AIMC Topic: Machine Learning

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Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning.

Pediatric research
BACKGROUND: Early identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. This study aims to disentangle the relative timing and importance of early markers of asthma.

PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information.

Interdisciplinary sciences, computational life sciences
The protein S-nitrosylation (SNO) is a significant post-translational modification that affects the stability, activity, cellular localization, and function of proteins. Therefore, highly accurate prediction of SNO sites aids in grasping biological f...

AI for targeted polypharmacology: The next frontier in drug discovery.

Current opinion in structural biology
In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise ...

Illusory generalizability of clinical prediction models.

Science (New York, N.Y.)
It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model's success in one or two d...

Practical challenges for precision medicine.

Science (New York, N.Y.)
The prediction of individual treatment responses with machine learning faces hurdles.

Unsupervised SoftOtsuNet Augmentation for Clinical Dermatology Image Classifiers.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Data Augmentation is a crucial tool in the Machine Learning (ML) toolbox because it can extract novel, useful training images from an existing dataset, thereby improving accuracy and reducing overfitting in a Deep Neural Network (DNNs). However, clin...

Evaluating Deep Learning Performance for P300 Neural Signal Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
P300 event-related potential (ERP) signals are useful neurological biomarkers, and their accurate classification is important when studying the cognitive functions in patients with neurological disorders. While many studies have proposed models for c...

Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discrimination.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Translating prediction models into practice and supporting clinicians' decision-making demand demonstration of clinical value. Existing approaches to evaluating machine learning models emphasize discriminatory power, which is only a part of the medic...

A QUEST for Model Assessment: Identifying Difficult Subgroups via Epistemic Uncertainty Quantification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Uncertainty quantification in machine learning can provide powerful insight into a model's capabilities and enhance human trust in opaque models. Well-calibrated uncertainty quantification reveals a connection between high uncertainty and an increase...