55 research outputs found
Towards Context Driven Modularization of Large Biomedical Ontologies
Formal knowledge about human anatomy, radiology or diseases is necessary to support clinical applications such as medical image search. This machine processable knowledge can be acquired from biomedical domain ontologies, which however, are typically very large and complex models. Thus, their straightforward incorporation into the software applications becomes difficult. In this paper we discuss first ideas on a statistical approach for modularizing large medical ontologies and we prioritize the practical applicability aspect. The underlying assumption is that the application relevant ontology fragments, i.e. modules, can be identified by the statistical analysis of the ontology concepts in the domain corpus. Accordingly, we argue that most frequently occurring concepts in the domain corpus define the application context and can therefore potentially yield the relevant ontology modules. We illustrate our approach on an example case that involves a large ontology on human anatomy and report on our first manual experiments
A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology
To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiolog
A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology
To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiology
AI Hazard Management: A framework for the systematic management of root causes for AI risks
Recent advancements in the field of Artificial Intelligence (AI) establish
the basis to address challenging tasks. However, with the integration of AI,
new risks arise. Therefore, to benefit from its advantages, it is essential to
adequately handle the risks associated with AI. Existing risk management
processes in related fields, such as software systems, need to sufficiently
consider the specifics of AI. A key challenge is to systematically and
transparently identify and address AI risks' root causes - also called AI
hazards. This paper introduces the AI Hazard Management (AIHM) framework, which
provides a structured process to systematically identify, assess, and treat AI
hazards. The proposed process is conducted in parallel with the development to
ensure that any AI hazard is captured at the earliest possible stage of the AI
system's life cycle. In addition, to ensure the AI system's auditability, the
proposed framework systematically documents evidence that the potential impact
of identified AI hazards could be reduced to a tolerable level. The framework
builds upon an AI hazard list from a comprehensive state-of-the-art analysis.
Also, we provide a taxonomy that supports the optimal treatment of the
identified AI hazards. Additionally, we illustrate how the AIHM framework can
increase the overall quality of a power grid AI use case by systematically
reducing the impact of identified hazards to an acceptable level
Navigating AI innovation ecosystems in manufacturing: Shaping factors and their implications
Manufacturers often encounter challenges when implementing artificial intelligence (AI) in their manufacturing operations. Similar challenges with other digital transformation technologies have resulted in the emergence of innovation ecosystems. In this paper, we aim to demonstrate the emergence of AI innovation ecosystems and highlight the factors that influence their structure in manufacturing. To achieve this, we conducted a qualitative study of ten manufacturing case studies, analyzing different value propositions, activities, actors, and modules in AI ecosystems in the manufacturing sector. We first visualize the AI innovation ecosystems to showcase their structure and then discuss factors such as trustworthiness, scalability, simulation, and cloud that impact the ecosystem structure. Our study provides practitioners with a better understanding of the structure of AI ecosystems and their influencing factors. For researchers, we introduce influencing factors as a new part of the ecosystem-as-structure concept, which can lead to new research opportunities
Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods
Machine learning (ML) on graph-structured data has recently received deepened
interest in the context of intrusion detection in the cybersecurity domain. Due
to the increasing amounts of data generated by monitoring tools as well as more
and more sophisticated attacks, these ML methods are gaining traction.
Knowledge graphs and their corresponding learning techniques such as Graph
Neural Networks (GNNs) with their ability to seamlessly integrate data from
multiple domains using human-understandable vocabularies, are finding
application in the cybersecurity domain. However, similar to other
connectionist models, GNNs are lacking transparency in their decision making.
This is especially important as there tend to be a high number of false
positive alerts in the cybersecurity domain, such that triage needs to be done
by domain experts, requiring a lot of man power. Therefore, we are addressing
Explainable AI (XAI) for GNNs to enhance trust management by exploring
combining symbolic and sub-symbolic methods in the area of cybersecurity that
incorporate domain knowledge. We experimented with this approach by generating
explanations in an industrial demonstrator system. The proposed method is shown
to produce intuitive explanations for alerts for a diverse range of scenarios.
Not only do the explanations provide deeper insights into the alerts, but they
also lead to a reduction of false positive alerts by 66% and by 93% when
including the fidelity metric.Comment: arXiv admin note: text overlap with arXiv:2105.0874
Semantic representation of reported measurements in radiology
Background
In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. It is difficult to retrieve specific information from unstructured reports and to compare them over time. Therefore, a prototype was implemented that demonstrates the structured representation of findings, allowing selective review in consecutive examinations and thus more efficient comparison over time.
Methods
We developed a semantic Model for Clinical Information (MCI) based on existing ontologies from the Open Biological and Biomedical Ontologies (OBO) library. MCI is used for the integrated representation of measured image findings and medical knowledge about the normal size of anatomical entities. An integrated view of the radiology findings is realized by a prototype implementation of a ReportViewer. Further, RECIST (Response Evaluation Criteria In Solid Tumors) guidelines are implemented by SPARQL queries on MCI. The evaluation is based on two data sets of German radiology reports: An oncologic data set consisting of 2584 reports on 377 lymphoma patients and a mixed data set consisting of 6007 reports on diverse medical and surgical patients. All measurement findings were automatically classified as abnormal/normal using formalized medical background knowledge, i.e., knowledge that has been encoded into an ontology. A radiologist evaluated 813 classifications as correct or incorrect. All unclassified findings were evaluated as incorrect.
Results
The proposed approach allows the automatic classification of findings with an accuracy of 96.4 % for oncologic reports and 92.9 % for mixed reports. The ReportViewer permits efficient comparison of measured findings from consecutive examinations. The implementation of RECIST guidelines with SPARQL enhances the quality of the selection and comparison of target lesions as well as the corresponding treatment response evaluation.
Conclusions
The developed MCI enables an accurate integrated representation of reported measurements and medical knowledge. Thus, measurements can be automatically classified and integrated in different decision processes. The structured representation is suitable for improved integration of clinical findings during decision-making. The proposed ReportViewer provides a longitudinal overview of the measurements
Technical Research Priorities for Big Data
To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data
Towards a New Science of a Clinical Data Intelligence
In this paper we define Clinical Data Intelligence as the analysis of data
generated in the clinical routine with the goal of improving patient care. We
define a science of a Clinical Data Intelligence as a data analysis that
permits the derivation of scientific, i.e., generalizable and reliable results.
We argue that a science of a Clinical Data Intelligence is sensible in the
context of a Big Data analysis, i.e., with data from many patients and with
complete patient information. We discuss that Clinical Data Intelligence
requires the joint efforts of knowledge engineering, information extraction
(from textual and other unstructured data), and statistics and statistical
machine learning. We describe some of our main results as conjectures and
relate them to a recently funded research project involving two major German
university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and
Healthcare, 201
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