1,254 research outputs found
Infectious Disease Ontology
Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain
Co-design of human-centered, explainable AI for clinical decision support
eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models, and the way such explanations are presented to users, i.e., the explanation user interface. Despite its importance, the second aspect has received limited attention so far in the literature. Effective AI explanation interfaces are fundamental for allowing human decision-makers to take advantage and oversee high-risk AI systems effectively. Following an iterative design approach, we present the first cycle of prototyping-testing-redesigning of an explainable AI technique, and its explanation user interface for clinical Decision Support Systems (DSS). We first present an XAI technique that meets the technical requirements of the healthcare domain: sequential, ontology-linked patient data, and multi-label classification tasks. We demonstrate its applicability to explain a clinical DSS, and we design a first prototype of an explanation user interface. Next, we test such a prototype with healthcare providers and collect their feedback, with a two-fold outcome: first, we obtain evidence that explanations increase users’ trust in the XAI system, and second, we obtain useful insights on the perceived deficiencies of their interaction with the system, so that we can re-design a better, more human-centered explanation interface
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
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records
Survival analysis plays a crucial role in many healthcare decisions, where
the risk prediction for the events of interest can support an informative
outlook for a patient's medical journey. Given the existence of data censoring,
an effective way of survival analysis is to enforce the pairwise temporal
concordance between censored and observed data, aiming to utilize the time
interval before censoring as partially observed time-to-event labels for
supervised learning. Although existing studies mostly employed ranking methods
to pursue an ordering objective, contrastive methods which learn a
discriminative embedding by having data contrast against each other, have not
been explored thoroughly for survival analysis. Therefore, in this paper, we
propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv)
analysis framework that utilizes survival durations from both censored and
observed data to define temporal distinctiveness and construct negative sample
pairs with adjustable hardness for contrastive learning. Specifically, we first
use an ontological encoder and a sequential self-attention encoder to represent
the longitudinal EHR data with rich contexts. Second, we design a temporal
contrastive loss to capture varying survival durations in a supervised setting
through a hardness-aware negative sampling mechanism. Last, we incorporate the
contrastive task into the time-to-event predictive task with multiple loss
components. We conduct extensive experiments using a large EHR dataset to
forecast the risk of hospitalized patients who are in danger of developing
acute kidney injury (AKI), a critical and urgent medical condition. The
effectiveness and explainability of the proposed model are validated through
comprehensive quantitative and qualitative studies.Comment: This paper has been accepted for publication at the CIKM 2023
conferenc
eXplainable AI for trustworthy healthcare applications
Acknowledging that AI will inevitably become a central element of clinical practice,
this thesis investigates the role of eXplainable AI (XAI) techniques in developing
trustworthy AI applications in healthcare. The first part of this thesis focuses on the
societal, ethical, and legal aspects of the use of AI in healthcare. It first compares
the different approaches to AI ethics worldwide and then focuses on the practical
implications of the European ethical and legal guidelines for AI applications in
healthcare. The second part of the thesis explores how XAI techniques can help meet
three key requirements identified in the initial analysis: transparency, auditability,
and human oversight. The technical transparency requirement is tackled by enabling
explanatory techniques to deal with common healthcare data characteristics
and tailor them to the medical field. In this regard, this thesis presents two novel
XAI techniques that incrementally reach this goal by first focusing on multi-label
predictive algorithms and then tackling sequential data and incorporating domainspecific
knowledge in the explanation process. This thesis then analyzes the ability
to leverage the developed XAI technique to audit a fictional commercial black-box
clinical decision support system (DSS). Finally, the thesis studies AI explanation’s
ability to effectively enable human oversight by studying the impact of explanations
on the decision-making process of healthcare professionals
Association Patterns of Ontological Features Signify Electronic Health Records in Liver Cancer
published_or_final_versio
Improving ICD-based semantic similarity by accounting for varying degrees of comorbidity
Finding similar patients is a common objective in precision medicine,
facilitating treatment outcome assessment and clinical decision support.
Choosing widely-available patient features and appropriate mathematical methods
for similarity calculations is crucial. International Statistical
Classification of Diseases and Related Health Problems (ICD) codes are used
worldwide to encode diseases and are available for nearly all patients.
Aggregated as sets consisting of primary and secondary diagnoses they can
display a degree of comorbidity and reveal comorbidity patterns. It is possible
to compute the similarity of patients based on their ICD codes by using
semantic similarity algorithms. These algorithms have been traditionally
evaluated using a single-term expert rated data set.
However, real-word patient data often display varying degrees of documented
comorbidities that might impair algorithm performance. To account for this, we
present a scale term that considers documented comorbidity-variance. In this
work, we compared the performance of 80 combinations of established algorithms
in terms of semantic similarity based on ICD-code sets. The sets have been
extracted from patients with a C25.X (pancreatic cancer) primary diagnosis and
provide a variety of different combinations of ICD-codes. Using our scale term
we yielded the best results with a combination of level-based information
content, Leacock & Chodorow concept similarity and bipartite graph matching for
the set similarities reaching a correlation of 0.75 with our expert's ground
truth. Our results highlight the importance of accounting for comorbidity
variance while demonstrating how well current semantic similarity algorithms
perform.Comment: 11 pages, 6 figures, 1 tabl
Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Automatically extracting useful information from electronic medical records
along with conducting disease diagnoses is a promising task for both clinical
decision support(CDS) and neural language processing(NLP). Most of the existing
systems are based on artificially constructed knowledge bases, and then
auxiliary diagnosis is done by rule matching. In this study, we present a
clinical intelligent decision approach based on Convolutional Neural
Networks(CNN), which can automatically extract high-level semantic information
of electronic medical records and then perform automatic diagnosis without
artificial construction of rules or knowledge bases. We use collected 18,590
copies of the real-world clinical electronic medical records to train and test
the proposed model. Experimental results show that the proposed model can
achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using
convolutional neural network to automatically learn high-level semantic
features of electronic medical records and then conduct assist diagnosis is
feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report
- …