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Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning Techniques.
In Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE :
METHODS :
RESULTS :
CONCLUSION :
Nguyen Van Sau, Lobo Marques J A, Biala T A, Li Ye
2020-Nov-20
ABPM (Ambulatory Blood Pressure Monitoring), HRV (Heart Rate Variability), IUGR (Intrauterine Growth Restriction), Machine Learning
Natural language processing and entrustable professional activity text feedback in surgery: A machine learning model of resident autonomy.
In American journal of surgery
BACKGROUND :
METHODS :
RESULTS :
CONCLUSIONS :
Stahl Christopher C, Jung Sarah A, Rosser Alexandra A, Kraut Aaron S, Schnapp Benjamin H, Westergaard Mary, Hamedani Azita G, Minter Rebecca M, Greenberg Jacob A
2020-Nov-26
Assessment, Entrustable professional activities, Feedback, Natural language processing, Surgery education
Trends and influencing factors of plasma folate levels in Chinese women at mid-pregnancy, late pregnancy, and lactation periods.
In The British journal of nutrition
Zhou Yu-Bo, Si Ke-Yi, Li Hong-Tian, Li Xiu-Cui, Meng Ying, Liu Jian-Meng
2020-Dec-01
China, influencing factor, lactation, late pregnancy, mid-pregnancy, plasma folate
A machine learning-based clinical tool for diagnosing myopathy using multi-cohort microarray expression profiles.
In Journal of translational medicine
BACKGROUND :
MATERIALS AND METHODS :
RESULTS :
CONCLUSION :
Tran Andrew, Walsh Chris J, Batt Jane, Dos Santos Claudia C, Hu Pingzhao
2020-Nov-30
Biomarker, Clinical tool, Machine learning, Microarray, Muscle diseases

Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases.
In Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND :
METHODS :
RESULTS :
CONCLUSIONS :
Karimi-Bidhendi Saeed, Arafati Arghavan, Cheng Andrew L, Wu Yilei, Kheradvar Arash, Jafarkhani Hamid
2020-Nov-30
CMR image analysis, Complex CHD analysis, Deep learning, Fully convolutional networks, Generative adversarial networks, Machine learning

Development and validation of a 25-Gene Panel urine test for prostate cancer diagnosis and potential treatment follow-up.
In BMC medicine ; h5-index 89.0
BACKGROUND :
METHODS :
RESULTS :
CONCLUSIONS :
Johnson Heather, Guo Jinan, Zhang Xuhui, Zhang Heqiu, Simoulis Athanasios, Wu Alan H B, Xia Taolin, Li Fei, Tan Wanlong, Johnson Allan, Dizeyi Nishtman, Abrahamsson Per-Anders, Kenner Lukas, Feng Xiaoyan, Zou Chang, Xiao Kefeng, Persson Jenny L, Chen Lingwu
2020-Dec-01
Clinically significant prostate cancer, Gene Panel, Prostate cancer, Prostate cancer diagnosis, Prostate cancer treatment follow-up, Urine test
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