15 Result(s)
within
Szymon Wilk
Database Management
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Chapter and Conference Paper
Comparision of Models Built Using AutoML and Data Fusion
Automated machine learning (AutoML) has made life easier for data analysts or scientists by providing quick insights into data by building machine learning (ML) models. AutoML techniques are applied to vast ar...
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Chapter and Conference Paper
Assessing the Impact of Distance Functions on K-Nearest Neighbours Imputation of Biomedical Datasets
In healthcare domains, dealing with missing data is crucial since absent observations compromise the reliability of decision support models. K-nearest neighbours imputation has proven beneficial since it takes...
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Chapter and Conference Paper
Fusion of Clinical Data: A Case Study to Predict the Type of Treatment of Bone Fractures
Clinical data is characterized not only by its constantly increasing volume but also by its diversity. Information collected in clinical information systems such as electronic health records is highly heteroge...
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Chapter and Conference Paper
Using First-Order Logic to Represent Clinical Practice Guidelines and to Mitigate Adverse Interactions
Clinical practice guidelines (CPGs) were originally designed to help with evidence-based management of a single disease and such single disease focus has impacted research on CPG computerization. This computer...
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Chapter and Conference Paper
Discovering the Preferences of Physicians with Regards to Rank-Ordered Medical Documents
The practice of evidence-based medicine involves consulting documents from repositories such as Scopus, PubMed, or the Cochrane Library. The most common approach for presenting retrieved documents is in the fo...
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Chapter and Conference Paper
A Constraint Logic Programming Approach to Identifying Inconsistencies in Clinical Practice Guidelines for Patients with Comorbidity
This paper describes a novel methodological approach to identifying inconsistencies when concurrently using multiple clinical practice guidelines. We discuss how to construct a formal guideline model using Con...
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Chapter and Conference Paper
Learning from Imbalanced Data in Presence of Noisy and Borderline Examples
In this paper we studied re-sampling methods for learning classifiers from imbalanced data. We carried out a series of experiments on artificial data sets to explore the impact of noisy and borderline examples...
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Chapter and Conference Paper
Integrating Selective Pre-processing of Imbalanced Data with Ivotes Ensemble
In the paper we present a new framework for improving classifiers learned from imbalanced data. This framework integrates the SPIDER method for selective data pre-processing with the Ivotes ensemble. The goal ...
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Chapter and Conference Paper
Experienced Physicians and Automatic Generation of Decision Rules from Clinical Data
Clinical Decision Support Systems embed data-driven decision models designed to represent clinical acumen of an experienced physician. We argue that eliminating physicians’ diagnostic biases from data improves...
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Chapter and Conference Paper
Selective Pre-processing of Imbalanced Data for Improving Classification Performance
In this paper we discuss problems of constructing classifiers from imbalanced data. We describe a new approach to selective pre-processing of imbalanced data which combines local over-sampling of the minority ...
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Chapter and Conference Paper
A Concept-Based Framework for Retrieving Evidence to Support Emergency Physician Decision Making at the Point of Care
The goal of evidence-based medicine is to uniformly apply evidence gained from scientific research to aspects of clinical practice. In order to achieve this goal, new applications that integrate increasingly d...
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Chapter and Conference Paper
Develo** a Decision Model for Asthma Exacerbations: Combining Rough Sets and Expert-Driven Selection of Clinical Attributes
The paper describes the development of a clinical decision model to help Emergency Department physicians assess the severity of pediatric asthma exacerbations. The model should support an early identification ...
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Chapter and Conference Paper
Mining Clinical Data: Selecting Decision Support Algorithm for the MET-AP System
We have developed an algorithm for triaging acute pediatric abdominal pain in the Emergency Department using the discovery-driven approach. This algorithm is embedded into the MET-AP (Mobile Emergency Triage –...
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Chapter and Conference Paper
Rough Set Methodology in Clinical Practice: Controlled Hospital Trial of the MET System
Acute abdominal pain in childhood is a common but diagnostically challenging problem facing Emergency Department personnel. Experienced physicians use a combination of key clinical attributes to assess and tri...
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Chapter and Conference Paper
Rough set based data exploration using ROSE system
This article briefly describes the process of data exploration based on rough set theory and also proposes ROSE system as a useful toolkit for doing such data analysis on PC computers.
