AIMC Topic: Disease Susceptibility
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Machine Learning to Quantify Humoral Selection in Human Lupus Tubulointerstitial Inflammation.
Frontiers in immunology
Nov 27, 2020
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
Nov 10, 2020
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...
Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning.
EBioMedicine
Nov 7, 2020
BACKGROUND: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversit...
Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.
Journal of healthcare engineering
Oct 29, 2020
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
Sep 18, 2020
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
Jul 17, 2020
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
Jul 13, 2020
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
May 15, 2020
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...
Artificial intelligence and machine learning to fight COVID-19.
Physiological genomics
Mar 27, 2020
Long Short-Term Memory Recurrent Neural Networks for Multiple Diseases Risk Prediction by Leveraging Longitudinal Medical Records.
IEEE journal of biomedical and health informatics
Dec 25, 2019
Individuals suffer from chronic diseases without being identified in time, which brings lots of burden of disease to the society. This paper presents a multiple disease risk prediction method to systematically assess future disease risks for patients...
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