Academic Articles & Research Papers

TRANSFER LEARNING: INCEPTION-V3 BASED CUSTOM CLASSIFICATION APPROACH FOR FOOD IMAGES

Deep-learning approach has become more popular in the field of image processing. When it is concerned with health issues, there are lots of improvements in the applications of food image classification by deep learning methods. Transfer lea...

Machine learning framework for early identification of high-spending Crohn’s disease patients using administrative claims

Objectives To develop and evaluate a machine learning (ML) model that predicts Crohn’s disease (CD) patients responsible for the top quartile of healthcare spending.Methods De-identified commercial claims (2016–2018) from ~267 000 continuou...

Online peer support for Young Onset Dementia is a promising resource, but not a panacea: a commentary from the INTERDEM Young Onset Dementia taskforce

Web-based versus simulation-based refresher training in newborn life support

Background: Regular training for healthcare professionals in newborn life support is life-saving. Web-based refresher has the potential to be non-inferior to simulation-based refresher training. Methods: The multicenter non-inferiority rand...

ACHENY: A standard Chenopodiaceae image dataset for deep learning models

This paper contains datasets related to the “Efficient Deep Learning Models for Categorizing Chenopodiaceae in the wild” (Heidary-Sharifabad et al., 2021). There are about 1500 species of Chenopodiaceae that are spread worldwide and often a...

Deep learning-based multimodal data fusion in bone tumor management: Advances in clinical decision support

Bone tumors (BTs)—including osteosarcoma, Ewing sarcoma, and chondrosarcoma—are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location, histological subtype, and molecular alterations. Re...

Automated measurement of CD8+ T-cell distribution at the tumor epithelial–stromal interface is associated with NSCLC immunotherapy benefit and transcriptomic pathways

Whereas anti-PD-(L)1 therapies are widely used in cancer treatment, only a subset of patients achieve long-term survival. Companion diagnostics based on PD-L1 expression have limited predictive power for these therapies, motivating developm...

Behavioral dataset for Long-Evans and its schizophrenia-like substrain through several generations

Abstract We present a high-throughput behavioral dataset acquired with Ambitus, an automated reward-based corridor system that records locomotor and exploratory activities and cognitive functions after minimal handling. The collection conta...

The Role of iSchools in Medical Informatics

Similar to iSchools, healthcare is an interdisciplinary field that encompasses heterogeneous environments (e.g., hospitals, clinics, home) and perspectives. However, medical informatics research focuses mainly on technical solutions to narr...

Artificial intelligence–aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs

Objective The aim of this study was to develop an artificial intelligence–based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data. Method The transfer learning method w...

Classroom Management Strategies of Multigrade Schools with Emphasis on the Role of Technology

Introduction : It appears that the effectiveness of a multigrade school class is dependent on the type of class management. Therefore this study aims to determine the classroom management strategies in multigrade schools with emphasis on th...

Deep learning prediction of pathological complete response in breast cancer using Mamba architecture

Abstract Deep learning is capable of efficiently predicting the therapeutic efficacy of neoadjuvant chemotherapy (NAC) in breast cancer. However, current methods predominantly rely on convolutional neural networks or transformer architectur...

A career in medical informatics

Mohammad Al-Ubaydli explains how to have a career in medical informatics1 If you want to understand why I love technology, take a look at my hearing aid. Just before starting my house job, my nightmare scenario was of being deafened by the...

Development and validation of an interpretable machine learning model for predicting the risk of coronary heart disease risk in diabetes mellitus patients: a dual-center retrospective study

Abstract Background Diabetes mellitus (DM) combined with coronary heart disease (CHD) significantly increases the risk of cardiovascular events with a greater mortality rate. Therefore, establishing a predictive model can help DM patients r...

Analysis of Strengths, Weaknesses, Opportunities and Threats of E-learning from the Perspective of Experts in the Period of COVID-19 Pandemic

Background: Today, e-learning has become one of the basic components of education process, especially in higher education. Institutions and universities employ e-learning extensively in their educational operations. In light of this, the go...

Identifying peer experts in online health forums

Abstract Background Online health forums have become increasingly popular over the past several years. They provide members with a platform to network with peers and share information, experiential advice, and support. Among the members of ...

The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

Abstract Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for ...

Exploring feature selection and classification methods for predicting heart disease

Machine learning has been used successfully to improve the accuracy of computer-aided diagnosis systems. This paper experimentally assesses the performance of models derived by machine learning techniques by using relevant features chosen b...

From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the ...

Integrating machine learning algorithms and explainable artificial intelligence approach for predicting patient unpunctuality in psychiatric clinics

This study addresses patient unpunctuality, a major concern affecting patient waiting time, resource utilization, and quality of care. We develop and compare four machine learning models, including multinomial logistic regression, decision ...