AIMC Topic: Machine Learning

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EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.

BMC medical informatics and decision making
BACKGROUND: There has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Records (EHRs). Vector representations of medical concepts facilitate exploratory analysis and predictive ...

Chronic Kidney Disease stratification using office visit records: Handling data imbalance via hierarchical meta-classification.

BMC medical informatics and decision making
BACKGROUND: Chronic Kidney Disease (CKD) is one of several conditions that affect a growing percentage of the US population; the disease is accompanied by multiple co-morbidities, and is hard to diagnose in-and-of itself. In its advanced forms it car...

Wrist accelerometer shape feature derivation methods for assessing activities of daily living.

BMC medical informatics and decision making
BACKGROUND: There has been an increasing interest in understanding the usefulness of wrist-based accelerometer data for physical activity (PA) assessment due to the ease of use and higher user compliance than other body placements. PA assessment stud...

Leveraging auxiliary measures: a deep multi-task neural network for predictive modeling in clinical research.

BMC medical informatics and decision making
BACKGROUND: Accurate predictive modeling in clinical research enables effective early intervention that patients are most likely to benefit from. However, due to the complex biological nature of disease progression, capturing the highly non-linear in...

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure.

Genomics
The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning method...

Deep embeddings for novelty detection in myopathy.

Computers in biology and medicine
We address the challenge of finding anomalies in ultrasound images via deep learning, specifically applying this to screening for myopathies and finding rare presentations of myopathic disease. Among myopathic diseases, this study focuses on the use ...

Learning-based CBCT correction using alternating random forest based on auto-context model.

Medical physics
PURPOSE: Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and p...

SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.

PLoS computational biology
LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational ...

Distributed Repetitive Learning Control for Cooperative Cadence Tracking in Functional Electrical Stimulation Cycling.

IEEE transactions on cybernetics
Closed-loop control of functional electrical stimulation coupled with motorized assistance to induce cycling is a rehabilitative strategy that can improve the mobility of people with neurological conditions (NCs). However, robust control methods, whi...