AIMC Topic: Female

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Prediction of hospital no-show appointments through artificial intelligence algorithms.

Annals of Saudi medicine
BACKGROUND: No-shows, a major issue for healthcare centers, can be quite costly and disruptive. Capacity is wasted and expensive resources are underutilized. Numerous studies have shown that reducing uncancelled missed appointments can have a tremend...

Automated oral squamous cell carcinoma identification using shape, texture and color features of whole image strips.

Tissue & cell
Despite profound knowledge of the incidence of oral cancers and a large body of research beyond it, it continues to beat diagnosis and treatment management. Post physical observation by clinicians, a biopsy is a gold standard for accurate detection o...

Epidemiological Features of Human Brucellosis in Iran (2011-2018) and Prediction of Brucellosis with Data-Mining Models.

Journal of research in health sciences
BACKGROUND: Brucellosis is known as the major zoonotic disease. We aimed to compare the performance of some data-mining models in predicting the monthly brucellosis cases in Iran.

Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction.

Scientific reports
Chronic and recurrent opiate use injuries brain tissue and cause serious pathophysiological changes in hemodynamic and subsequent inflammatory responses. Prefrontal cortex (PFC) has been implicated in drug addiction. However, the mechanism underlying...

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Genomics, proteomics & bioinformatics
Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are applied to discover biomarker signatures. These have to be validated in larger cohorts and evaluated by ...

Potential of machine learning methods to identify patients with nonvalvular atrial fibrillation.

Future cardiology
Nonvalvular atrial fibrillation (NVAF) is associated with an increased risk of stroke however many patients are diagnosed after onset. This study assessed the potential of machine-learning algorithms to detect NVAF. A retrospective database study u...

Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features.

Sensors (Basel, Switzerland)
The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of op...

Effectiveness of a chat-bot for the adult population to quit smoking: protocol of a pragmatic clinical trial in primary care (Dejal@).

BMC medical informatics and decision making
BACKGROUND: The wide scale and severity of consequences of tobacco use, benefits derived from cessation, low rates of intervention by healthcare professionals, and new opportunities stemming from novel communications technologies are the main factors...

Subgrouping Factors Influencing Migraine Intensity in Women: A Semi-automatic Methodology Based on Machine Learning and Information Geometry.

Pain practice : the official journal of World Institute of Pain
BACKGROUND: Migraine is a heterogeneous condition with multiple clinical manifestations. Machine learning algorithms permit the identification of population groups, providing analytical advantages over other modeling techniques.

Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis.

Medical image analysis
Although convolutional neural networks have achieved tremendous success on histopathology image classification, they usually require large-scale clean annotated data and are sensitive to noisy labels. Unfortunately, labeling large-scale images is lab...