AIMC Topic: Early Diagnosis

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Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.

The Lancet. Digital health
BACKGROUND: Cardiovascular outcomes for people with familial hypercholesterolaemia can be improved with diagnosis and medical management. However, 90% of individuals with familial hypercholesterolaemia remain undiagnosed in the USA. We aimed to accel...

Convolutional neural networks for multi-class brain disease detection using MRI images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The brain disorders may cause loss of some critical functions such as thinking, speech, and movement. So, the early detection of brain diseases may help to get the timely best treatment. One of the conventional methods used to diagnose these disorder...

Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models.

Computers in biology and medicine
Sub-clinical bovine mastitis decreases milk quality and production. Moreover, sub-clinical mastitis leads to the use of antibiotics with consequent increased risk of the emergence of antibiotic-resistant bacteria. Therefore, early detection of infect...

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
BACKGROUND: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).

Emerging role of eHealth in the identification of very early inflammatory rheumatic diseases.

Best practice & research. Clinical rheumatology
Digital health or eHealth technologies, notably pervasive computing, robotics, big-data, wearable devices, machine learning, and artificial intelligence (AI), have opened unprecedented opportunities as to how the diseases are diagnosed and managed wi...

Automated detection and classification of early AMD biomarkers using deep learning.

Scientific reports
Age-related macular degeneration (AMD) affects millions of people and is a leading cause of blindness throughout the world. Ideally, affected individuals would be identified at an early stage before late sequelae such as outer retinal atrophy or exud...

Prodromal clinical, demographic, and socio-ecological correlates of asthma in adults: a 10-year statewide big data multi-domain analysis.

The Journal of asthma : official journal of the Association for the Care of Asthma
To identify prodromal correlates of asthma as compared to chronic obstructive pulmonary disease and allied-conditions (COPDAC) using a multi domain analysis of socio-ecological, clinical, and demographic domains. This is a retrospective case-risk-co...

The "MS-ROM/IFAST" Model, a Novel Parallel Nonlinear EEG Analysis Technique, Distinguishes ASD Subjects From Children Affected With Other Neuropsychiatric Disorders With High Degree of Accuracy.

Clinical EEG and neuroscience
. In a previous study, we showed a new EEG processing methodology called Multi-Scale Ranked Organizing Map/Implicit Function As Squashing Time (MS-ROM/IFAST) performing an almost perfect distinction between computerized EEG of Italian children with a...

Application of artificial neural network model in diagnosis of Alzheimer's disease.

BMC neurology
BACKGROUND: Alzheimer's disease has become a public health crisis globally due to its increasing incidence. The purpose of this study was to establish an early warning model using artificial neural network (ANN) for early diagnosis of AD and to explo...