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1
A Machine Learning Approach to Detect Suicidal Ideation in US Veterans Based on Acoustic and Linguistic Features of Speech ...
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2
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention
In: Faculty Publications (2019)
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3
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Krishnaprasad Thirunarayan (2019)
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4
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Amit P. Sheth (2019)
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5
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention
In: Amit P. Sheth (2019)
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6
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Kno.e.sis Publications (2018)
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7
Comparative Analysis of Online Health Queries Originating from Personal Computers and Smart Devices on a Consumer Health Information Portal
In: Ashutosh Jadhav (2015)
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8
An Analysis of Mayo Clinic Search Query Logs for Cardiovascular Diseases
In: Ashutosh Jadhav (2015)
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9
Online Information Searching for Cardiovascular Diseases: An Analysis of Mayo Clinic Search Query Logs
In: Ashutosh Jadhav (2015)
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10
Analysis of Online Information Searching for Cardiovascular Diseases on a Consumer Health Information Portal
Jadhav, Ashutosh; Sheth, Amit; Pathak, Jyotishman. - : American Medical Informatics Association, 2014
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11
Online Information Searching for Cardiovascular Diseases: An Analysis of Mayo Clinic Search Query Logs
In: Publications (2014)
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12
An Analysis of Mayo Clinic Search Query Logs for Cardiovascular Diseases
In: Publications (2014)
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13
Comparative Analysis of Online Health Queries Originating from Personal Computers and Smart Devices on a Consumer Health Information Portal
In: Publications (2014)
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14
Online Information Searching for Cardiovascular Diseases: An Analysis of Mayo Clinic Search Query Logs
In: Kno.e.sis Publications (2014)
Abstract: Since the early 2000’s, Internet usage for health information searching has increased significantly. Studying search queries can help us to understand users “information need” and how do they formulate search queries (“expression of information need”). Although cardiovascular diseases (CVD) affect a large percentage of the population, few studies have investigated how and what users search for CVD. We address this knowledge gap in the community by analyzing a large corpus of 10 million CVD related search queries from MayoClinic.com. Using UMLS MetaMap and UMLS semantic types/concepts, we developed a rule-based approach to categorize the queries into 14 health categories. We analyzed structural properties, types (keyword-based/Wh-questions/Yes-No questions) and linguistic structure of the queries. Our results show that the most searched health categories are ‘Diseases/Conditions’, ‘Vital- Sings’, ‘Symptoms’ and ‘Living-with’. CVD queries are longer and are predominantly keyword-based. This study extends our knowledge about online health information searching and provides useful insights for Web search engines and health websites.
Keyword: Bioinformatics; Communication; Communication Technology and New Media; Computer Sciences; Databases and Information Systems; Life Sciences; OS and Networks; Physical Sciences and Mathematics; Science and Technology Studies; Social and Behavioral Sciences
URL: https://corescholar.libraries.wright.edu/knoesis/539
https://corescholar.libraries.wright.edu/cgi/viewcontent.cgi?article=1915&context=knoesis
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15
Comparative Analysis of Online Health Queries Originating from Personal Computers and Smart Devices on a Consumer Health Information Portal
In: Amit P. Sheth (2014)
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16
Comparative Analysis of Online Health Queries Originating from Personal Computers and Smart Devices on a Consumer Health Information Portal
In: Kno.e.sis Publications (2014)
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17
An Analysis of Mayo Clinic Search Query Logs for Cardiovascular Diseases
In: Kno.e.sis Publications (2014)
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18
An Analysis of Mayo Clinic Search Query Logs for Cardiovascular Diseases
In: Amit P. Sheth (2014)
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19
Online Information Searching for Cardiovascular Diseases: An Analysis of Mayo Clinic Search Query Logs
In: Amit P. Sheth (2014)
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20
Terminology Representation Guidelines for Biomedical Ontologies in the Semantic Web Notations
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