AIMC Topic: Machine Learning

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Natural language processing of head CT reports to identify intracranial mass effect: CTIME algorithm.

The American journal of emergency medicine
BACKGROUND: The Mortality Probability Model (MPM) is used in research and quality improvement to adjust for severity of illness and can also inform triage decisions. However, a limitation for its automated use or application is that it includes the v...

Characteristics of publicly available skin cancer image datasets: a systematic review.

The Lancet. Digital health
Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and their respective content is currently unclear. This systematic review aimed to id...

Proteome-Informed Machine Learning Studies of Cocaine Addiction.

The journal of physical chemistry letters
No anti-cocaine addiction drugs have been approved by the Food and Drug Administration despite decades of effort. The main challenge is the intricate molecular mechanisms of cocaine addiction, involving synergistic interactions among proteins upstrea...

Artificial Intelligence for Autonomous Molecular Design: A Perspective.

Molecules (Basel, Switzerland)
Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reaso...

Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features.

Cells
The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we prese...

Co-evolution based machine-learning for predicting functional interactions between human genes.

Nature communications
Over the next decade, more than a million eukaryotic species are expected to be fully sequenced. This has the potential to improve our understanding of genotype and phenotype crosstalk, gene function and interactions, and answer evolutionary question...

A robust and stable gene selection algorithm based on graph theory and machine learning.

Human genomics
BACKGROUND: Nowadays we are observing an explosion of gene expression data with phenotypes. It enables us to accurately identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data i...

DRG grouping by machine learning: from expert-oriented to data-based method.

BMC medical informatics and decision making
BACKGROUND: Diagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented ...

The ensemble learning model is not better than the Asian modified CKD-EPI equation for glomerular filtration rate estimation in Chinese CKD patients in the external validation study.

BMC nephrology
OBJECTIVE: To assess the clinical practicability of the ensemble learning model established by Liu et al. in estimating glomerular filtration rate (GFR) and validate whether it is a better model than the Asian modified Chronic Kidney Disease Epidemio...

Exploring the Underlying Mechanism of Shenyankangfu Tablet in the Treatment of Glomerulonephritis Through Network Pharmacology, Machine Learning, Molecular Docking, and Experimental Validation.

Drug design, development and therapy
PURPOSE: This study aimed to explore the underlying mechanisms of Shenyankangfu tablet (SYKFT) in the treatment of glomerulonephritis (GN) based on network pharmacology, machine learning, molecular docking, and experimental validation.