期刊名： Machine Learning and Knowledge Extraction

期刊主页：https://www.mdpi.com/journal/make

随着人工智能与机器学习技术的快速发展，数据驱动的方法正不断拓展其在医疗健康与生物医学领域中的应用边界。本专题汇集了发表于 Machine Learning and Knowledge Extraction（MAKE）的18篇2024和2025年高被引文章，系统展示了人工智能在疾病诊断、医学影像分析、临床预测和生物医学数据分析等方向的研究进展。

研究涵盖阿尔茨海默病、脑肿瘤、心血管疾病、脓毒症和膝骨关节炎等疾病的检测、分类与预测，以及医学图像分类与分割、胚胎选择、出生体重预测和基于RNA测序数据的癌症分类等多元应用场景。在方法层面，相关研究涉及卷积神经网络、贝叶斯网络与贝叶斯神经网络、超图卷积神经网络、物理信息引导深度学习、模拟退火超参数优化、深度学习与语言模型等多种机器学习技术，并探索了多模态数据融合、模型优化与可解释性等重要问题。

我们希望此次精选能够帮助读者了解人工智能赋能医疗健康与生物医学研究的最新进展，并为未来智能诊疗、医学影像分析和精准医疗的发展提供有益启发。

1. Alzheimers Disease Detection Using Deep Learning on Neuroimaging: A Systematic Review

基于神经影像深度学习的阿尔茨海默病检测：系统综述

https://www.mdpi.com/2504-4990/6/1/24

Alsubaie, M.G.; Luo, S.; Shaukat, K. Alzheimer’s Disease Detection Using Deep Learning on Neuroimaging: A Systematic Review. Mach. Learn. Knowl. Extr. 2024, 6, 464-505.

2. Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

基于卷积神经网络的医学图像分类：现有方法综述与文献统计建模

https://www.mdpi.com/2504-4990/6/1/33

Mohammed, F.A.; Tune, K.K.; Assefa, B.G.; Jett, M.; Muhie, S. Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature. Mach. Learn. Knowl. Extr. 2024, 6, 699-735.

3. Bayesian Networks for the Diagnosis and Prognosis of Diseases: A Scoping Review

用于疾病诊断与预后的贝叶斯网络：范围综述

https://www.mdpi.com/2504-4990/6/2/58

Polotskaya, K.; Muñoz-Valencia, C.S.; Rabasa, A.; Quesada-Rico, J.A.; Orozco-Beltrán, D.; Barber, X. Bayesian Networks for the Diagnosis and Prognosis of Diseases: A Scoping Review. Mach. Learn. Knowl. Extr. 2024, 6, 1243-1262.

4. Impact of Nature of Medical Data on Machine and Deep Learning for Imbalanced Datasets: Clinical Validity of SMOTE Is Questionable

医学数据特性对不平衡数据集机器学习与深度学习的影响：SMOTE的临床有效性值得质疑

https://www.mdpi.com/2504-4990/6/2/39

Gholampour, S. Impact of Nature of Medical Data on Machine and Deep Learning for Imbalanced Datasets: Clinical Validity of SMOTE Is Questionable. Mach. Learn. Knowl. Extr. 2024, 6, 827-841.

5. Multilayer Perceptron Neural Network with Arithmetic Optimization Algorithm-Based Feature Selection for Cardiovascular Disease Prediction

基于算术优化算法特征选择的多层感知器神经网络用于心血管疾病预测

https://www.mdpi.com/2504-4990/6/2/46

Alghamdi, F.A.; Almanaseer, H.; Jaradat, G.; Jaradat, A.; Alsmadi, M.K.; Jawarneh, S.; Almurayh, A.S.; Alqurni, J.; Alfagham, H. Multilayer Perceptron Neural Network with Arithmetic Optimization Algorithm-Based Feature Selection for Cardiovascular Disease Prediction. Mach. Learn. Knowl. Extr. 2024, 6, 987-1008.

6. Application of Bayesian Neural Networks in Healthcare: Three Case Studies

贝叶斯神经网络在医疗健康领域的应用：三个案例研究

https://www.mdpi.com/2504-4990/6/4/127

Ngartera, L.; Issaka, M.A.; Nadarajah, S. Application of Bayesian Neural Networks in Healthcare: Three Case Studies. Mach. Learn. Knowl. Extr. 2024, 6, 2639-2658.

7. Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach

出生体重范围预测与分类：一种基于机器学习的可持续方法

https://www.mdpi.com/2504-4990/6/2/36

Alabbad, D.A.; Ajibi, S.Y.; Alotaibi, R.B.; Alsqer, N.K.; Alqahtani, R.A.; Felemban, N.M.; Rahman, A.; Aljameel, S.S.; Ahmed, M.I.B.; Youldash, M.M. Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach. Mach. Learn. Knowl. Extr. 2024, 6, 770-788.

8. Empowering Brain Tumor Diagnosis through Explainable Deep Learning

利用可解释深度学习助力脑肿瘤诊断

https://www.mdpi.com/2504-4990/6/4/111

Li, Z.; Dib, O. Empowering Brain Tumor Diagnosis through Explainable Deep Learning. Mach. Learn. Knowl. Extr. 2024, 6, 2248-2281.

9. Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification

数据增强对基于深度学习的医学图像分类可解释性的影响分析

https://www.mdpi.com/2504-4990/7/1/1

Liu, X.; Karagoz, G.; Meratnia, N. Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification. Mach. Learn. Knowl. Extr. 2025, 7, 1.

10. Explicit Physics-Informed Deep Learning for Computer-Aided Diagnostic Tasks in Medical Imaging

面向医学影像计算机辅助诊断任务的显式物理信息引导深度学习

https://www.mdpi.com/2504-4990/6/1/19

Nemirovsky-Rotman, S.; Bercovich, E. Explicit Physics-Informed Deep Learning for Computer-Aided Diagnostic Tasks in Medical Imaging. Mach. Learn. Knowl. Extr. 2024, 6, 385-401.

11. AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview

人工智能在重症监护病房中的进展：聚焦脓毒症预测

https://www.mdpi.com/2504-4990/7/1/6

Stylianides, C.; Nicolaou, A.; Sulaiman, W.A.; Alexandropoulou, C.-A.; Panagiotopoulos, I.; Karathanasopoulou, K.; Dimitrakopoulos, G.; Kleanthous, S.; Politi, E.; Ntalaperas, D.; et al. AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview. Mach. Learn. Knowl. Extr. 2025, 7, 6.

12. Artificial Intelligence-Empowered Embryo Selection for IVF Applications: A Methodological Review

人工智能赋能的体外受精胚胎选择：方法学综述

https://www.mdpi.com/2504-4990/7/2/56

Moysis, L.; Iliadis, L.A.; Vergos, G.; Sotiroudis, S.P.; Boursianis, A.D.; Papatheodorou, A.; Kokkinidis, K.-I.D.; Abdul Matin, M.; Sarigiannidis, P.; Siniosoglou, I.; et al. Artificial Intelligence-Empowered Embryo Selection for IVF Applications: A Methodological Review. Mach. Learn. Knowl. Extr. 2025, 7, 56.

13. Simulated Annealing-Based Hyperparameter Optimization of a Convolutional Neural Network for MRI Brain Tumor Classification

基于模拟退火的卷积神经网络超参数优化用于MRI脑肿瘤分类

https://www.mdpi.com/2504-4990/7/2/50

El Amoury, S.; Smili, Y.; Fakhri, Y. Simulated Annealing-Based Hyperparameter Optimization of a Convolutional Neural Network for MRI Brain Tumor Classification. Mach. Learn. Knowl. Extr. 2025, 7, 50.

14. A Novel Approach Based on Hypergraph Convolutional Neural Networks for Cartilage Shape Description and Longitudinal Prediction of Knee Osteoarthritis Progression

一种基于超图卷积神经网络的软骨形状描述与膝骨关节炎进展纵向预测新方法

https://www.mdpi.com/2504-4990/7/2/40

Theocharis, J.B.; Chadoulos, C.G.; Symeonidis, A.L. A Novel Approach Based on Hypergraph Convolutional Neural Networks for Cartilage Shape Description and Longitudinal Prediction of Knee Osteoarthritis Progression. Mach. Learn. Knowl. Extr. 2025, 7, 40.

15. A Comparison Between Unimodal and Multimodal Segmentation Models for Deep Brain Structures from T1- and T2-Weighted MRI

基于T1加权和T2加权MRI的深部脑结构单模态与多模态分割模型比较

https://www.mdpi.com/2504-4990/7/3/84

Altini, N.; Lasaracina, E.; Galeone, F.; Prunella, M.; Suglia, V.; Carnimeo, L.; Triggiani, V.; Ranieri, D.; Brunetti, G.; Bevilacqua, V. A Comparison Between Unimodal and Multimodal Segmentation Models for Deep Brain Structures from T1- and T2-Weighted MRI. Mach. Learn. Knowl. Extr. 2025, 7, 84.

16. Enhancing Cancer Classification from RNA Sequencing Data Using Deep Learning and Explainable AI

利用深度学习与可解释人工智能提升基于RNA测序数据的癌症分类

https://www.mdpi.com/2504-4990/7/4/114

Younis, H.; Minghim, R. Enhancing Cancer Classification from RNA Sequencing Data Using Deep Learning and Explainable AI. Mach. Learn. Knowl. Extr. 2025, 7, 114.

17. Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare

小型还是大型？零样本还是微调？医疗健康专业应用中的语言模型选择指南

https://www.mdpi.com/2504-4990/7/4/121

Gondara, L.; Simkin, J.; Sayle, G.; Devji, S.; Arbour, G.; Ng, R. Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare. Mach. Learn. Knowl. Extr. 2025, 7, 121.

18. Automatic Prompt Generation Using Class Activation Maps for Foundational Models: A Polyp Segmentation Case Study

利用类激活图为基础模型自动生成提示词：息肉分割案例研究

https://www.mdpi.com/2504-4990/7/1/22

Borgli, H.; Stensland, H.K.; Halvorsen, P. Automatic Prompt Generation Using Class Activation Maps for Foundational Models: A Polyp Segmentation Case Study. Mach. Learn. Knowl. Extr. 2025, 7, 22.

期刊介绍

主编：Prof. Dr. Andreas Holzinger

Machine Learning and Knowledge Extraction (ISSN 2504-4990) 是一个国际型开放获取英文学术期刊，聚焦机器学习和知识提取所有领域相关的研究。期刊发表综述、研究论文、通讯、观点，以及特定主题的特刊。主题领域包括但不限于：机器学习；知识提取；人工智能；神经网络；自然语言处理；无监督学习；隐私保护；不确定性；迁移学习；图像分类；信息检索；特征选择；可视化；基于网络和图的机器学习；几何机器学习与拓扑学；熵及其在机器学习中的应用

2025 Impact Factor：8.4

2025 CiteScore：12.7

Time to First Decision：18.7 Days

Acceptance to Publication：3.8 Days