Assessing the acceptability, feasibility, and usefulness of a psychosocial screening tool to patients and clinicians in a clinical genetics service in Australia

2021 ◽  
Author(s):  
Katrina Monohan ◽  
Rebecca Purvis ◽  
Adrienne Sexton ◽  
Maira Kentwell ◽  
Monica Thet ◽  
...  
Keyword(s):  
Screening Tool ◽  
Clinical Genetics ◽  
Psychosocial Screening

Use of a psychosocial screening tool to assess the needs of HIV infected youth

1993 ◽  
Vol 14 (1) ◽  
pp. 53
Author(s):  
Lori Wolfe ◽  
Mari Radzik ◽  
Richard G. MacKenzie
Keyword(s):  
Screening Tool ◽  
Psychosocial Screening ◽  
Hiv Infected Youth

Development of a Prenatal Psychosocial Screening Tool for Post-Partum Depression and Anxiety

2012 ◽  
Vol 26 (4) ◽  
pp. 316-327 ◽  
Author(s):  
Sheila McDonald ◽  
Jennifer Wall ◽  
Kaitlin Forbes ◽  
Dawn Kingston ◽  
Heather Kehler ◽  
...  
Keyword(s):  
Screening Tool ◽  
Post Partum ◽  
Depression And Anxiety ◽  
Psychosocial Screening ◽  
Post Partum Depression

scholarly journals Deep Learning-Based Analysis of Face Images as a Screening Tool for Genetic Syndromes

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6595
Author(s):  
Maciej Geremek ◽  
Krzysztof Szklanny
Keyword(s):  
Deep Learning ◽  
Screening Tool ◽  
Diagnostic Yield ◽  
Genetic Disorders ◽  
Training Dataset ◽  
Clinical Genetics ◽  
Classification Problems ◽  
Genetic Syndromes ◽  
Dysmorphic Features ◽  
Accuracy Result

Approximately 4% of the world’s population suffers from rare diseases. A vast majority of these disorders have a genetic background. The number of genes that have been linked to human diseases is constantly growing, but there are still genetic syndromes that remain to be discovered. The diagnostic yield of genetic testing is continuously developing, and the need for testing is becoming more significant. Due to limited resources, including trained clinical geneticists, patients referred to clinical genetics units must be accurately selected. Around 30–40% of genetic disorders are associated with specific facial characteristics called dysmorphic features. As part of our research, we analyzed the performance of classifiers based on deep learning face recognition models in detecting dysmorphic features. We tested two classification problems: a multiclass problem (15 genetic disorders vs. controls) and a two-class problem (disease vs. controls). In the multiclass task, the best result reached an accuracy level of 84%. The best accuracy result in the two-class problem reached 96%. More importantly, the binary classifier detected disease features in patients with diseases that were not previously present in the training dataset. The classifier was able to generalize differences between patients and controls, and to detect abnormalities without information about the specific disorder. This indicates that a screening tool based on deep learning and facial recognition could not only detect known diseases, but also detect patients with diseases that were not previously known. In the future, this tool could help in screening patients before they are referred to the genetic unit.

Triumphs and Challenges of Implementing a Nursing Bedside Swallow Screening Tool: A Stroke Coordinator's Perspective

2009 ◽  
Vol 18 (4) ◽  
pp. 129-133 ◽  
Author(s):  
Kelly Poskus
Keyword(s):  
Screening Tool ◽  
Hospital Setting ◽  
Swallow Screening

Abstract The bedside swallow screen has become an essential part of the evaluation of a patient after stroke in the hospital setting. Implementing this type of tool should be simple. However, reinforcement and monitoring of the tool presents a challenge. Verifying the consistency and reliability of nurses performing the bedside swallow screen can be a difficult task. This article will document the journey of implementing and maintaining a reliable and valid nursing bedside swallow screen.

Integrating Psychosocial Screening And Referral Into Prenatal Care

2002 ◽  
Author(s):  
Ellen Hutchins ◽  
Carol Korenbrot ◽  
Jeanne Mahoney
Keyword(s):  
Prenatal Care ◽  
Psychosocial Screening
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