AIMC Topic: Disease Susceptibility

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Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission.

Frontiers in immunology
A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression,...

Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria.

International journal of molecular sciences
ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. It includes genetic, biochemical, histopathologica...

Machine Learning to Quantify Humoral Selection in Human Lupus Tubulointerstitial Inflammation.

Frontiers in immunology
In human lupus nephritis, tubulointerstitial inflammation (TII) is associated with expansion of B cells expressing anti-vimentin antibodies (AVAs). The mechanism by which AVAs are selected is unclear. Herein, we demonstrate that AVA somatic hypermut...

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values.

EBioMedicine
BACKGROUND: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subcla...

Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.

Journal of healthcare engineering
COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing characteristics, and the lack of a vaccine or effective medicines. A model for measuring and preventing the continued spread of COVID-19 is urgently re...

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

Surgery
BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these...

Structure equation model and neural network analyses to predict coronary artery lesions in Kawasaki disease: a single-centre retrospective study.

Scientific reports
A new method to predict coronary artery lesions (CALs) in Kawasaki disease (KD) was developed using a mean structure equation model (SEM) and neural networks (Nnet). There were 314 admitted children with KD who met at least four of the six diagnostic...

Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review.

Oral oncology
This systematic review analyses and describes the application and diagnostic accuracy of Artificial Intelligence (AI) methods used for detection and grading of potentially malignant (pre-cancerous) and cancerous head and neck lesions using whole slid...

AxoNet: A deep learning-based tool to count retinal ganglion cell axons.

Scientific reports
In this work, we develop a robust, extensible tool to automatically and accurately count retinal ganglion cell axons in optic nerve (ON) tissue images from various animal models of glaucoma. We adapted deep learning to regress pixelwise axon count de...