Dynamic brain connectome and high risk of mental problem in clinical nurses

Objective: to construct an integrated nursing risk management assessment system, standardize nursing risk assessment and management process, and improve the implementation rate of nursing risk assessment and nursing safety quality. Methods: a special team was set up to construct an integrated nursing risk management and assessment system, including management personnel, clinical nurses and information engineers, to analyze the problems existing in the old nursing risk assessment and design an integrated nursing risk management and assessment system. Results: the integrated nursing risk management assessment system was applied in all wards of the hospital from July 2019 to September 2019, and 25,778 cases were evaluated. It has the advantages of intelligence, integration, convenient operation, historical score query, guiding standard management of high-risk patients. Conclusion: the intelligence, integration and standardization of the integrated nursing risk management assessment system can improve nursing efficiency, standardize nursing risk management, improve nursing staff satisfaction, and reduce the incidence of nursing adverse events in high-risk patients.

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A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants

Scientific Reports ◽

10.1038/s41598-020-71914-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Lili He ◽

Hailong Li ◽

Jinghua Wang ◽

Ming Chen ◽

Elveda Gozdas ◽

...

Keyword(s):

Deep Learning ◽

High Risk ◽

Transfer Learning ◽

Preterm Infants ◽

Learning Model ◽

Very Preterm Infants ◽

Very Preterm ◽

Brain Connectome ◽

Multi Stage ◽

Neurodevelopmental Deficits

Abstract Survivors following very premature birth (i.e., ≤ 32 weeks gestational age) remain at high risk for neurodevelopmental impairments. Recent advances in deep learning techniques have made it possible to aid the early diagnosis and prognosis of neurodevelopmental deficits. Deep learning models typically require training on large datasets, and unfortunately, large neuroimaging datasets with clinical outcome annotations are typically limited, especially in neonates. Transfer learning represents an important step to solve the fundamental problem of insufficient training data in deep learning. In this work, we developed a multi-task, multi-stage deep transfer learning framework using the fusion of brain connectome and clinical data for early joint prediction of multiple abnormal neurodevelopmental (cognitive, language and motor) outcomes at 2 years corrected age in very preterm infants. The proposed framework maximizes the value of both available annotated and non-annotated data in model training by performing both supervised and unsupervised learning. We first pre-trained a deep neural network prototype in a supervised fashion using 884 older children and adult subjects, and then re-trained this prototype using 291 neonatal subjects without supervision. Finally, we fine-tuned and validated the pre-trained model using 33 preterm infants. Our proposed model identified very preterm infants at high-risk for cognitive, language, and motor deficits at 2 years corrected age with an area under the receiver operating characteristic curve of 0.86, 0.66 and 0.84, respectively. Employing such a deep learning model, once externally validated, may facilitate risk stratification at term-equivalent age for early identification of long-term neurodevelopmental deficits and targeted early interventions to improve clinical outcomes in very preterm infants.

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Comment on “Use of a High-Risk Register in Newborn Screening”

Journal of Speech and Hearing Disorders ◽

10.1044/jshd.4104.555 ◽

1976 ◽

Vol 41 (4) ◽

pp. 555-555 ◽

Cited By ~ 1

Author(s):

Daniel Ling

Keyword(s):

High Risk ◽

Newborn Screening

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A Community Based High Risk Register for Hearing Loss

Journal of Speech and Hearing Disorders ◽

10.1044/jshd.4704.373 ◽

1982 ◽

Vol 47 (4) ◽

pp. 373-375 ◽

Cited By ~ 2

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).

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A Community Based High Risk Register for Hearing Loss

Journal of Speech and Hearing Disorders ◽

10.1044/jshd.4801.110 ◽

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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