2,810 research outputs found
Explanation-Based Auditing
To comply with emerging privacy laws and regulations, it has become common
for applications like electronic health records systems (EHRs) to collect
access logs, which record each time a user (e.g., a hospital employee) accesses
a piece of sensitive data (e.g., a patient record). Using the access log, it is
easy to answer simple queries (e.g., Who accessed Alice's medical record?), but
this often does not provide enough information. In addition to learning who
accessed their medical records, patients will likely want to understand why
each access occurred. In this paper, we introduce the problem of generating
explanations for individual records in an access log. The problem is motivated
by user-centric auditing applications, and it also provides a novel approach to
misuse detection. We develop a framework for modeling explanations which is
based on a fundamental observation: For certain classes of databases, including
EHRs, the reason for most data accesses can be inferred from data stored
elsewhere in the database. For example, if Alice has an appointment with Dr.
Dave, this information is stored in the database, and it explains why Dr. Dave
looked at Alice's record. Large numbers of data accesses can be explained using
general forms called explanation templates. Rather than requiring an
administrator to manually specify explanation templates, we propose a set of
algorithms for automatically discovering frequent templates from the database
(i.e., those that explain a large number of accesses). We also propose
techniques for inferring collaborative user groups, which can be used to
enhance the quality of the discovered explanations. Finally, we have evaluated
our proposed techniques using an access log and data from the University of
Michigan Health System. Our results demonstrate that in practice we can provide
explanations for over 94% of data accesses in the log.Comment: VLDB201
PathwAI Systems in Healthcare – a Framework for Coupling AI and Pathway-based Health Information Systems
Pathway-based Health Information Systems (HIS) enable planning, execution and improvement of standardized care processes. Adaptive behavior and learning effects are taken to a new level by advances in Artificial Intelligence (AI). Yet, design support to unlock synergies from coupling pathway-based HIS with AI is lacking. This Umbrella Review identifies applied purposes of AI in healthcare, describes the relation to pathway-based HIS, and derives a PathwAI Framework as design support for future research and development activities. Previous findings already provide a large base of approaches to realize personalized care pathways and improve coordination and business operations. Furthermore, potentials for designing learning health systems at micro, meso, and macro levels are formulated, but there is still greater opportunity for future research and design. Pathway-based HIS in this context can not only provide interpretable and interoperable data input, but can be conceptual as well as operational receivers of artificially generated knowledge
Clinical information extraction for preterm birth risk prediction
This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed. Based on an annotated collection of notes, we trained and evaluated information extraction components to discover clinical entities such as symptoms, events, anatomical sites and procedures, as well as attributes linked to these clinical entities. In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in combination with structured information from electronic health records
A Systematic Review of Knowledge Visualization Approaches Using Big Data Methodology for Clinical Decision Support
This chapter reports on results from a systematic review of peer-reviewed studies related to big data knowledge visualization for clinical decision support (CDS). The aims were to identify and synthesize sources of big data in knowledge visualization, identify visualization interactivity approaches for CDS, and summarize outcomes. Searches were conducted via PubMed, Embase, Ebscohost, CINAHL, Medline, Web of Science, and IEEE Xplore in April 2019, using search terms representing concepts of: big data, knowledge visualization, and clinical decision support. A Google Scholar gray literature search was also conducted. All references were screened for eligibility. Our review returned 3252 references, with 17 studies remaining after screening. Data were extracted and coded from these studies and analyzed using a PICOS framework. The most common audience intended for the studies was healthcare providers (n = 16); the most common source of big data was electronic health records (EHRs) (n = 12), followed by microbiology/pathology laboratory data (n = 8). The most common intervention type was some form of analysis platform/tool (n = 7). We identified and classified studies by visualization type, user intent, big data platforms and tools used, big data analytics methods, and outcomes from big data knowledge visualization of CDS applications
Big data analytics for preventive medicine
© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations
Design considerations for a hierarchical semantic compositional framework for medical natural language understanding
Medical natural language processing (NLP) systems are a key enabling
technology for transforming Big Data from clinical report repositories to
information used to support disease models and validate intervention methods.
However, current medical NLP systems fall considerably short when faced with
the task of logically interpreting clinical text. In this paper, we describe a
framework inspired by mechanisms of human cognition in an attempt to jump the
NLP performance curve. The design centers about a hierarchical semantic
compositional model (HSCM) which provides an internal substrate for guiding the
interpretation process. The paper describes insights from four key cognitive
aspects including semantic memory, semantic composition, semantic activation,
and hierarchical predictive coding. We discuss the design of a generative
semantic model and an associated semantic parser used to transform a free-text
sentence into a logical representation of its meaning. The paper discusses
supportive and antagonistic arguments for the key features of the architecture
as a long-term foundational framework
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