A framework of clinical recommender system with genomic information

Zhang, Q; Zhang, G; Lu, J; Lin, H

A framework of clinical recommender system with genomic information

Zhang, Q

Zhang, G

Lu, J

Lin, H

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Publisher:: WORLD SCIENTIFIC
Publication Type:: Conference Proceeding
Citation:: Developments of Artificial Intelligence Technologies in Computation and Robotics, 2020, pp. 522-529
Issue Date:: 2020-10

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Field	Value	Language
dc.contributor.author	Zhang, Q https://orcid.org/0000-0001-9977-8418
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.contributor.author	Lin, H
dc.date	2020-08-18
dc.date.accessioned	2021-04-23T03:27:20Z
dc.date.available	2021-04-23T03:27:20Z
dc.date.issued	2020-10
dc.identifier.citation	Developments of Artificial Intelligence Technologies in Computation and Robotics, 2020, pp. 522-529
dc.identifier.isbn	9789811223327
dc.identifier.uri	http://hdl.handle.net/10453/148297
dc.description.abstract	Clinicians make decisions that affect life and death, quality of life, every single day. It is important to support clinicians by discovering medical knowledge from the accumulated electronic health records (EHRs). The integration of genomic information and EHRs are long recognized by the medical community as the inherent feature of the disease. The demand for developing a clinical recommender system that is able to deal with both genomic and phenotypic data is urgent. This paper proposes a framework of clinical recommender system with genomic information, which is used in the clinical process and connects the four types of users: clinicians, patients, clinical labs, researchers. With models and methods in artificial intelligence (AI), five functions are designed in this framework: diagnosis prediction, disease risk prediction, test prediction, and event prediction. The proposed framework will help clinicians to make decisions on the next step in clinical care action for patients.
dc.language	en
dc.publisher	WORLD SCIENTIFIC
dc.relation.ispartof	Developments of Artificial Intelligence Technologies in Computation and Robotics
dc.relation.ispartof	International FLINS Conference
dc.relation.isbasedon	10.1142/9789811223334_0063
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	A framework of clinical recommender system with genomic information
dc.type	Conference Proceeding
utslib.location.activity	Cologne, Germany
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2021-10-01T00:00:00+1000Z
dc.date.updated	2021-04-23T03:27:19Z
pubs.finish-date	2020-08-21
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2020-08-18
dc.location	USA

Abstract:

Clinicians make decisions that affect life and death, quality of life, every single day. It is important to support clinicians by discovering medical knowledge from the accumulated electronic health records (EHRs). The integration of genomic information and EHRs are long recognized by the medical community as the inherent feature of the disease. The demand for developing a clinical recommender system that is able to deal with both genomic and phenotypic data is urgent. This paper proposes a framework of clinical recommender system with genomic information, which is used in the clinical process and connects the four types of users: clinicians, patients, clinical labs, researchers. With models and methods in artificial intelligence (AI), five functions are designed in this framework: diagnosis prediction, disease risk prediction, test prediction, and event prediction. The proposed framework will help clinicians to make decisions on the next step in clinical care action for patients.

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http://hdl.handle.net/10453/148297