AIMC Topic: Humans

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Towards the automatic detection of activities of daily living using eye-movement and accelerometer data with neural networks.

Computers in biology and medicine
Early diagnosis of neurodegenerative diseases, such as Alzheimer's disease, improves treatment and care outcomes for patients. Early signs of cognitive decline can be detected using functional scales, which are written records completed by a clinicia...

Feature efficiency in IoMT security: A comprehensive framework for threat detection with DNN and ML.

Computers in biology and medicine
BACKGROUND: To address critical security challenges in the Internet of Medical Things (IoMT), this study develops a feature selection framework to improve detection accuracy and computational efficiency in IoMT cybersecurity. By optimizing feature se...

Identifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.

The journal of prevention of Alzheimer's disease
BACKGROUND: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is c...

Neural networks through the lens of evolutionary dynamics.

Bio Systems
This article revisits Artificial Neural Networks (NNs) through the lens of Evolutionary Dynamics. The two most important features of NNs are shown to reflect the two most general processes of Evolutionary Dynamics. This overlap may serve as a new and...

Predicting working alliance in psychotherapy: A multi-modal machine learning approach.

Psychotherapy research : journal of the Society for Psychotherapy Research
OBJECTIVE: Session-by-session tracking of the working alliance enables clinicians to detect alliance deterioration and intervene accordingly, which has shown to improve treatment outcome, and reduce dropout. Despite this, regular use of alliance self...

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.

Science advances
DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory varian...

Deep learning-enabled filter-free fluorescence microscope.

Science advances
Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and suppressing background noise. However, the utilization of optical filtering sets increases the comple...

Prognostic Features for Overall Survival in Male Diabetic Patients Undergoing Hemodialysis Using Elastic Net Penalized Cox Regression; A Machine Learning Approach.

Archives of Iranian medicine
BACKGROUND: Diabetics constitute a significant percentage of hemodialysis (HD) patients with higher mortality, especially among male patients. A machine learning algorithm was used to optimize the prediction of time to death in male diabetic hemodial...

Analyzing Secondary Cancer Risk: A Machine Learning Approach.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: Addressing the rising cancer rates through timely diagnosis and treatment is crucial. Additionally, cancer survivors need to understand the potential risk of developing secondary cancer (SC), which can be influenced by several factors incl...

F-18 FDG PET/CT based Preoperative Machine Learning Prediction Models for Evaluating Regional Lymph Node Metastasis Status of Patients with Colon Cancer.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: This study aimed to develop a simple machine-learning model incorporating lymph node metastasis status with F-18 Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) and clinical information for predicting regio...