AIMC Topic: Machine Learning

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Determining gradient conditions for peptide purification in RPLC with machine-learning-based retention time predictions.

Journal of chromatography. A
A strategy for determining a suitable solvent gradient in silico in preparative peptide separations is presented. The strategy utilizes a machine-learning-based method, called ELUDE, for peptide retention time predictions based on the amino acid sequ...

Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.

Psychiatry research
This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases with schizophrenia (n = 60), schizoaffective disorder (n = 19), bipolar disorder (n = 20), unipolar dep...

Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination.

European journal of pediatrics
Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethosc...

Machine learning concepts, concerns and opportunities for a pediatric radiologist.

Pediatric radiology
Machine learning, a subfield of artificial intelligence, is a rapidly evolving technology that offers great potential for expanding the quality and value of pediatric radiology. We describe specific types of learning, including supervised, unsupervis...

Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.

BMC medical research methodology
BACKGROUND: Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative...

Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks.

Journal of neuroengineering and rehabilitation
BACKGROUND: To assist people with disabilities, exoskeletons must be provided with human-robot interfaces and smart algorithms capable to identify the user's movement intentions. Surface electromyographic (sEMG) signals could be suitable for this pur...

Information Theoretic Feature Transformation Learning for Brain Interfaces.

IEEE transactions on bio-medical engineering
OBJECTIVE: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential s...

Computed Tomography-Based Radiomic Features Could Potentially Predict Microsatellite Instability Status in Stage II Colorectal Cancer: A Preliminary Study.

Academic radiology
RATIONALE AND OBJECTIVES: To investigate whether quantitative radiomics features extracted from computed tomography (CT) can predict microsatellite instability (MSI) status in an Asian cohort of patients with stage Ⅱ colorectal cancer (CRC).

Comparison between random forest and gradient boosting machine methods for predicting Listeria spp. prevalence in the environment of pastured poultry farms.

Food research international (Ottawa, Ont.)
Foodborne pathogens such as Listeria spp. contain the ability to survive and multiply in poultry farming environments, which provides a route of contamination for poultry processing environments and final poultry products. An understanding of the eff...