AIMC Topic: Machine Learning

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Preparing Medical Imaging Data for Machine Learning.

Radiology
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief...

A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia.

Journal of materials chemistry. B
There is currently no effective treatment for acute myeloid leukemia, and surgery is also ineffective as an important treatment for most tumors. Rapidly developing artificial intelligence technology can be applied to different aspects of drug develop...

Expert-augmented machine learning.

Proceedings of the National Academy of Sciences of the United States of America
Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performanc...

Monitoring stance towards vaccination in twitter messages.

BMC medical informatics and decision making
BACKGROUND: We developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative stance. Such a system makes it possible to monitor the ongoing stream of messages on social media, of...

Mental state space visualization for interactive modeling of personalized BCI control strategies.

Journal of neural engineering
OBJECTIVE: Numerous studies in the area of BCI are focused on the search for a better experimental paradigm-a set of mental actions that a user can evoke consistently and a machine can discriminate reliably. Examples of such mental activities are mot...

Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface.

Journal of neural engineering
OBJECTIVE: Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteri...

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques.

BioMed research international
The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the...

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients.

Journal of healthcare engineering
In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson's disease (PD) are more prone to a movement disord...

Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.

Psychological medicine
BACKGROUND: This study evaluated whether natural language processing (NLP) of psychotherapy note text provides additional accuracy over and above currently used suicide prediction models.