# Exploring Computational Methods to Advance Clinical Decision Making
###### Abstract
We curated a collection of studies that address innovative procedures in evidence-based medicine, data analytics advances and risk management in medical treatment decisions, applications of artificial intelligence methods, modeling and simulation in medical decision making, as well as policy development for better evidence-based practices and services. Collectively, these works exemplify the translational potential of advanced decision methodologies to deliver safer, more equitable, and evidence-informed clinical care. We strongly encouraged articles from interdisciplinary teams that include medical professionals, researchers, clinicians, data scientists, and AI experts. There are eleven state-of-the-art research articles in this Special Issue that are pertinent to the issues covered. Each paper addresses unique aspects of the enhancement of medical decision making through intelligent methods and data analysis. The contributions made by these studies reflect the variety and richness of current research, focusing on topics ranging from improved diagnostics using AI, through fuzzy data analysis for improved medical treatment, to exploration of adverse drug reactions and improved personalized health case and decision making. In the following paragraphs, we shortly describe the articles included in this Special Issue. Early or timely detection of serious medical conditions is an area where machine learning can considerably improve efficiency of medical care and patient outcomes. The work of [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], titled _An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning_[10], explores the use of pre-trained deep transfer learning (TL) for the detection and recognition of the three types of brain tumors--gliomas, meningiomas, and pituitary tumors. The latter are among the most critical, widespread, and life-threatening illnesses worldwide. More specifically, they assess the performance of nine pre-trained TL classifiers by automatically identifying and classifying brain tumors using a detailed classification method. The TL algorithms are tested on a baseline brain tumor classification (MRI) dataset, which is freely accessible on Kaggle. The deep learning (DL) models are fine-tuned using their default parameters. The authors find that the inceptionresnetv2 TL algorithm yields the best performance and achieves the highest accuracy in detecting and classifying glioma, meningioma, and pituitary brain tumors, thereby ranking as the top classification algorithm, surpassing the other DL algorithms. The authors also verify their results through comparison with hybrid methods, where they employ convolutional neural networks (CNNs) for deep feature extraction and a support vector machine (SVM) for classification. Another study that deals with improved detection of serious medical conditions is by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], titled _Exploring Early Prediction of Chronic Kidney Disease Using Machine Learning Algorithms for Small and Imbalanced Datasets_[11]. The authors discuss chronic kidney disease (CKD), which is recognized as a global public health challenge, and is typically identified during the later stages of the condition due to imbalanced and small datasets. The work uses medical record data about Brazilians with and without a CKD diagnosis, which includes attributes such as hypertension, diabetes mellitus, creatinine levels, urea, albuminuria, age, gender, and glomerular filtration rate. They then apply oversampling methods with both manual and automated augmentation techniques, such as the synthetic minority oversampling technique (SMOTE), borderline-SMOTE, and borderline-SMOTE support vector machine (borderline-SMOTE SVM). Modeling is performed using decision trees (DTs), random forests, and multi-class AdaBoosted DT algorithms. The authors also apply methods for dynamic classifier selection, such as overall local accuracy and local class accuracy.