AIMC Topic: Machine Learning

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Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images.

Computational and mathematical methods in medicine
The level of patient's illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, and history of patient and by experience. To treat the patient, proper diagnosis is very much importan...

AntiMF: A deep learning framework for predicting anticancer peptides based on multi-view feature extraction.

Methods (San Diego, Calif.)
In recent years, anticancer peptides have emerged as a new viable option in cancer therapy, with the ability to overcome the considerable side effects and poor outcomes of standard cancer therapies. Accurate anticancer peptide identification can faci...

Machine Learning Assisted Prediction of Power Conversion Efficiency of All-Small Molecule Organic Solar Cells: A Data Visualization and Statistical Analysis.

Molecules (Basel, Switzerland)
Organic solar cells are famous for their cheap solution processing. Their industrialization needs fast designing of efficient materials. For this purpose, testing of large number of materials is necessary. Machine learning is a better option due to c...

Prediction of daily mean and one-hour maximum PM concentrations and applications in Central Mexico using satellite-based machine-learning models.

Journal of exposure science & environmental epidemiology
BACKGROUND: Machine-learning algorithms are becoming popular techniques to predict ambient air PM concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to pred...

Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.

BMC anesthesiology
BACKGROUND: Postoperative major adverse cardiovascular events (MACEs) account for more than one-third of perioperative deaths. Geriatric patients are more vulnerable to postoperative MACEs than younger patients. Identifying high-risk patients in adva...

A SuperLearner Approach to Predict Run-In Selection in Clinical Trials.

Computational and mathematical methods in medicine
A critical early step in a clinical trial is defining the study sample that appropriately represents the target population from which the sample will be drawn. Envisaging a "run-in" process in study design may accomplish this task; however, the tradi...

Medical Data Classification Assisted by Machine Learning Strategy.

Computational and mathematical methods in medicine
With the development of science and technology, data plays an increasingly important role in our daily life. Therefore, much attention has been paid to the field of data mining. Data classification is the premise of data mining, and how well the data...

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

Psychonomic bulletin & review
The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for natura...

The development and deployment of machine learning models.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Applications of artificial intelligence, specifically machine learning, are becoming increasingly popular in Orthopaedic Surgery, and medicine as a whole. This growing interest is shared by data scientists and physicians alike. However, there is an a...