AIMC Topic: Machine Learning

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A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.

Statistical methods in medical research
BACKGROUND: Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimat...

[Enhancement of radiomics-based machine learning models for predicting efficacy of high-intensity focused ultrasound ablation of uterine fibroids using undersampling methods].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVES: To improve the accuracy of machine learning models for preoperative prediction of high-intensity focused ultrasound (HIFU) ablation efficacy for uterine fibroids by correcting class imbalance in small sample datasets using undersampling m...

Predicting Helicoverpa zea (Lepidoptera: Noctuidae) populations in the southern United States.

Environmental entomology
Forecasting insect pest populations before crops are planted can help improve management and reduce pesticide use. Pests with long dispersal potentials and wide host ranges are difficult to predict but often cause losses in crops across broad spatial...

Machine Learning-Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2- Early Breast Cancer Using Real-World and NATALEE Data.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Despite current standard-of-care endocrine therapy, distant recurrence remains a concern for patients with hormone receptor-positive (HR+)/HER2- early breast cancer (EBC). Understanding individual recurrence risk would aid in clinical decisi...

The Retinal Age Gap as a Marker of Accelerated Aging in the Early Course of Schizophrenia.

Schizophrenia bulletin
BACKGROUND AND HYPOTHESIS: Given the available findings confirming accelerated brain aging in schizophrenia (SZ), we conducted a study aimed at verifying whether quantitative retinal morphological data enable age prediction and whether schizophrenia ...

Exploring Primary and Interaction Effects of Minor Physical Anomalies: Development and Validation of Prediction Models Using Explainable Machine Learning Algorithms for Early-Onset Schizophrenia.

Schizophrenia bulletin
BACKGROUND AND HYPOTHESIS: Minor physical abnormalities (MPAs) are neurodevelopmental markers that can be traced to prenatal events and may be significant features of early-onset schizophrenia (EOS). Therefore, our study aimed to (1) find the primary...

[Application and research progress of artificial intelligence in the diagnosis and treatment of rare lung diseases].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases
Rare lung diseases are a group of diseases characterized by significant clinical heterogeneity, challenging diagnosis and treatment processes, and diverse underlying causes. Due to their uncommon symptoms and limited awareness among healthcare provid...

Automated calibration of stomatal conductance models from thermal imagery by leveraging synthetic images generated from Helios 3D biophysical model simulations.

Journal of experimental botany
Stomatal conductance (gs) is indicative of plant carbon dioxide uptake via photosynthesis and water loss via transpiration, making it a crucial plant biophysical trait. Direct measurement of gs is labor-intensive and usually not scalable to large fie...