Developing an Ontology for Documenting Adverse Events While Avoiding Pitfalls

Ontologies promise more benefits than terminologies in terms of data annotation and computer-assisted reasoning, by defining a hierarchy of terms and their relations within a domain. Here, we present central insights related to the development of an ontology for documenting events during interoperative neuromonitoring (IOM), for which we used the Basic Formal Ontology (BFO) as an upper-level ontology. This work has the following two goals: to describe the development of the IOM ontology and to guide the practice with respect to documenting of biomedical events, as available ontologies pose difficulties on certain issues. We address the following issues: (i) differentiate between the sets documentation, identification, continuant and explanation, understanding, occurrent as we had problems in applying the available ontology of adverse events, (ii) covering diseases and injuries in a consistent way, and (iii) deciding on which level to define relations

What Kind of Ontologies Do We Need in the Biomedical Domain?

We tackle the question as to what sort of ontologies we primarily need in the biomedical domain. For this purpose, we will first provide a simple categorization of ontologies and describe an important use case related to modeling and documenting events. Then, the impact of using upper-level ontologies as a basis to address our use case will be shown in order to derive an answer to our research question. Although formal ontologies can serve as a starting point to understand conceptualization in a domain and facilitate interesting inferences, it is even more important to account for the dynamic and changing nature of knowledge. Being unconstrained by pre-defined categories and relationships can facilitate timely enrichment of a conceptual scheme and provide links and dependency structures in an informal manner. Semantic enrichment can be achieved by other mechanisms such as tagging or the creation of synsets as, for example, provided in WordNet

Library of model components for process simulation relevant to production activities, Prototype 1 versions

Production Economics,

Big Data Analytics: What Can Go Wrong

It is not uncommon to read that long-held beliefs about medical treatments have been dislodged by new studies. For example, there is now doubt as to whether women should undergo annual mammograms, previously a cornerstone of cancer screening. Hormone replacement therapy for menopausal women, once considered highly suspect in light of worrisome research findings, is now being reconsidered as a beneficial therapy. These reversals trouble and confuse many Americans. This Article explores why medical research findings can be erroneous and what can go wrong in the process of designing and conducting research studies. It provides readers with essential analytical tools and scientific vocabulary. The challenges of medical research include data quality deficiencies; selection, confounding, measurement, and confirmation biases; inadequate sample sizes; sampling errors; effect modifiers; and causal interactions, among others. All of these can cause researchers to mistake mere associations for causal relationships and to reach conclusions that are invalid and cannot be replicated in subsequent studies. Erroneous research findings can mislead legislators, regulators, and lawyers who use them for purposes of policy-making or litigation. Thus, understanding the pitfalls of big data analysis is important not only for scientists but also for anyone working with or reading about research studies, that is, for attorneys, health policy professionals, and the public at large

Semantic technologies for supporting KDD processes

209 p.Achieving a comfortable thermal situation within buildings with an efficient use of energy remains still an open challenge for most buildings. In this regard, IoT (Internet of Things) and KDD (Knowledge Discovery in Databases) processes may be combined to solve these problems, even though data analysts may feel overwhelmed by heterogeneity and volume of the data to be considered. Data analysts could benefit from an application assistant that supports them throughout the KDD process. This research work aims at supporting data analysts through the different KDD phases towards the achievement of energy efficiency and thermal comfort in tertiary buildings. To do so, the EEPSA (Energy Efficiency Prediction Semantic Assistant) is proposed, which aids data analysts discovering the most relevant variables for the matter at hand, and informs them about relationships among relevant data. This assistant leverages Semantic Technologies such as ontologies, ontology-driven rules and ontology-driven data access. More specifically, the EEPSA ontology is the cornerstone of the assistant. This ontology is developed on top of three ODPs (Ontology Design Patterns) and it is designed so that its customization to address similar problems in different types of buildings can be approached methodically

Data, Metrics and Monitoring in CGIAR - a strategic study

This document contains the final report of the Panel together with the ISPC commentary. The Panel Report presents an analysis of the current activities within CGIAR concerning data, metrics and indicators, and offers a series of recommendations to address the key issues and challenges identified

Positive health: The passport approach to improving continuity of care for low income South African chronic disease sufferers

Research Problem: The South African health system faces numerous challenges associated with its status as a middle-income developing nation. Wasteful expenditure and poor clinical outcomes arise from inefficient inter-organizational communication of patient information and the lack of a centralized health database. Research question: How does the experience of chronic disease patients with their health information inform the development of future health records in low income population groups? Proposition: Exploration of patient and health care workers experiences of medical records can inform their future development to enhance continuity of care. Objectives, methodology, procedures and outcome: Identification of an appropriate format, technological basis and functional design of a prototype medical record system by means of a phenomenological study conducted through in-depth interviews of patients and doctors in order to improve clinical care. Left and right hermeneutics were used to analyse the data and develop themes. Findings: Health records play a critical role in the clinics workflow processes, document the patients' management and clinical progress. They are an important intermediary in the relationship between the patient and the facility. Inefficiencies in the paper-based system lead to ineffective consultations, loss of continuity of care and discord between practitioners and patients. Improvement of the records format is required to provide ubiquitous access to health and improve patient health literacy

Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology

Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough exposure risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known “OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models”, with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

Bias in Artificial Intelligence (AI) is a critical and timely issue due to its sociological, economic and legal impact, as decisions made by biased algorithms could lead to unfair treatment of specific individuals or groups. Multiple surveys have emerged to provide a multidisciplinary view of bias or to review bias in specific areas such as social sciences, business research, criminal justice, or data mining. Given the ability of Semantic Web (SW) technologies to support multiple AI systems, we review the extent to which semantics can be a “tool” to address bias in different algorithmic scenarios. We provide an in-depth categorisation and analysis of bias assessment, representation, and mitigation approaches that use SW technologies. We discuss their potential in dealing with issues such as representing disparities of specific demographics or reducing data drifts, sparsity, and missing values. We find research works on AI bias that apply semantics mainly in information retrieval, recommendation and natural language processing applications and argue through multiple use cases that semantics can help deal with technical, sociological, and psychological challenges