AIMC Topic: Machine Learning

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A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer's Disease.

Cells
The clinical spectrum of Alzheimer's disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been develo...

Anomaly-based threat detection in smart health using machine learning.

BMC medical informatics and decision making
BACKGROUND: Anomaly detection is crucial in healthcare data due to challenges associated with the integration of smart technologies and healthcare. Anomaly in electronic health record can be associated with an insider trying to access and manipulate ...

Analysis and prediction of infectious diseases based on spatial visualization and machine learning.

Scientific reports
Infectious diseases are a global public health problem that poses a threat to human society. Since the 1970s, constantly mutated new infectious viruses have been quietly attacking humanity, and at least one new type of infectious disease is discovere...

Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients.

Scientific reports
Knee osteoarthritis (KOA) combined with depressive symptoms is prevalent and leads to poor outcomes and significant financial burdens. However, practical tools for identifying at-risk patients remain limited. A robust prediction model is needed to ad...

Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases.

Scientific reports
The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for ...

Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.

Scientific reports
With the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researc...

A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy.

Scientific reports
Current approaches for cardiac amyloidosis (CA) identification are time-consuming, labor-intensive, and present challenges in sensitivity and accuracy, leading to limited treatment efficacy and poor prognosis for patients. In this retrospective study...

Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach.

JMIR medical informatics
BACKGROUND: Prostate cancer is the second leading cause of death among American men. If detected and treated at an early stage, prostate cancer is often curable. However, an advanced stage such as metastatic castration-resistant prostate cancer (mCRP...

Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity.

European journal of medical research
BACKGROUND: Cytopathological examination serves as a tool for diagnosing solid tumors and hematologic malignancies. Artificial intelligence (AI)-assisted methods have been widely discussed in the literature for increasing sensitivity, specificity and...

Aneurysmal formation of periventricular anastomosis is associated with collateral development of Moyamoya disease and its rupture portends poor prognosis: detailed analysis by multivariate statistical and machine learning approaches.

Neurosurgical review
Periventricular anastomosis (PA) is the characteristic collateral network in Moyamoya disease (MMD). However, PA aneurysms are rare, resulting in limited knowledge of their clinical significance. We aimed to elucidate the associated factors and clini...