AIMC Topic: Early Diagnosis

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Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks.

International journal of environmental research and public health
Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health condit...

Improving Prediction Performance Using Hierarchical Analysis of Real-Time Data: A Sepsis Case Study.

IEEE journal of biomedical and health informatics
This paper presents a novel method for hierarchical analysis of machine learning algorithms to improve predictions of at risk patients, thus further enabling prompt therapy. Specifically, we develop a multi-layer machine learning approach to analyze ...

Early esophageal adenocarcinoma detection using deep learning methods.

International journal of computer assisted radiology and surgery
PURPOSE: This study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically identify esophageal adenocarcinoma (EAC) regions from high-definition white light endoscopy (HD-WLE) ...

Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis.

Annals of emergency medicine
STUDY OBJECTIVE: The Third International Consensus Definitions (Sepsis-3) Task Force recommended the use of the quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA) score to screen patients for sepsis outside of the ICU. However, subseq...

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease.

NeuroImage
Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable over-time and do not progress to AD. To identify and choose effective and personalized strategies to pr...

Renal Function Impairment in Kidney Transplantation: Importance of Early BK Virus Detection.

Transplantation proceedings
BACKGROUND: BK virus allograft nephropathy is a major complication of kidney transplantation that markedly reduces graft survival (50% graft failure 24 months after being diagnosed). BK virus replication can occur at any time posttransplantation. Vir...

Prediction of fatty liver disease using machine learning algorithms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for preven...

A deep learning approach to automatic detection of early glaucoma from visual fields.

PloS one
PURPOSE: To investigate the suitability of multi-scale spatial information in 30o visual fields (VF), computed from a Convolutional Neural Network (CNN) classifier, for early-glaucoma vs. control discrimination.

Mobile detection of autism through machine learning on home video: A development and prospective validation study.

PLoS medicine
BACKGROUND: The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access...

Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and improve the consistency of diagnosis and treatment in medicine. In this study, a new CAD system based o...