483 research outputs found
AI-assisted patent prior art searching - feasibility study
This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy
AI-assisted patent prior art searching - feasibility study
This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy
Cross-language Information Retrieval
Two key assumptions shape the usual view of ranked retrieval: (1) that the
searcher can choose words for their query that might appear in the documents
that they wish to see, and (2) that ranking retrieved documents will suffice
because the searcher will be able to recognize those which they wished to find.
When the documents to be searched are in a language not known by the searcher,
neither assumption is true. In such cases, Cross-Language Information Retrieval
(CLIR) is needed. This chapter reviews the state of the art for CLIR and
outlines some open research questions.Comment: 49 pages, 0 figure
Generative Transformers for Design Concept Generation
Generating novel and useful concepts is essential during the early design
stage to explore a large variety of design opportunities, which usually
requires advanced design thinking ability and a wide range of knowledge from
designers. Growing works on computer-aided tools have explored the retrieval of
knowledge and heuristics from design data. However, they only provide stimuli
to inspire designers from limited aspects. This study explores the recent
advance of the natural language generation (NLG) technique in the artificial
intelligence (AI) field to automate the early-stage design concept generation.
Specifically, a novel approach utilizing the generative pre-trained transformer
(GPT) is proposed to leverage the knowledge and reasoning from textual data and
transform them into new concepts in understandable language. Three concept
generation tasks are defined to leverage different knowledge and reasoning:
domain knowledge synthesis, problem-driven synthesis, and analogy-driven
synthesis. The experiments with both human and data-driven evaluation show good
performance in generating novel and useful concepts.Comment: Accepted by J. Comput. Inf. Sci. En
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Computational Approaches to Assisting Patients\u27 Medical Comprehension from Electronic Health Records
Patient-centered care has been established as a fundamental approach to improve the quality of health care in a seminal report by the Institute of Medicine published at the start of the century. Improved access to health information and demand for greater transparency contributed to its move into the mainstream. Research has also demonstrated that actively involving patients in the management of their own health can lead to better outcomes, and potentially lower costs. However, despite the efforts in many areas of medicine to embrace patient-centered care, engaging patients is still considered a challenge. One of the barriers is the lack of effective tools to help patients understand their health conditions, options and their consequences.
Patient portals are now widely adopted by hospitals and other healthcare practices to provide patients with the capabilities to view their own Electronic Health Records. They are a rich resource of information for patients. However, the language in the records are generally difficult for patients without training in medicine to understand. Furthermore, the amount of information can often be overwhelming as well. In this work, we propose computational approaches to foster patient engagement from three aspects by exploiting the rich information in the medical records.
First, we design a framework to automatically generate health literacy instruments to measure a patient\u27s literacy levels. This framework exploits readily available large scale corpora to generate instruments in a commonly used test format. Second, we investigate methods that can determine the readability of complex documents such as health records. We propose to rank document readability, instead of assigning a grade level or a pre-defined difficulty category. Lastly, we examine the problem of finding targeted educational materials to facilitate patient comprehension of medical notes. We study methods to formulate effective queries from specialized and long clinical narratives. In addition, we propose a neural network based method to identify medical concepts that are important to patients.
The three aspects of this work address the issues of the overabundance and technical complexity of medical language in health records. We demonstrate that our approaches are effective with various experiments and evaluation metric
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