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Showing 20 of 1,249 academic articles

ACHENY: A standard Chenopodiaceae image dataset for deep learning models

Ahmad Heidary-Sharifabad, Mohsen Sardari Zarchi, Sima Emadi, Gholamreza Zarei • Data in Brief (2021)

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...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.dib.2021.107478

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

Tongtong Huo, Wei Wu, Xiaoliang Chen, Mingdi Xue et al. • Intelligent Oncology (2025)

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...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.intonc.2025.06.005

Data Mining Techniques in Medical Informatics

U. Rajendra Acharya, Wenwei Yu • The Open Medical Informatics Journal (2010)

The advent of high-performance computing has benefited various disciplines in finding practical solutions to their problems, and our health care is no exception to this.Signal processing, image processing, and data mining tools have been de...

OPENALEX cc-by-nc DOI: https://doi.org/10.2174/1874431101004010021 PMC: 2916206

Real-World Evaluation of AI-Driven Diabetic Retinopathy Screening in Public Health Settings: Validation and Implementation Study

Mona Duggal, Anshul Chauhan, Vishali Gupta, Ankita Kankaria et al. • JMIR Medical Informatics (2025)

Abstract BackgroundArtificial intelligence (AI) algorithms offer an effective solution to alleviate the burden of diabetic retinopathy (DR) screening in public health settings. However, there are challenges in translating diagno...

DOAJ Open Access (DOAJ) DOI: 10.2196/67529

G-Aligner: a graph-based feature alignment method for untargeted LC–MS-based metabolomics

Ruimin Wang, Miaoshan Lu, Shaowei An, Jinyin Wang et al. • BMC Bioinformatics (2023)

Abstract Background Liquid chromatography–mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment ...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12859-023-05525-4

Injury death certificates without specification of the circumstances leading to the fatal injury – the Norwegian Cause of Death Registry 2005–2014

Christian Lycke Ellingsen, Marta Ebbing, G. Cecilie Alfsen, Stein Emil Vollset • Population Health Metrics (2018)

Abstract Background For injury deaths, the underlying cause of death is defined as the circumstances leading to the injury. When this information is missing, the ICD-10 code X59 (Exposure to unspecified factor) is used. Lack of knowledge of...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12963-018-0176-2

Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors

Ariel A. Hippen, Dalia K. Omran, Lukas M. Weber, Euihye Jung et al. • Genome Biology (2023)

Abstract Background Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13059-023-03077-7

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

Gábor Kőrösi, Oliver Czimbalmos, Gabriella Kekesi, Gyongyi Horvath • Scientific Data (2026)

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...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41597-026-06735-0

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

Kai-Chih Pai, Wen-Cheng Chao, Yu-Len Huang, Ruey-Kai Sheu et al. • Digital Health (2022)

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...

DOAJ Open Access (DOAJ) DOI: 10.1177/20552076221120317

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

Ye Kuang, Yan Yu, Jing Li, Chuanmei Peng et al. • BMC Medical Informatics and Decision Making (2026)

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...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12911-026-03461-w

Exploring feature selection and classification methods for predicting heart disease

Robinson Spencer, Fadi Thabtah, Neda Abdelhamid, Michael Thompson • Digital Health (2020)

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...

DOAJ Open Access (DOAJ) DOI: 10.1177/2055207620914777

Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

Daigle Bernie J, Roh Min K, Petzold Linda R, Niemi Jarad • BMC Bioinformatics (2012)

Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data i...

DOAJ Open Access (DOAJ) DOI: 10.1186/1471-2105-13-68

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

Alireza Kasaie, Suchithra Rajendran • Healthcare Analytics (2023)

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 ...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.health.2023.100242

Central vein sign: A diagnostic biomarker in multiple sclerosis (CAVS-MS) study protocol for a prospective multicenter trial

D. Ontaneda, P. Sati, P. Raza, M. Kilbane et al. • NeuroImage: Clinical (2021)

The specificity and implementation of current MRI-based diagnostic criteria for multiple sclerosis (MS) are imperfect. Approximately 1 in 5 of individuals diagnosed with MS are eventually determined not to have the disease, with overrelianc...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.nicl.2021.102834

Multi-label classification of fundus images based on graph convolutional network

Yinlin Cheng, Mengnan Ma, Xingyu Li, Yi Zhou • BMC Medical Informatics and Decision Making (2021)

Abstract Background Diabetic Retinopathy (DR) is the most common and serious microvascular complication in the diabetic population. Using computer-aided diagnosis from the fundus images has become a method of detecting retinal diseases, but...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12911-021-01424-x

Argumentation approaches for explanaible AI in medical informatics

Luciano Caroprese, Eugenio Vocaturo, Ester Zumpano • Intelligent Systems with Applications (2022)

Artificial Intelligence algorithms are powerful in performing accurate predictions, but they are often considered black boxes as they do not provide any explanation about how outputs are derived from inputs or why a decision is taken. There...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.iswa.2022.200109