AIMC Topic: Machine Learning

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The use of digital health in heart rhythm care.

Expert review of cardiovascular therapy
INTRODUCTION: Digital health is a broad term that includes telecommunication technologies to collect, share and manipulate health information to improve patient health and health care services. With the growing use of wearables, artificial intelligen...

Computational prediction of the metabolites of agrochemicals formed in rats.

The Science of the total environment
Today, computational tools for the prediction of the metabolite structures of xenobiotics are widely available and employed in small-molecule research. Reflecting the availability of measured data, these in silico tools are trained and validated prim...

Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects.

Seminars in cancer biology
Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technol...

A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets.

Computers in biology and medicine
The missing data mechanism is a relevant problem in Machine Learning (ML) and biomedical informatics communities. Real-world Electronic Health Record (EHR) datasets comprise several missing values, thus revealing a high level of spatiotemporal sparsi...

Predicting Surgical Experience After Robotic Nerve-sparing Radical Prostatectomy Simulation Using a Machine Learning-based Multimodal Analysis of Objective Performance Metrics.

Urology practice
INTRODUCTION: Machine learning methods have emerged as objective tools to evaluate operative performance in urological procedures. Our objectives were to establish machine learning-based methods for predicting surgeon caseload for nerve-sparing robot...

Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review.

Journal of biomedical informatics
BACKGROUND: As the application of Artificial Intelligence (AI) technologies increases in the healthcare sector, the industry faces a need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML), which o...

Supervised Text Classification System Detects Fontan Patients in Electronic Records With Higher Accuracy Than Codes.

Journal of the American Heart Association
Background The Fontan operation is associated with significant morbidity and premature mortality. Fontan cases cannot always be identified by () codes, making it challenging to create large Fontan patient cohorts. We sought to develop natural langua...

A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases.

Journal of biomedical informatics
BACKGROUND: The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to im...

A large expert-curated cryo-EM image dataset for machine learning protein particle picking.

Scientific data
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. Howeve...

Digital Education for the Deployment of Artificial Intelligence in Health Care.

Journal of medical Internet research
Artificial Intelligence (AI) represents a significant milestone in health care's digital transformation. However, traditional health care education and training often lack digital competencies. To promote safe and effective AI implementation, health ...