scholarly journals Entrapped: Making Sense of High-Risk Migration through Gambling

2019 ◽  
pp. 125-136
Keyword(s):  
High Risk ◽  
Making Sense

scholarly journals Making sense of being at ‘high risk’ of coronary heart disease within primary prevention

2010 ◽  
Vol 25 (3) ◽  
pp. 289-304 ◽  
Author(s):  
Hannah Farrimond ◽  
Paula M. Saukko ◽  
Nadeem Qureshi ◽  
Philip H. Evans
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Primary Prevention ◽  
High Risk ◽  
Making Sense

scholarly journals Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 722
Author(s):  
Mahaly Baptiste ◽  
Sarah Shireen Moinuddeen ◽  
Courtney Lace Soliz ◽  
Hashimul Ehsan ◽  
Gen Kaneko
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Genetic Information ◽  
Large Scale ◽  
Sequence Data ◽  
Precision Oncology ◽  
Nucleotide Polymorphisms ◽  
Making Sense ◽  
High Risk Populations ◽  
Systematic Identification

Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person’s variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.

A Community Based High Risk Register for Hearing Loss

1982 ◽  
Vol 47 (4) ◽  
pp. 373-375 ◽  
Author(s):  
James L. Fitch ◽  
Thomas F. Williams ◽  
Josephine E. Etienne
Keyword(s):  
Hearing Loss ◽  
System Identification ◽  
High Risk ◽  
Language Learning ◽  
Education Program ◽  
Communication System ◽  
Auditory Signal ◽  
Early Age ◽  
Community Based ◽  
Children With Hearing Loss

The critical need to identify children with hearing loss and provide treatment at the earliest possible age has become increasingly apparent in recent years (Northern & Downs, 1978). Reduction of the auditory signal during the critical language-learning period can severely limit the child's potential for developing a complete, effective communication system. Identification and treatment of children having handicapping conditions at an early age has gained impetus through the Handicapped Children's Early Education Program (HCEEP) projects funded by the Bureau of Education for the Handicapped (BEH).

A Community Based High Risk Register for Hearing Loss

1983 ◽  
Vol 48 (1) ◽  
pp. 110-110
Keyword(s):  
Hearing Loss ◽  
High Risk ◽  
Community Based

For the November 1982 JSHD article, "A Community Based High Risk Register for Hearing Loss," the author would like to acknowledge three additional individuals who made valuable contributions to the study. They are Marie Carrier, Gene Lyon, and Bobbie Robertson.

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