AIMC Topic: Machine Learning

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Machine Learning Assessment of Gestational Age in Accelerated Maturation, Delayed Maturation, Villous Edema, Chorangiosis, and Intrauterine Fetal Demise.

Archives of pathology & laboratory medicine
CONTEXT.—: Assessment of placental villous maturation is among the most common tasks in perinatal pathology. However, the significance of abnormalities in morphology is unclear and interobserver variability is significant.

EPOC: A 28-nm 5.3 pJ/SOP Event-Driven Parallel Neuromorphic Hardware With Neuromodulation-Based Online Learning.

IEEE transactions on biomedical circuits and systems
Bio-inspired neuromorphic hardware with learning ability is highly promising to achieve human-like intelligence, particularly in terms of high energy efficiency and strong environmental adaptability. Though many customized prototypes have demonstrate...

An Ultra-Low Power Wearable BMI System With Continual Learning Capabilities.

IEEE transactions on biomedical circuits and systems
Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery life. However,...

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

Breast cancer research and treatment
PURPOSE: To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negative predictive value to minimize unnecessary biopsies.

MCD-LightGBM System for Intelligent Analyzing Heterogeneous Clinical Drug Therapeutic Effects.

IEEE journal of biomedical and health informatics
Causal effect estimation of individual heterogeneity is a core issue in the field of causal inference, and its application in medicine poses an active and challenging problem. In high-risk decision-making domain such as healthcare, inappropriate trea...

Machine learning algorithms to predict stroke in China based on causal inference of time series analysis.

BMC neurology
IMPORTANCE: Identifying and managing high-risk populations for stroke in a targeted manner is a key area of preventive healthcare.

Identifying the key factors of mercury exposure in residents of southwestern Iran using machine learning algorithms.

Environmental geochemistry and health
It is necessary to predict hair mercury (Hg) levels and specify the related effective factors to develop preventive strategies to reduce Hg exposure in different regions. This study is the first effort to investigate the effectiveness of eight machin...

A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma.

Scientific reports
Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is extremely poor. Angiogenesis has been linked to clinical outcomes, tumor progression, and treatment sensitivity. However, the role of angiogenesis in...

Applying machine learning with MobileNetV2 model for rapid screening of vaginal discharge samples in vaginitis diagnosis.

Scientific reports
Vaginitis is a prevalent gynecological condition that impacts women's quality of life, with most women likely to experience it at least once. Traditional diagnosis involves manually observing vaginal discharge samples under a microscope. This process...