18,834 research outputs found
Recommended from our members
Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems
There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings
Planning strategically, designing architecturally : a framework for digital library services
In an era of unprecedented technological innovation and evolving user expectations and information seeking behaviour, we are arguably now an online society, with digital services increasingly common and increasingly preferred. As a trusted information provider, libraries are in an advantageous position to respond, but this requires integrated strategic and enterprise architecture planning, for information technology (IT) has evolved from a support role to a strategic role, providing the core management systems, communication networks, and delivery channels of the modern library. Further, IT components do not function in isolation from one another, but are interdependent elements of distributed and multidimensional systems encompassing people, processes, and technologies, which must consider social, economic, legal, organisational, and ergonomic requirements and relationships, as well as being logically sound from a technical perspective. Strategic planning provides direction, while enterprise architecture strategically aligns and holistically integrates business and information system architectures. While challenging, such integrated planning should be regarded as an opportunity for the library to evolve as an enterprise in the digital age, or at minimum, to simply keep pace with societal change and alternative service providers. Without strategy, a library risks being directed by outside forces with independent motivations and inadequate understanding of its broader societal role. Without enterprise architecture, it risks technological disparity, redundancy, and obsolescence. Adopting an interdisciplinary approach, this conceptual paper provides an integrated framework for strategic and architectural planning of digital library services. The concept of the library as an enterprise is also introduced
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Knowledge Transfer for Melanoma Screening with Deep Learning
Knowledge transfer impacts the performance of deep learning -- the state of
the art for image classification tasks, including automated melanoma screening.
Deep learning's greed for large amounts of training data poses a challenge for
medical tasks, which we can alleviate by recycling knowledge from models
trained on different tasks, in a scheme called transfer learning. Although much
of the best art on automated melanoma screening employs some form of transfer
learning, a systematic evaluation was missing. Here we investigate the presence
of transfer, from which task the transfer is sourced, and the application of
fine tuning (i.e., retraining of the deep learning model after transfer). We
also test the impact of picking deeper (and more expensive) models. Our results
favor deeper models, pre-trained over ImageNet, with fine-tuning, reaching an
AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated.Comment: 4 page
A canonical theory of dynamic decision-making
Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering
- …