Results 51 to 60 of about 4,279,934 (276)

Impact of Radiation Therapy on Physical and Psychosocial Health of Adolescents and Young Adults: A Joint Report From the Children's Oncology Group AYA and Radiation Oncology Committees

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Rates of cancer among adolescents and young adults (AYA), age 15–39 years, are increasing. Consequently, radiation oncologists are treating more AYAs who have diagnoses spanning both pediatric and adult practices. Compared to pediatric and older adult patients, AYAs face a unique set of challenges.
Hesham Elhalawani, Rachel K. Peterson, Avani D. Rao, Laurie J. McKenzie, Ahmed Magdy Elmotayam, Leslie Chang, Michael Roth, Susan L. McGovern   +7 more
wiley   +1 more source

Predictive Attractor Models

Advances in Neural Information Processing Systems 37
Accepted to NeurIPS ...
Ramy Mounir, Sudeep Sarkar
openaire   +4 more sources

Predictive Statistical Models for User Modeling [PDF]

User Modeling and User-Adapted Interaction, 2001
Summary: The limitations of traditional knowledge representation methods for modeling complex human behaviour led to the investigation of statistical models. Predictive statistical models enable the anticipation of certain aspects of human behaviour, such as goals, actions and preferences.
Ingrid Zukerman, David W. Albrecht
openaire   +2 more sources

Social Functioning Within the First Years After Pediatric Brain Tumor Diagnosis and the Relationship With Family Psychosocial Risk

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Survivors of pediatric brain tumors (PBTs) can experience long‐term social difficulties, impacting quality of life. Beyond medical and environmental factors, family psychosocial risk may play a role in social outcomes by shaping the caregiving environment and may provide intervention options.
Renske H. Houben, Marisa H. Huisman, Martha A. Grootenhuis, Femke K. Aarsen, Marita Partanen   +4 more
wiley   +1 more source

Balancing Complex Signals for Robust Predictive Modeling

Sensors, 2021
Robust predictive modeling is the process of creating, validating, and testing models to obtain better prediction outcomes. Datasets usually contain outliers whose trend deviates from the most data points.
Fazal Aman, Azhar Rauf, Rahman Ali, Jamil Hussain, Ibrar Ahmed   +4 more
doaj   +1 more source

Retrospective Analysis of Donor Lymphocyte Infusions in Pediatric Patients With Mixed Chimerism After Hematopoietic Stem Cell Transplantation

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Allogeneic hematopoietic stem cell transplantation (alloHSCT) is an essential therapy for several malignant and nonmalignant diseases, but relapse and graft loss remain the principal threats to its success. Routine monitoring of chimerism and minimal residual disease (MRD) enables early detection of imminent recurrence and guides ...
Carmen Junk, Sebastian Michaelis, Michaela Döring, Thomas Eichholz, Ursula Holzer, Martin Ebinger, Tim Flaadt, Rupert Handgretinger, Peter Lang, Jan Frederic Weller, Markus Mezger   +10 more
wiley   +1 more source

Predictive modeling of emergency cesarean delivery.

PLoS ONE, 2018
ObjectiveTo increase discriminatory accuracy (DA) for emergency cesarean sections (ECSs).Study designWe prospectively collected data on and studied all 6,157 births occurring in 2014 at four public hospitals located in three different autonomous ...
Carlos Campillo-Artero, Miquel Serra-Burriel, Andrés Calvo-Pérez   +2 more
doaj   +1 more source

Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models. [PDF]

PLoS ONE, 2016
Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to
Nikola Simidjievski, Ljupčo Todorovski, Sašo Džeroski   +2 more
doaj   +1 more source

Classification of first-episode schizophrenia patients, individuals at ultra-high risk for psychosis, and healthy controls using structural mri, eeg, and machine learning

European Psychiatry, 2021
Introduction Machine learning has increasingly been applied to classification of psychosis spectrum in neuroimaging research. However, a number of multimodal studies using MRI and electroencephalography (EEG) is quite limited.
A. Tomyshev, N. Lutsyak, M. Belyaev, V. Kaleda, I. Lebedeva   +4 more
doaj   +1 more source

Predicted models and CCP 4

Acta Crystallographica Section D Structural Biology, 2023
In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem.
Adam J. Simpkin, Iracema Caballero, Stuart McNicholas, Kyle Stevenson, Elisabet Jiménez, Filomeno Sánchez Rodríguez, Maria Fando, Ville Uski, Charles Ballard, Grzegorz Chojnowski, Andrey Lebedev, Eugene Krissinel, Isabel Usón, Daniel J. Rigden, Ronan M. Keegan   +14 more
openaire   +3 more sources