Natural Language to SQL Generation for Observational Study Designs: Current Challenges and Possible Directions (Preprint)

2020 ◽  
Author(s):  
Han Wang ◽  
Wesley Yeung ◽  
Mengling Feng
Keyword(s):  
Natural Language ◽  
Observational Studies ◽  
Relational Databases ◽  
Query Language ◽  
Data Extraction ◽  
Process Data ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Study Designs

UNSTRUCTURED Electronic Health Record (EHR) systems used in hospitals and healthcare institutes generate vast amounts of data stored in relational databases. Structured Query Language (SQL) is a common language used to update, extract and pre-process data in EHR databases. Pre-processing is a necessary step before statistical modeling and causal inference studies can be carried out in observational studies. Data extraction and pre-processing using SQL require a collaborative effort between data engineers and researchers such as clinicians or biostatisticians. Natural Language to SQL (NL2SQL) models converts study designs in natural language to SQL queries to obtain the desired cohort and risk factors. While they cannot completely replace the need for cross-disciplinary collaboration, they have the potential to enable clinicians and biostatisticians who are not trained in SQL to explore EHR databases on their own and reduce the burden placed on data engineers by automating less-complex tasks. There has been substantial research on NL2SQL tasks on general knowledge databases but their application in EHR databases that contain domain-specific knowledge are not well studied. In this paper, we will introduce the general NL2SQL tasks, and discuss in-depth about the potential challenges in developing NL2SQL tools for EHR databases.

scholarly journals Robot Learning of Domain Specific Knowledge from Natural Language Sources

10.5772/10249 ◽  
2010 ◽  
Author(s):  
Ines eh ◽  
Sandi Pohorec ◽  
Marjan Mernik ◽  
Milan Zorm
Keyword(s):  
Natural Language ◽  
Robot Learning ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge

‘I’m an expert in me and I know what I can cope with’: Patient expertise in rheumatoid arthritis

2014 ◽  
Vol 10 (3) ◽  
pp. 249-261 ◽  
Author(s):  
Tessa Sanderson ◽  
Jo Angouri
Keyword(s):  
Rheumatoid Arthritis ◽  
Meta Analysis ◽  
Healthcare Systems ◽  
Specific Knowledge ◽  
Active Involvement ◽  
Domain Specific ◽  
Access To Health ◽  
Patient Expertise ◽  
Domain Specific Knowledge ◽  
The Uk

The active involvement of patients in decision-making and the focus on patient expertise in managing chronic illness constitutes a priority in many healthcare systems including the NHS in the UK. With easier access to health information, patients are almost expected to be (or present self) as an ‘expert patient’ (Ziebland 2004). This paper draws on the meta-analysis of interview data collected for identifying treatment outcomes important to patients with rheumatoid arthritis (RA). Taking a discourse approach to identity, the discussion focuses on the resources used in the negotiation and co-construction of expert identities, including domain-specific knowledge, access to institutional resources, and ability to self-manage. The analysis shows that expertise is both projected (institutionally sanctioned) and claimed by the patient (self-defined). We close the paper by highlighting the limitations of our pilot study and suggest avenues for further research.

scholarly journals Domain-specific knowledge and memory performance: A comparison of high- and low-aptitude children.

1989 ◽  
Vol 81 (3) ◽  
pp. 306-312 ◽  
Author(s):  
Wolfgang Schneider ◽  
Joachim Körkel ◽  
Franz E. Weinert
Keyword(s):  
Memory Performance ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge

Domain-Specific Knowledge Systems in the Brain: The Animate-Inanimate Distinction

1998 ◽  
Vol 10 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Alfonso Caramazza ◽  
Jennifer R. Shelton
Keyword(s):  
Knowledge Systems ◽  
Neural Mechanisms ◽  
Specific Knowledge ◽  
Knowledge Framework ◽  
Semantic Categories ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Cognitive Neuropsychological ◽  
The Brain ◽  
Selective Impairment

We claim that the animate and inanimate conceptual categories represent evolutionarily adapted domain-specific knowledge systems that are subserved by distinct neural mechanisms, thereby allowing for their selective impairment in conditions of brain damage. On this view, (some of) the category-specific deficits that have recently been reported in the cognitive neuropsychological literature—for example, the selective damage or sparing of knowledge about animals—are truly categorical effects. Here, we articulate and defend this thesis against the dominant, reductionist theory of category-specific deficits, which holds that the categorical nature of the deficits is the result of selective damage to noncategorically organized visual or functional semantic subsystems. On the latter view, the sensory/functional dimension provides the fundamental organizing principle of the semantic system. Since, according to the latter theory, sensory and functional properties are differentially important in determining the meaning of the members of different semantic categories, selective damage to the visual or the functional semantic subsystem will result in a category-like deficit. A review of the literature and the results of a new case of category-specific deficit will show that the domain-specific knowledge framework provides a better account of category-specific deficits than the sensory/functional dichotomy theory.

scholarly journals Toward Knowledge Management for Smart Manufacturing

2017 ◽  
Vol 17 (3) ◽  
Author(s):  
Shaw C. Feng ◽  
William Z. Bernstein ◽  
Thomas Hedberg ◽  
Allison Barnard Feeney
Keyword(s):  
Knowledge Management ◽  
Design Process ◽  
Process Planning ◽  
Smart Manufacturing ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Knowledge Objects ◽  
Planning Production

The need for capturing knowledge in the digital form in design, process planning, production, and inspection has increasingly become an issue in manufacturing industries as the variety and complexity of product lifecycle applications increase. Both knowledge and data need to be well managed for quality assurance, lifecycle impact assessment, and design improvement. Some technical barriers exist today that inhibit industry from fully utilizing design, planning, processing, and inspection knowledge. The primary barrier is a lack of a well-accepted mechanism that enables users to integrate data and knowledge. This paper prescribes knowledge management to address a lack of mechanisms for integrating, sharing, and updating domain-specific knowledge in smart manufacturing (SM). Aspects of the knowledge constructs include conceptual design, detailed design, process planning, material property, production, and inspection. The main contribution of this paper is to provide a methodology on what knowledge manufacturing organizations access, update, and archive in the context of SM. The case study in this paper provides some example knowledge objects to enable SM.

scholarly journals Cross-Fertilizing Deep Web Analysis and Ontology Enrichment

2017 ◽  
Author(s):  
Marilena Oita ◽  
Antoine Amarilli ◽  
Pierre Senellart
Keyword(s):  
Domain Knowledge ◽  
Deep Web ◽  
Web Pages ◽  
Complete Understanding ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Web Crawlers ◽  
New Perspective ◽  
The Impact

Deep Web databases, whose content is presented as dynamically-generated Web pages hidden behind forms, have mostly been left unindexed by search engine crawlers. In order to automatically explore this mass of information, many current techniques assume the existence of domain knowledge, which is costly to create and maintain. In this article, we present a new perspective on form understanding and deep Web data acquisition that does not require any domain-specific knowledge. Unlike previous approaches, we do not perform the various steps in the process (e.g., form understanding, record identification, attribute labeling) independently but integrate them to achieve a more complete understanding of deep Web sources. Through information extraction techniques and using the form itself for validation, we reconcile input and output schemas in a labeled graph which is further aligned with a generic ontology. The impact of this alignment is threefold: first, the resulting semantic infrastructure associated with the form can assist Web crawlers when probing the form for content indexing; second, attributes of response pages are labeled by matching known ontology instances, and relations between attributes are uncovered; and third, we enrich the generic ontology with facts from the deep Web.

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