnosis varies depending on the skill and knowledge a physician has in the medical field. Using ICT solution, the physicians can be assisted so that they can make an accurate decision. Many applications have been developed to enhance physician performance and improve the patient outcome, however, the quality of these applications varies depending on knowledge representation methodology and reasoning approach adopted. Nowadays, ontology is being used in many clinical decision support as it formally represents the concepts and relationships of terms associated with medical domains which in turn improves the information processing, retrieval, and decision support. However, some of these applications do not explain their reasoning process; their knowledge base may not be built using the standard medical ontologies and they build ontology but fail to develop an application that a physician can use. The main objective of this thesis was to investigate how an ontology and its generic tools can be used to overcome the above challenges; how diagnostic can be formally represented and to implement a clinical decision support system that uses that ontology and diagnostic criteria to assist physician when making a diagnosis of diabetes mellitus. The ontology extends DDO and has been designed in protégé 5.0.0 OWL-DL. 19 rules were created based on diabetes diagnostic criteria by using jena rule syntax and forward chaining inference. Furthermore, the system uses the jena rule engine to reason with patient data from ontology against the rules defined in a rule file to generate a patient diagnosis, recommendation and decision explanation based on the patient vital signs and blood glucose test result. As result, 4 patient’s case was successful diagnosed; recommendation and decision explanation produced by the system undoubtedly matched the patient diagnosis. Overall system result is the same as the one expected by the physician, thus, makes it an expert system. The ontology supports the interoperability between CDSS and healthcare system and can be used for the management of the patient. Keywords: Clinical decision support system, diagnostic criteria, semantic web, ontology, diagnosi