AIMC Topic: Machine Learning

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Prediction model of ipsilateral level II lymph node metastasis in papillary thyroid carcinoma.

Auris, nasus, larynx
OBJECTIVES: This study aimed to develop a predictive model for ipsilateral level II lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) using machine learning techniques. The necessity of level II dissection in lateral neck...

Predictors of smartphone addiction in adolescents with depression: combing the machine learning and moderated mediation model approach.

Behaviour research and therapy
Smartphone addiction (SA) significantly impacts the physical and mental health of adolescents, and can further exacerbate existing mental health issues in those with depression. However, fewer studies have focused on the predictors of SA in adolescen...

Advances in artificial intelligence for diabetes prediction: insights from a systematic literature review.

Artificial intelligence in medicine
Diabetes mellitus (DM), a prevalent metabolic disorder, has significant global health implications. The advent of machine learning (ML) has revolutionized the ability to predict and manage diabetes early, offering new avenues to mitigate its impact. ...

Estimating individualized effectiveness of receiving successful recanalization for ischemic stroke cases using machine learning techniques.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Directly measuring the causal effect of mechanical thrombectomy (MT) for each ischemic stroke patient remains challenging, as it is impossible to observe the outcomes for both with and without successful recanalization in the same individ...

Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models.

Artificial intelligence in medicine
A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical ...

Development of a patient reported outcomes based machine learning model to predict recurrences in head and neck cancer.

Oral oncology
INTRODUCTION: Recurrence rates among Head and Neck Cancer (HNC) patients are high, with earlier detection associated with improved survival. Patient-reported outcomes (PROs) have increasingly been found to predict patient care needs. Here, we examine...

Development and validation of a predictive machine learning model for postoperative long-term diabetes insipidus following transsphenoidal surgery for sellar lesions.

Clinical neurology and neurosurgery
OBJECTIVE: Diabetes Insipidus (DI) is a common complication that occurs following transsphenoidal surgery for sellar lesions. DI is usually transient but can be permanent in select patients. Prior studies have described preoperative risk factors for ...

Coronary artery disease severity and location detection using deep-mining-based magnetocardiography pattern features.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The objective of this study was to develop an automated, accurate method of assessing coronary artery disease (CAD), including its severity and location, using deep-mining-based magnetocardiography (MCG) pattern features.