Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling.
In International journal of medical informatics ; h5-index 49.0
BACKGROUND :
RESULTS :
CONCLUSIONS :
Thieu Thanh, Maldonado Jonathan Camacho, Ho Pei-Shu, Ding Min, Marr Alex, Brandt Diane, Newman-Griffis Denis, Zirikly Ayah, Chan Leighton, Rasch Elizabeth
2020-Dec-24
Clinical notes, Functioning information, Mobility, Named entity recognition, Natural language processing, Text mining

Melancholia defined with the precision of a machine.
In Journal of affective disorders ; h5-index 79.0
BACKGROUND :
METHODS :
RESULTS :
LIMITATIONS :
CONCLUSIONS :
Parker Gordon, Spoelma Michael J
2020-Dec-29
Categorical versus spectrum models, Depressive disorders, Diagnosis, Machine learning, Melancholia, Psychiatric classification

Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17-18 years.
In Journal of affective disorders ; h5-index 79.0
BACKGROUND :
METHODS :
RESULTS :
CONCLUSIONS :
Jacobson Nicholas C, Lekkas Damien, Huang Raphael, Thomas Natalie
2020-Dec-27
anxiety disorders, artficial intelligence, deep learning, digital phenotyping, passive sensing, wearable movement

Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network.
In Neural networks : the official journal of the International Neural Network Society
Zhang Kaishuo, Robinson Neethu, Lee Seong-Whan, Guan Cuntai
2020-Dec-23
Brain–computer interface (BCI), Convolutional Neural Network (CNN), Electroencephalography (EEG), Transfer learning
Immunoinformatics designed T cell multi epitope dengue peptide vaccine derived from non structural proteome.
In Microbial pathogenesis
Krishnan G Sunil, Joshi Amit, Akhtar Nahid, Kaushik Vikas
2021-Jan-02
Dengue, Docking, Epitope, Population coverage, Simulation, Vaccine
External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction.
In International journal of cardiology ; h5-index 68.0
OBJECTIVE :
BACKGROUND :
METHODS :
RESULTS :
CONCLUSIONS :
Attia Itzhak Zachi, Tseng Andrew S, Benavente Ernest Diez, Inojosa Jose Medina, Clark Taane G, Malyutina Sofia, Kapa Suraj, Schirmer Henrik, Kudryavtsev Alexander V, Noseworthy Peter A, Carter Rickey E, Ryabikov Audrey, Perel Pablo, Friedman Paul A, Leon David A, Lopez-Jimenez Francisco
2021-Jan-02
Artificial intelligence, Electrocardiogram, Left ventricular systolic dysfunction, Machine learning
Weekly Summary
Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.