AIMC Topic: Early Diagnosis

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Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.

Journal of medical systems
Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic info...

An accessible and efficient autism screening method for behavioural data and predictive analyses.

Health informatics journal
Autism spectrum disorder is associated with significant healthcare costs, and early diagnosis can substantially reduce these. Unfortunately, waiting times for an autism spectrum disorder diagnosis are lengthy due to the fact that current diagnostic p...

Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT.

International ophthalmology
PURPOSE: In this study, we compared deep learning (DL) with support vector machine (SVM), both of which use three-dimensional optical coherence tomography (3D-OCT) images for detecting epiretinal membrane (ERM).

[Expert systems-urgently needed for early diagnosis of sepsis : Results of retrospective clinical validation of the expert system FLORIDA].

Medizinische Klinik, Intensivmedizin und Notfallmedizin
The expert system FLORIDA (Fuzzy Logic Orientated Rule Interpreter for Diagnostic Applications) is equipped with a knowledge base applying linguistic rules of clinical experts according to the pathophysiologic conception of the sepsis-3 definition an...

An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.

Journal of the American Heart Association
BACKGROUND: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high fal...

Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach.

Journal of medical Internet research
BACKGROUND: Parents are likely to seek Web-based communities to verify their suspicions of autism spectrum disorder markers in their child. Automated tools support human decisions in many domains and could therefore potentially support concerned pare...

Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy.

BMC psychiatry
BACKGROUND: Early diagnosis of schizophrenia could improve the outcome of the illness. Unlike classical between-group comparisons, machine learning can identify subtle disease patterns on a single subject level, which could help realize the potential...

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

Physiological measurement
OBJECTIVE: Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distingu...

Predicting urinary tract infections in the emergency department with machine learning.

PloS one
BACKGROUND: Urinary tract infection (UTI) is a common emergency department (ED) diagnosis with reported high diagnostic error rates. Because a urine culture, part of the gold standard for diagnosis of UTI, is usually not available for 24-48 hours aft...