AIMC Topic: Young Adult
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Diagnosing brain tumours by routine blood tests using machine learning.
Scientific reports
Oct 9, 2019
Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients we built a machine learning predictive model for t...
Development of a Wearable Electrical Impedance Tomographic Sensor for Gesture Recognition With Machine Learning.
IEEE journal of biomedical and health informatics
Oct 4, 2019
A wearable electrical impedance tomographic (wEIT) sensor with 8 electrodes is developed to realize gesture recognition with machine learning algorithms. To optimize the wEIT sensor, gesture recognition rates are compared by using a series of electro...
Predicting likelihood of psychological disorders in PlayerUnknown's Battlegrounds (PUBG) players from Asian countries using supervised machine learning.
Addictive behaviors
Oct 4, 2019
Multiplayer Online Battle Arena (MOBA) has become one of the most popular genre of online video games played by gamers worldwide. Previous studies have exhibited that excessive engagement in games can lead to Internet Gaming Disorder (IGD). Internet ...
Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study.
The Lancet. Oncology
Oct 4, 2019
BACKGROUND: Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but ...
The Effect of Robot Attentional Behaviors on User Perceptions and Behaviors in a Simulated Health Care Interaction: Randomized Controlled Trial.
Journal of medical Internet research
Oct 4, 2019
BACKGROUND: For robots to be effectively used in health applications, they need to display appropriate social behaviors. A fundamental requirement in all social interactions is the ability to engage, maintain, and demonstrate attention. Attentional b...
[Prediction of chemotherapy response in primary osteosarcoma using the machine learning technique on radiomic data].
Bulletin du cancer
Oct 3, 2019
INTRODUCTION: Osteosarcoma is the most common malignant bone tumor before 25 years of age. Response to neoadjuvant chemotherapy determines continuation of treatment and is also a powerful prognostic factor. There are currently no reliable ways to eva...
Bone Neoplasms
Predictive Value of Tests
France
Analysis of Variance
Chemotherapy, Adjuvant
Sensitivity and Specificity
Male
Infant
Adolescent
Child, Preschool
Child
Machine Learning
Humans
Antineoplastic Combined Chemotherapy Protocols
Retrospective Studies
Treatment Outcome
Female
Neoadjuvant Therapy
Infant, Newborn
Magnetic Resonance Imaging
Young Adult
Osteosarcoma
Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.
Drug and alcohol dependence
Oct 1, 2019
BACKGROUND: This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics.
A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1.
Blood cancer journal
Oct 1, 2019
Deep learning-enabled system for rapid pneumothorax screening on chest CT.
European journal of radiology
Sep 26, 2019
PURPOSE: Prompt diagnosis and quantitation of pneumothorax impact decisions pertaining to patient management. The purpose of our study was to develop and evaluate the accuracy of a deep learning (DL)-based image classification program for detection o...
Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists.
AJR. American journal of roentgenology
Sep 25, 2019
The objective of our study was to compare the performance of radiologicradiomic machine learning (ML) models and expert-level radiologists for differentiation of benign and malignant solid renal masses using contrast-enhanced CT examinations. This ...
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