AIMC Topic: Machine Learning

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Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables.

BMC medical research methodology
To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders an...

Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model.

Scientific reports
One of the important concerns in the field of exercise immunology is determining the appropriate intensity and duration of exercise to prevent suppression of the immune system. Adopting a reliable approach to predict the number of white blood cells (...

Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning.

Journal of biophotonics
While cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensitive. This study examined the possibility of using serum fluorescence spectroscopy an...

Unlock the algorithms: regulation of adaptive algorithms in reproduction.

Fertility and sterility
In the USA, the Food and Drug Administration plans to regulate artificial intelligence and machine learning software systems as medical devices to improve the quality, consistency, and transparency of their performance across specific age, racial, an...

Radiomics and artificial intelligence for precision medicine in lung cancer treatment.

Seminars in cancer biology
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic ...

Continual learning with invertible generative models.

Neural networks : the official journal of the International Neural Network Society
Catastrophic forgetting (CF) happens whenever a neural network overwrites past knowledge while being trained on new tasks. Common techniques to handle CF include regularization of the weights (using, e.g., their importance on past tasks), and rehears...

Applications of artificial intelligence in ovarian stimulation: a tool for improving efficiency and outcomes.

Fertility and sterility
Because of the birth of the first baby after in vitro fertilization (IVF), the field of assisted reproductive technologies (ARTs) has seen significant advancements in the past 40 years. Over the last decade, the healthcare industry has increasingly a...

Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly.

Journal of chemical information and modeling
While polymerization-induced self-assembly (PISA) has become a preferred synthetic route toward amphiphilic block copolymer self-assemblies, predicting their phase behavior from experimental design is extremely challenging, requiring time and work-in...

Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement.

International journal of environmental research and public health
To solve the research-practice gap and take one step forward toward using big data with real-world evidence, the present study aims to adopt a novel method using machine learning to pool findings from meta-analyses and predict the change of countermo...

Machine Learning of Physiologic Waveforms and Electronic Health Record Data: A Large Perioperative Data Set of High-Fidelity Physiologic Waveforms.

Critical care clinics
Perioperative morbidity and mortality are significantly associated with both static and dynamic perioperative factors. The studies investigating static perioperative factors have been reported; however, there are a limited number of previous studies ...