Information Technology

ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data

Symptom phenotypes are one of the key types of manifestations for diagnosis and treatment of various disease conditions. However, the diversity of symptom terminologies is one of the major obstacles hindering the analysis and knowledge sharing of various types of symptom-related medical data particularly in the fields of Traditional Chinese Medicine (TCM). Objective: This study aimed to construc...

Evaluating Predictive Models in Cybersecurity: A Comparative Analysis of Machine and Deep Learning Techniques for Threat Detection

As these attacks become more and more difficult to see, the need for the great hi-tech models that detect them is undeniable. This paper examines and compares various machine learning as well as deep learning models to choose the most suitable ones for detecting and fighting against cybersecurity risks. The two datasets are used in the study to assess models like Naive Bayes, SVM, Random Forest,...

Multi-modal Masked Siamese Network Improves Chest X-Ray Representation Learning

Self-supervised learning methods for medical images primarily rely on the imaging modality during pretraining. While such approaches deliver promisi...

Performance Analysis and Comparison of Full-Fledged 5G Standalone Experimental TDD Testbeds in Single & Multi-UE Scenarios

Open-source software and Commercial Off-The-Shelf hardware are finally paving their way into the 5G world, resulting in a proliferation of experimen...

A Proposal for a FAIR Management of 3D Data in Cultural Heritage: The Aldrovandi Digital Twin Case

In this article we analyse 3D models of cultural heritage with the aim of answering three main questions: what processes can be put in place to crea...

Introducing SWIRL: An Intermediate Representation Language for Scientific Workflows

In the ever-evolving landscape of scientific computing, properly supporting the modularity and complexity of modern scientific applications requires...

Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals,...

Enhancing Model Generalizability In Parkinson's Disease Automatic Assessment: A Semi-Supervised Approach Across Independent Experiments.

Machine learning in Parkinson's disease assessment uses data from clinically-coded movements, such as finger tapping, to objectively measure motor imp...

Towards the development of a FAIR-compliant biomedical ontology for colorectal cancer.

Despite the widespread development of ontologies in many domains of healthcare, the field of colorectal cancer (CRC) presents a notable gap considerin...

Video-based Clinical Gait Analysis in Parkinson's Disease: A Novel Approach Using Frontal Plane Videos and Machine Learning.

Gait can be significantly impaired by neurological conditions such as Parkinson's disease (PD). Gait impairments can be quantified by using instrument...

Smartphone-Based Balance Assessment Using Machine Learning.

This study explores the potential of smartphones to objectively assess balance, which is crucial for the elderly and individuals recovering from vario...

Remote Motor Rehabilitation: EMG-IMU based Deep Learning Model Improves the Estimate of Wrist Kinematics.

Technology for motor rehabilitation faces challenges in uncontrolled settings, such as at home. In these real-world scenarios, robust signals like ele...

Cost-Saving Data-Driven Diabetic Retinopathy Prediction via a Sampling-Empowered Incremental Learning Approach.

Diabetic retinopathy (DR) is a serious complication of diabetes that can lead to vision impairment or even blindness if not detected and treated in th...

PhishNet: A Phishing Website Detection Tool using XGBoost

PhisNet is a cutting-edge web application designed to detect phishing websites using advanced machine learning. It aims to help individuals and orga...

TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.

MOTIVATION: Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advan...

Evaluating the Efficacy of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-Tuning Decision-Making

Recently, large language models (LLMs) have expanded into various domains. However, there remains a need to evaluate how these models perform when p...

From data to diagnosis: skin cancer image datasets for artificial intelligence.

Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models,...

Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy

An Electronic Health Record (EHR) is an electronic database used by healthcare providers to store patients' medical records which may include diagno...

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

OBJECTIVE: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to ...

Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.

OBJECTIVE: Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health rec...