Determinants of Long-term Economic Development: An Empirical Cross-country Study Involving Rough Sets Theory and Rule Induction

Empirical findings on determinants of long-term economic growth are numerous, sometimes inconsistent, highly exciting and still incomplete. The empirical analysis was almost exclusively carried out by standard econometrics. This study compares results gained by cross-country regressions as reported in the literature with those gained by the rough sets theory and rule induction. The main advantages of using rough sets are being able to classify classes and to discretize. Thus, we do not have to deal with distributional, independence, (log-)linearity, and many other assumptions, but can keep the data as they are. The main difference between regression results and rough sets is that most education and human capital indicators can be labeled as robust attributes. In addition, we find that political indicators enter in a non-linear fashion with respect to growth.Economic growth, Rough sets, Rule induction

Sequential Classification by Exploring Levels of Abstraction

AbstractIn the paper we describe a sequential classification scheme that iteratively explores levels of abstraction in the description of examples. These levels of abstraction represent attribute values of increasing precision. Specifically, we assume attribute values constitute an ontology (i.e., attribute value ontology) reflecting a domain-specific background knowledge, where more general values subsumes more precise ones. While there are approaches that consider levels of abstraction during learning, the novelty of our proposal consists in exploring levels of abstraction when classifying new examples. The described scheme is essential when tests that increase precision of example description are associated with costs – such a situation is often encountered in medical diagnosis. Experimental evaluation of the proposed classification scheme combined with ontological Bayes classifier (i.e., a näıve Bayes classifier expanded to handle attribute value ontologies) demonstrates that the classification accuracy obtained at higher levels of abstraction (i.e., more general description of classified examples) converges very quickly to the classification accuracy for classified examples represented precisely. This finding indicates we should be able to reduce the number of tests and thus limit their cost without deterioration of the prediction accuracy

Personalising Digital Health Behavior Change Interventions using Machine Learning and Domain Knowledge

We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI). Our proposed system predicts whether a patient will perform the targeted behavior and uses counterfactual examples with feature control to guide personalizsation of BCI. We use simulated patient data with varying levels of receptivity to intervention to arrive at the study design which would enable evaluation of our system.Comment: 8 pages,3 figures, Knowledge Representation for Health Care (HR4HC) 202

Can Large Language Models Augment a Biomedical Ontology with missing Concepts and Relations?

Ontologies play a crucial role in organizing and representing knowledge. However, even current ontologies do not encompass all relevant concepts and relationships. Here, we explore the potential of large language models (LLM) to expand an existing ontology in a semi-automated fashion. We demonstrate our approach on the biomedical ontology SNOMED-CT utilizing semantic relation types from the widely used UMLS semantic network. We propose a method that uses conversational interactions with an LLM to analyze clinical practice guidelines (CPGs) and detect the relationships among the new medical concepts that are not present in SNOMED-CT. Our initial experimentation with the conversational prompts yielded promising preliminary results given a manually generated gold standard, directing our future potential improvements.Comment: Presented as a short paper at the Knowledge Representation for Healthcare 2023 worksho

Decision Making by Emergency Room Physicians and Residents: Results From a Clinical Trial

Clinical decision-making is complex and uncertain and is dependent on accurate and timely information that is typically managed through Information Technology (IT) solutions. One particular class of IT that is becoming increasingly prevalent in the medical community is Clinical Decision Support Systems (CDSS). This paper will discuss results of the use of a CDSS that was developed for assisting triage decision making of pediatric abdominal pain in the Emergency department. We show how different user groups (staff physicians and residents) use the CDSS input variables in their triage decision making models

How Do Spinal Surgeons Perceive The Impact of Factors Used in Post-Surgical Complication Risk Scores?

When deciding about surgical treatment options, an important aspect of the decision-making process is the potential risk of complications. A risk assessment performed by a spinal surgeon is based on their knowledge of the best available evidence and on their own clinical experience. The objective of this work is to demonstrate the differences in the way spine surgeons perceive the importance of attributes used to calculate risk of post-operative and quantify the differences by building individual formal models of risk perceptions. We employ a preference-learning method - ROR-UTADIS - to build surgeon-specific additive value functions for risk of complications. Comparing these functions enables the identification and discussion of differences among personal perceptions of risk factors. Our results show there exist differences in surgeons\u27 perceived factors including primary diagnosis, type of surgery, patient\u27s age, body mass index, or presence of comorbidities

Is There a Consensus when Physicians Evaluate the Relevance of Retrieved Systematic Reviews?

Background: A significant challenge associated with practicing evidence-based medicine is to provide physicians with relevant clinical information when it is needed. At the same time it appears that the notion of relevance is subjective and its perception is affected by a number of contextual factors. Objectives: To assess to what extent physicians agree on the relevance of evidence in the form of systematic reviews for a common set of patient cases, and to identify possible contextual factors that influence their perception of relevance. Methods: A web-based survey was used where pediatric emergency physicians from multiple academic centers across Canada were asked to evaluate the relevance of systematic reviews retrieved automatically for 14 written case vignettes (paper patients). The vignettes were derived from prospective data describing pediatric patients with asthma exacerbations presenting at the emergency department. To limit the cognitive burden on respondents, the number of reviews associated with each vignette was limited to three. Results: Twenty-two academic emergency physicians with varying years of clinical practice completed the survey. There was no consensus in their evaluation of relevance of the retrieved reviews and physicians’ assessments ranged from very relevant to irrelevant evidence, with the majority of evaluations being somewhere in the middle. This indicates that the study participants did not share a notion of relevance uniformly. Further analysis of commentaries provided by the physicians allowed identifying three possible contextual factors: expected specificity of evidence (acute vs chronic condition), the terminology used in the systematic reviews, and the micro environment of clinical setting. Conclusion: There is no consensus among physicians with regards to what constitutes relevant clinical evidence for a given patient case. Subsequently, this finding suggests that evidence retrieval systems should allow for deep customization with regards to physician’s preferences and contextual factors, including differences in the micro environment of each clinical setting

Ideating Mobile Health Behavioral Support for Compliance to Therapy for Patients with Chronic Disease: A Case Study of Atrial Fibrillation Management

Poor patient compliance to therapy results in a worsening condition that often increases healthcare costs. In the MobiGuide project, we developed an evidence-based clinical decision-support system that delivered personalized reminders and recommendations to patients, helping to achieve higher therapy compliance. Yet compliance could still be improved and therefore building on the MobiGuide project experience, we designed a new component called the Motivational Patient Assistant (MPA) that is integrated within the MobiGuide architecture to further improve compliance. This component draws from psychological theories to provide behavioral support to improve patient engagement and thereby increasing patients\u27 compliance. Behavior modification interventions are delivered via mobile technology at patients\u27 home environments. Our approach was inspired by the IDEAS (Integrate, Design, Assess, and Share) framework for developing effective digital interventions to change health behavior; it goes beyond this approach by extending the Ideation phase\u27 concepts into concrete backend architectural components and graphical user-interface designs that implement behavioral interventions. We describe in detail our ideation approach and how it was applied to design the user interface of MPA for anticoagulation therapy for the atrial fibrillation patients. We report results of a preliminary evaluation involving patients and care providers that shows the potential usefulness of the MPA for improving compliance to anticoagulation therapy

A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment

We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes—condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific course content according to Bloom’s taxonomy. Customizing educational materials along these learning axes stimulates and sustains patients’ attention when learning about their conditions or treatment options. Our proposed framework creates a personalized course that prepares patients for their meetings with specialists and educates them about their prescribed treatment. We posit that the improvement in patients’ understanding of prescribed care will result in better outcomes and we validate that the constructs of our framework are appropriate for representing content and deriving personalized courses for two use cases: anticoagulation treatment of an atrial fibrillation patient and lower back pain management to treat a lumbar degenerative disc condition. We conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the framework among the target patient population and medical practitioners