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Effects of Robot-Assisted Training for the Unaffected Arm in Patients with Hemiparetic Cerebral Palsy: A Proof-of-Concept Pilot Study.
Behavioural neurology
Jul 6, 2017
On a voluntary basis, 10 adolescents with hemiparesis due to cerebral palsy and 11 neurologically healthy control subjects participated in this proof-of-concept pilot study. The aim was to examine the effects of robot-assisted training for the unaffe...
Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth.
PloS one
Jul 6, 2017
Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy r...
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A Control Scheme to Minimize Muscle Energy for Power Assistant Robotic Systems Under Unknown External Perturbation.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jul 5, 2017
This paper proposes a novel control method to minimize muscle energy for power-assistant robotic systems that support the intended motions of a user under unknown external perturbations, using surface electromyogram (sEMG) signals. Conventional contr...
PAK6-Associated Support Vector Machine Classifier: A New Way to Evaluate Response and Survival of Gastric Cancer Treated by 5-FU/Oxaliplatin Chemotherapy.
EBioMedicine
Jul 5, 2017
Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data.
BMC medical informatics and decision making
Jul 5, 2017
BACKGROUND: As one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially ...
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Entity recognition from clinical texts via recurrent neural network.
BMC medical informatics and decision making
Jul 5, 2017
BACKGROUND: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health inform...
Detecting clinically relevant new information in clinical notes across specialties and settings.
BMC medical informatics and decision making
Jul 5, 2017
BACKGROUND: Automated methods for identifying clinically relevant new versus redundant information in electronic health record (EHR) clinical notes is useful for clinicians and researchers involved in patient care and clinical research, respectively....
Lightweight predicate extraction for patient-level cancer information and ontology development.
BMC medical informatics and decision making
Jul 5, 2017
BACKGROUND: Knowledge engineering for ontological knowledgebases is resource and time intensive. To alleviate these issues, especially for novices, automated tools from the natural language domain can assist in the development process of ontologies. ...
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An active learning-enabled annotation system for clinical named entity recognition.
BMC medical informatics and decision making
Jul 5, 2017
BACKGROUND: Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a ...
Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data.
BMC medical informatics and decision making
Jul 5, 2017
BACKGROUND: Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learn...
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