Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

Advanced Decision Making in Clinical Medicine

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 21469

Share This Special Issue

Special Issue Editors

Prof. Kiril Tenekedjiev

E-Mail Website
Guest Editor

Australian Maritime College (AMC), National Centre for Maritime Engineering and Hydrodynamics (NCMEH), University of Tasmania (UTAS), Launceston, TAS 7248, Australia
Interests: decision sciences; intelligent systems; simulation modelling; subjective statistics; econometrics

Prof. Dr. Mike Calford

E-Mail Website
Guest Editor

Hunter Medical Research Institute, Locked Bag 1000, New Lambton, NSW, Australia
Interests: neuroscience; plasticity; sensory

Special Issue Information

Dear Colleagues,

In modern medical practice, patients should be actively engaged in all medical treatment decisions since these influence their quality of life and strongly impact the livelihood of their family. However, patients are rarely fully qualified or confident enough to engage with all stages of decision-making concerning their treatment. One of the significant ways in which the public utilizes its health system is through the support that medical professionals and medical researchers bring to the understanding of the implications of patients’ medical decisions. The proper medical decision-making is at the heart of evidence-based medicine. It integrates the expertise of medical professionals with the preferences and value system of the patient and with the best possible interpretation of medical information in order to effectively guide medical decisions in clinical practice. Recent advances in artificial intelligence, data science and statistics are in position to improve the quality of medical decision-making and raise the confidence of the public in the value of the proposed solutions.

In this Special Issue, we want to address recent advances in the following key areas:

Developments and applications in evidence-based medicine concepts and procedures;
Data analytics advances in medical treatment decisions;
Risk management in medical decisions;
Diagnostics for better treatment and optimal medical outcomes;
Application of intelligent methods to medical decision making;
Modelling and simulation in medical decision making;
Policy developments for evidence-based medicine;
Data, knowledge and decisions for better health services and health practices;
Research to recognize underlying relationships or develop diagnostic and therapeutic solutions based on interdisciplinary approach combining biomedical and bioinformatics tools (big data analysis, artificial intelligence, decision trees, neural networks).

In case you feel uncertain as to whether your research aligns with these topics, please approach the guest editors to discuss whether your research is in line with the intended outcomes of the Special Issue.

We invite authors to submit manuscripts that represent original research, case study results, and reviews. We strongly encourage submissions from diverse author teams that include medical professionals/researchers, clinicians, data scientists and artificial intelligence experts.

We wish to see this Special Issue making significant contribution and serve as inspiration for future developments in medical decision support.

Prof. Kiril Tenekedjiev
Prof. Dr. Mike Calford
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (8 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

17 pages, 1928 KiB

Open AccessArticle

An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning

by Naeem Ullah, Javed Ali Khan, Mohammad Sohail Khan, Wahab Khan, Izaz Hassan, Marwa Obayya, Noha Negm and Ahmed S. Salama

Appl. Sci. 2022, 12(11), 5645; https://doi.org/10.3390/app12115645 - 02 Jun 2022

Cited by 42 | Viewed by 5060

Abstract

Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techniques to identify and detect glioma, meningioma and pituitary brain tumors. The aim is to identify the performance of nine pre-trained TL classifiers, i.e., Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2, by automatically identifying and detecting brain tumors using a fine-grained classification approach. For this, the TL algorithms are evaluated on a baseline brain tumor classification (MRI) dataset, which is freely available on Kaggle. Additionally, all deep learning (DL) models are fine-tuned with their default values. The fine-grained classification experiment demonstrates that the inceptionresnetv2 TL algorithm performs better and achieves the highest accuracy in detecting and classifying glioma, meningioma and pituitary brain tumors, and hence it can be classified as the best classification algorithm. We achieve 98.91% accuracy, 98.28% precision, 99.75% recall and 99% F-measure values with the inceptionresnetv2 TL algorithm, which out-performs the other DL algorithms. Additionally, to ensure and validate the performance of TL classifiers, we compare the efficacy of the inceptionresnetv2 TL algorithm with hybrid approaches, in which we use convolutional neural networks (CNN) for deep feature extraction and a Support Vector Machine (SVM) for classification. Similarly, the experiment’s results show that TL algorithms, and inceptionresnetv2 in particular, out-perform the state-of-the-art DL algorithms in classifying brain MRI images into glioma, meningioma, and pituitary. The hybrid DL approaches used in the experiments are Mobilnetv2, Densenet201, Squeeznet, Alexnet, Googlenet, Inceptionv3, Resnet50, Resnet18, Resnet101, Xception, Inceptionresnetv3, VGG19 and Shufflenet. Full article

(This article belongs to the Special Issue Advanced Decision Making in Clinical Medicine)

► Show Figures

Figure 1

25 pages, 832 KiB

Open AccessArticle

Exploring Early Prediction of Chronic Kidney Disease Using Machine Learning Algorithms for Small and Imbalanced Datasets

by Andressa C. M. da Silveira, Álvaro Sobrinho, Leandro Dias da Silva, Evandro de Barros Costa, Maria Eliete Pinheiro and Angelo Perkusich

Appl. Sci. 2022, 12(7), 3673; https://doi.org/10.3390/app12073673 - 06 Apr 2022

Cited by 14 | Viewed by 2880

Abstract

Chronic kidney disease (CKD) is a worldwide public health problem, usually diagnosed in the late stages of the disease. To alleviate such issue, investment in early prediction is necessary. The purpose of this study is to assist the early prediction of CKD, addressing problems related to imbalanced and limited-size datasets. We used data from medical records of Brazilians with or without a diagnosis of CKD, containing the following attributes: hypertension, diabetes mellitus, creatinine, urea, albuminuria, age, gender, and glomerular filtration rate. We present an oversampling approach based on manual and automated augmentation. We experimented with the synthetic minority oversampling technique (SMOTE), Borderline-SMOTE, and Borderline-SMOTE SVM. We implemented models based on the algorithms: decision tree (DT), random forest, and multi-class AdaBoosted DTs. We also applied the overall local accuracy and local class accuracy methods for dynamic classifier selection; and the k-nearest oracles-union, k-nearest oracles-eliminate, and META-DES for dynamic ensemble selection. We analyzed the models’ performances using the hold-out validation, multiple stratified cross-validation (CV), and nested CV. The DT model presented the highest accuracy score (98.99%) using the manual augmentation and SMOTE. Our approach can assist in designing systems for the early prediction of CKD using imbalanced and limited-size datasets. Full article

(This article belongs to the Special Issue Advanced Decision Making in Clinical Medicine)

► Show Figures

Figure 1

25 pages, 908 KiB

Open AccessArticle

AI and Clinical Decision Making: The Limitations and Risks of Computational Reductionism in Bowel Cancer Screening

by Saleem Ameen, Ming-Chao Wong, Kwang-Chien Yee and Paul Turner

Appl. Sci. 2022, 12(7), 3341; https://doi.org/10.3390/app12073341 - 25 Mar 2022

Cited by 6 | Viewed by 3193

Abstract

Advances in artificial intelligence in healthcare are frequently promoted as ‘solutions’ to improve the accuracy, safety, and quality of clinical decisions, treatments, and care. Despite some diagnostic success, however, AI systems rely on forms of reductive reasoning and computational determinism that embed problematic assumptions about clinical decision-making and clinical practice. Clinician autonomy, experience, and judgement are reduced to inputs and outputs framed as binary or multi-class classification problems benchmarked against a clinician’s capacity to identify or predict disease states. This paper examines this reductive reasoning in AI systems for colorectal cancer (CRC) to highlight their limitations and risks: (1) in AI systems themselves due to inherent biases in (a) retrospective training datasets and (b) embedded assumptions in underlying AI architectures and algorithms; (2) in the problematic and limited evaluations being conducted on AI systems prior to system integration in clinical practice; and (3) in marginalising socio-technical factors in the context-dependent interactions between clinicians, their patients, and the broader health system. The paper argues that to optimise benefits from AI systems and to avoid negative unintended consequences for clinical decision-making and patient care, there is a need for more nuanced and balanced approaches to AI system deployment and evaluation in CRC. Full article

(This article belongs to the Special Issue Advanced Decision Making in Clinical Medicine)

► Show Figures

Figure 1

16 pages, 845 KiB

Open AccessCommunication

Anatomy of a Data Science Software Toolkit That Uses Machine Learning to Aid ‘Bench-to-Bedside’ Medical Research—With Essential Concepts of Data Mining and Analysis Explained

by László Beinrohr, Eszter Kail, Péter Piros, Erzsébet Tóth, Rita Fleiner and Krasimir Kolev

Appl. Sci. 2021, 11(24), 12135; https://doi.org/10.3390/app112412135 - 20 Dec 2021

Cited by 1 | Viewed by 1557

Abstract

Data science and machine learning are buzzwords of the early 21st century. Now pervasive through human civilization, how do these concepts translate to use by researchers and clinicians in the life-science and medical field? Here, we describe a software toolkit, just large enough in scale, so that it can be maintained and extended by a small team, optimised for problems that arise in small/medium laboratories. In particular, this system may be managed from data ingestion statistics preparation predictions by a single person. At the system’s core is a graph type database, so that it is flexible in terms of irregular, constantly changing data types, as such data types are common during explorative research. At the system’s outermost shell, the concept of ’user stories’ is introduced to help the end-user researchers perform various tasks separated by their expertise: these range from simple data input, data curation, statistics, and finally to predictions via machine learning algorithms. We compiled a sizable list of already existing, modular Python platform libraries usable for data analysis that may be used as a reference in the field and may be incorporated into this software. We also provide an insight into basic concepts, such as labelled-unlabelled data, supervised vs. unsupervised learning, regression vs. classification, evaluation by different error metrics, and an advanced concept of cross-validation. Finally, we show some examples from our laboratory using our blood sample and blood clot data from thrombosis patients (sufferers from stroke, heart and peripheral thrombosis disease) and how such tools can help to set up realistic expectations and show caveats. Full article

(This article belongs to the Special Issue Advanced Decision Making in Clinical Medicine)

► Show Figures

Figure 1

8 pages, 1011 KiB

Open AccessArticle

Evolution of Hemodynamic Parameters Simulated by Means of Diffusion Models

by Andrzej Walczak, Paweł Moszczyński and Paweł Krzesiński

Appl. Sci. 2021, 11(23), 11412; https://doi.org/10.3390/app112311412 - 02 Dec 2021

Cited by 1 | Viewed by 1439

Abstract

Diffusion is a well-known physical phenomenon governing such processes as movement of particles or transportation of heat. In this paper, we prove that a close analogy to those processes exists in medical data behavior, and that changes in the values of medical parameters measured while treating patients may be described using diffusion models as well. The medical condition of a patient is usually described by a set of discrete values. The evolution of that condition and, consequently, of the disease has the form of a transition of that set of discrete values, which correspond to specific parameters. This is a typical medical diagnosis scheme. However, disease evolution is a phenomenon that is characterized by continuously varying, temporal characteristics. A mathematical disease evolution model is, in fact, a continuous diffusion process from one discrete slot of the diagnosed parameter value to another inside the mentioned set. The ability to predict such diffusion-related properties offer precious support in diagnostic decision-making. We have examined several hundred patients while conducting a medical research project. All patients were under treatment to stabilize their hemodynamic parameters. A diffusion model relied upon simulating the results of treatment is proposed here. Time evolution of thoraric fluid content (TFC) has been used as the illustrative example. The objective is to prove that diffusion models are a proper and convenient solution for predicting disease evolution processes. We applied the Fokker-Planck equation (FPE), considering it to be most adequate for examining the treatment results by means of diffusion. We confirmed that the phenomenon of diffusion explains the evolution of the heart disease parameters observed. The evolution of TFC has been chosen as an example of a hemodynamic parameter. Full article

(This article belongs to the Special Issue Advanced Decision Making in Clinical Medicine)

► Show Figures

Figure 1

9 pages, 1053 KiB

Open AccessArticle

Decision Tree-Based Data Stratification Method for the Minimization of the Masking Effect in Adverse Drug Reaction Signal Detection

by Jianxiang Wei, Lu Cheng, Pu Han, Yunxia Zhu and Weidong Huang

Appl. Sci. 2021, 11(23), 11380; https://doi.org/10.3390/app112311380 - 01 Dec 2021

Cited by 2 | Viewed by 1834

Abstract

Data masking is an inborn defect of measures of disproportionality in adverse drug reactions signal detection. Some improved methods which used gender and age for data stratification only considered the patient-related confounding factors, ignoring the drug-related influencing factors. Due to a large number of reports and the high proportion of antibiotics in the Chinese spontaneous reporting database, this paper proposes a decision tree-stratification method for the minimization of the masking effect by integrating the relevant factors of patients and drugs. The adverse drug reaction monitoring reports of Jiangsu Province in China from 2011 to 2018 were selected for this study. First, the age division interval was determined based on the statistical analysis of antibiotic-related data. Secondly, correlation analysis was conducted based on the patient’s gender and age respectively with the drug category attributes. Thirdly, the decision tree based on age and gender was constructed by the J48 algorithm, which was used to determine if drugs belonged to antibiotics as a classification label. Fourthly, some performance evaluation indicators were constructed based on the data of drug package inserts as a standard signal library: recall, precision, and F (the arithmetic harmonic mean of recall and precision). Finally, four experiments were carried out by means of the proportional reporting ratio method: non-stratification (total data), gender-stratification, age-stratification and decision tree-stratification, and the performance of the signal detection results was compared. The experimental results showed that the decision tree-stratification was superior to the other three methods. Therefore, the data-masking effect can be further minimized by comprehensively considering the patient and drug-related confounding factors. Full article

(This article belongs to the Special Issue Advanced Decision Making in Clinical Medicine)

► Show Figures

Figure 1

13 pages, 406 KiB

Open AccessArticle

Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals

by Jianxiang Wei, Jimin Dai, Yingya Zhao, Pu Han, Yunxia Zhu and Weidong Huang

Appl. Sci. 2021, 11(22), 10828; https://doi.org/10.3390/app112210828 - 16 Nov 2021

Cited by 1 | Viewed by 1561

Abstract

Adverse drug reactions (ADRs) are increasingly becoming a serious public health problem. Spontaneous reporting systems (SRSs) are an important way for many countries to monitor ADRs produced in the clinical use of drugs, and they are the main data source for ADR signal detection. The traditional signal detection methods are based on disproportionality analysis (DPA) and lack the application of data mining technology. In this paper, we selected the spontaneous reports from 2011 to 2018 in Jiangsu Province of China as the research data and used association rules analysis (ARA) to mine signals. We defined some important metrics of the ARA according to the two-dimensional contingency table of ADRs, such as Confidence and Lift, and constructed performance evaluation indicators such as Precision, Recall, and F1 as objective standards. We used experimental methods based on data to objectively determine the optimal thresholds of the corresponding metrics, which, in the best case, are Confidence = 0.007 and Lift = 1. We obtained the average performance of the method through 10-fold cross-validation. The experimental results showed that F1 increased from 31.43% in the MHRA method to 40.38% in the ARA method; this was a significant improvement. To reduce drug risk and provide decision making for drug safety, more data mining methods need to be introduced and applied to ADR signal detection. Full article

(This article belongs to the Special Issue Advanced Decision Making in Clinical Medicine)

► Show Figures

Figure 1

Review

Jump to: Research

18 pages, 723 KiB

Open AccessReview

Molecular Techniques and Target Selection for the Identification of Candida spp. in Oral Samples

by Joana Magalhães, Maria José Correia, Raquel M. Silva, Ana Cristina Esteves, Artur Alves and Ana Sofia Duarte

Appl. Sci. 2022, 12(18), 9204; https://doi.org/10.3390/app12189204 - 14 Sep 2022

Cited by 4 | Viewed by 2615

Abstract

Candida species are the causative agent of oral candidiasis, with medical devices being platforms for yeast anchoring and tissue colonization. Identifying the infectious agent involved in candidiasis avoids an empirical prescription of antifungal drugs. The application of high-throughput technologies to the diagnosis of yeast pathogens has clear advantages in sensitivity, accuracy, and speed. Yet, conventional techniques for the identification of Candida isolates are still routine in clinical and research settings. Molecular approaches are the focus of intensive research, but conversion into clinic settings requires overcoming important challenges. Several molecular approaches can accurately identify Candida spp.: Polymerase Chain Reaction, Microarray, High-Resolution Melting Analysis, Multi-Locus Sequence Typing, Restriction Fragment Length Polymorphism, Loop-mediated Isothermal Amplification, Matrix Assisted Laser Desorption Ionization-mass spectrometry, and Next Generation Sequencing. This review examines the advantages and disadvantages of the current molecular methods used for Candida spp. Identification, with a special focus on oral candidiasis. Discussion regarding their application for the diagnosis of oral infections aims to identify the most rapid, affordable, accurate, and easy-to-perform molecular techniques to be used as a point-of-care testing method. Special emphasis is given to the difficulties that health care professionals need to overcome to provide an accurate diagnosis. Full article

(This article belongs to the Special Issue Advanced Decision Making in Clinical Medicine)

► Show Figures

Journal Menu

Journal Browser

Advanced Decision Making in Clinical Medicine

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (8 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI