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Advanced Computational Models for Clinical Decision Support

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A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 37488

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Dr. Haishuai Wang

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Department of Computer Science and Engineering, Fairfield University, Fairfield, CT 06824, USA
Interests: data mining; health informatics; machine learning; clinical data science; healthcare

Prof. Dr. Chi-Hua Chen

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College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
Interests: healthcare systems; augmented reality; big data; deep learning; internet of things; data mining
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Dr. Lianhua Chi

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Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia
Interests: healthcare; big data; autism screening; medical AI; clinical decision support
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Dr. Jun Wu

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School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: machine learning; data mining; pattern recognition; health informatics

Dr. Shirui Pan

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Department of Data Science & AI, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
Interests: AI; data mining; deep learning; graph and network analysis; machine learning; healthcare
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Dr. Li Li

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Department of Genetics, Harvard University, Boston, MA 02115, USA
Interests: machine learning; network studies; clinical informatics; next generation

Special Issue Information

Dear Colleagues,

The importance of advanced computational modeling for clinical diagnosis is becoming increasingly recognized with the rise of e-health. In particular, given the outbreak of COVID-19, advanced computational models will play a significant role in understanding complex clinical data over the coming years. Computational models have a wide range of applications in clinical settings. For example, advanced computational models can be used to track infectious coronaviruses in populations, identify the most effective interventions, and make predictions regarding disease diagnosis from symptoms, which is critical for saving lives and reducing stress on the healthcare system, especially during infectious disease pandemics. Therefore, advanced computational models can be used to provide precise diagnoses and predictions for clinical decision support.

Although current research in this field has shown promising results, several open research questions remain that need to be addressed through further discussions and studies. Among the many questions include how to learn from high dimensionality when there is only a small amount of labeled biomedical data, how to effectively deal with multi-source and multi-modal biomedical data, how to improve predictive performance along with interpretation, how to share clinical data between hospitals while preserving privacy, and how to build end-to-end disease diagnostic platforms for clinical decision support.

The aim of this Special Issue is to showcase how novel computational methods can be applied to address the challenges of complex biomedical data. Special attention will be devoted to the handling of multi-source and multi-modal biomedical data, limited biomedical data, and multi-institutional biomedical data in terms of privacy preservation. This Special Issue aims to provide stronger technical support and stimulate an environment for the development of computational models and applications for clinical decision support.

Potential topics include but are not limited to the following:

Advanced computational models for disease prediction and prevention
Advanced computational models for small biomedical data
Advanced computational models for multi-source and multi-modal biomedical data
Advanced computational models for medical imaging
Secure biomedical data analysis with advanced computational methods
Machine learning methods applied to biomedical data
Advanced computational models for clinical decision support
End-to-end disease diagnostic platforms using advanced computational models
Advanced computational models with interpretation
Advanced computational models for imbalanced biomedical data
Computer-aided detection and diagnosis

Dr. Haishuai Wang
Prof. Dr. Chi-Hua Chen
Dr. Lianhua Chi
Dr. Jun Wu
Dr. Shirui Pan
Dr. Li Li
Guest Editors

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Keywords

advanced computational models
clinical diagnosis
e-health
COVID-19
biology
clinical decision support
biomedical data

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Published Papers (11 papers)

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Research

8 pages, 1032 KiB

Open AccessCommunication

COVID-19 in Italy: Is the Mortality Analysis a Way to Estimate How the Epidemic Lasts?

by Pietro M. Boselli and Jose M. Soriano

Biology 2023, 12(4), 584; https://doi.org/10.3390/biology12040584 - 11 Apr 2023

Cited by 2 | Viewed by 1451

Abstract

When an epidemic breaks out, many health, economic, social, and political problems arise that require a prompt and effective solution. It would be useful to obtain all information about the virus, including epidemiological ones, as soon as possible. In a previous study of our group, the analysis of the positive-alive was proposed to estimate the epidemic duration. It was stated that every epidemic ends when the number of positive-alive (=infected-healed-dead) glides toward zero. In fact, if with the contagion everyone can enter the epidemic phenomenon, only by healing or dying can they get out of it. In this work, a different biomathematical model is proposed. A necessary condition for the epidemic to be resolved is that the mortality reaches the asymptotic value, from there, remains stable. At that time, the number of positive-alive must also be close to zero. This model seems to allow us to interpret the entire development of the epidemic and highlight its phases. It is also more appropriate than the previous one, especially when the spread of the infection is so rapid that the increase in live positives is staggering. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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20 pages, 4509 KiB

Open AccessArticle

MFIDMA: A Multiple Information Integration Model for the Prediction of Drug–miRNA Associations

by Yong-Jian Guan, Chang-Qing Yu, Yan Qiao, Li-Ping Li, Zhu-Hong You, Zhong-Hao Ren, Yue-Chao Li and Jie Pan

Biology 2023, 12(1), 41; https://doi.org/10.3390/biology12010041 - 26 Dec 2022

Cited by 4 | Viewed by 2581

Abstract

Abnormal microRNA (miRNA) functions play significant roles in various pathological processes. Thus, predicting drug–miRNA associations (DMA) may hold great promise for identifying the potential targets of drugs. However, discovering the associations between drugs and miRNAs through wet experiments is time-consuming and laborious. Therefore, it is significant to develop computational prediction methods to improve the efficiency of identifying DMA on a large scale. In this paper, a multiple features integration model (MFIDMA) is proposed to predict drug–miRNA association. Specifically, we first formulated known DMA as a bipartite graph and utilized structural deep network embedding (SDNE) to learn the topological features from the graph. Second, the Word2vec algorithm was utilized to construct the attribute features of the miRNAs and drugs. Third, two kinds of features were entered into the convolution neural network (CNN) and deep neural network (DNN) to integrate features and predict potential target miRNAs for the drugs. To evaluate the MFIDMA model, it was implemented on three different datasets under a five-fold cross-validation and achieved average AUCs of 0.9407, 0.9444 and 0.8919. In addition, the MFIDMA model showed reliable results in the case studies of Verapamil and hsa-let-7c-5p, confirming that the proposed model can also predict DMA in real-world situations. The model was effective in analyzing the neighbors and topological features of the drug–miRNA network by SDNE. The experimental results indicated that the MFIDMA is an accurate and robust model for predicting potential DMA, which is significant for miRNA therapeutics research and drug discovery. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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17 pages, 3816 KiB

Open AccessArticle

SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes

by Chang-Qing Yu, Xin-Fei Wang, Li-Ping Li, Zhu-Hong You, Wen-Zhun Huang, Yue-Chao Li, Zhong-Hao Ren and Yong-Jian Guan

Biology 2022, 11(9), 1350; https://doi.org/10.3390/biology11091350 - 13 Sep 2022

Cited by 20 | Viewed by 2191

Abstract

Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convolutional neural networks, combines node similarity to form node information and then predicts associated nodes using GCN with a distributive contribution mechanism. The model can be used not only to predict the molecular level of circRNA–miRNA interactions but also to predict circRNA–cancer and circRNA–gene associations. The AUCs of circRNA—miRNA, circRNA–disease, and circRNA–gene associations in the five-fold cross-validation experiment of SGCNCMI is 89.42%, 84.18%, and 82.44%, respectively. SGCNCMI is one of the few models in this field and achieved the best results. In addition, in our case study, six of the top ten relationship pairs with the highest prediction scores were verified in PubMed. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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18 pages, 2919 KiB

Open AccessArticle

Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder

by Anwer Mustafa Hilal, Amani Abdulrahman Albraikan, Sami Dhahbi, Mohamed K. Nour, Abdullah Mohamed, Abdelwahed Motwakel, Abu Sarwar Zamani and Mohammed Rizwanullah

Biology 2022, 11(8), 1220; https://doi.org/10.3390/biology11081220 - 15 Aug 2022

Cited by 20 | Viewed by 2666

Abstract

Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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14 pages, 3831 KiB

Open AccessArticle

Hybrid Deep Learning Approach for Automatic Detection in Musculoskeletal Radiographs

by Gurpreet Singh, Darpan Anand, Woong Cho, Gyanendra Prasad Joshi and Kwang Chul Son

Biology 2022, 11(5), 665; https://doi.org/10.3390/biology11050665 - 26 Apr 2022

Cited by 5 | Viewed by 3087

Abstract

The practice of Deep Convolution neural networks in the field of medicine has congregated immense success and significance in present situations. Previously, researchers have developed numerous models for detecting abnormalities in musculoskeletal radiographs of upper extremities, but did not succeed in achieving respectable accuracy in the case of finger radiographs. A novel deep neural network-based hybrid architecture named ComDNet-512 is proposed in this paper to efficiently detect the bone abnormalities in the musculoskeletal radiograph of a patient. ComDNet-512 comprises a three-phase pipeline structure: compression, training of the dense neural network, and progressive resizing. The ComDNet-512 hybrid model is trained with finger radiographs samples to make a binary prediction, i.e., normal or abnormal bones. The proposed model showed phenomenon outcomes when cross-validated on the testing samples of arthritis patients and gives many superior results when compared with state-of-the-art practices. The model is able to achieve an area under the ROC curve (AUC) equal to 0.894 (sensitivity = 0.941 and specificity = 0.847). The Precision, Recall, F1 Score, and Kappa values, recorded as 0.86, 0.94, 0.89, and 0.78, respectively, are better than any of the previous models’. With an increasing appearance of enormous cases of musculoskeletal conditions in people, deep learning-based computational solutions can play a big role in performing automated detections in the future. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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13 pages, 1722 KiB

Open AccessArticle

Determining Carina and Clavicular Distance-Dependent Positioning of Endotracheal Tube in Critically Ill Patients: An Artificial Intelligence-Based Approach

by Lung-Wen Tsai, Kuo-Ching Yuan, Sen-Kuang Hou, Wei-Lin Wu, Chen-Hao Hsu, Tyng-Luh Liu, Kuang-Min Lee, Chiao-Hsuan Li, Hann-Chyun Chen, Ethan Tu, Rajni Dubey, Chun-Fu Yeh and Ray-Jade Chen

Biology 2022, 11(4), 490; https://doi.org/10.3390/biology11040490 - 23 Mar 2022

Cited by 2 | Viewed by 5088

Abstract

Early and accurate prediction of endotracheal tube (ETT) location is pivotal for critically ill patients. Automatic and timely detection of faulty ETT locations from chest X-ray images may avert patients’ morbidity and mortality. Therefore, we designed convolutional neural network (CNN)-based algorithms to evaluate ETT position appropriateness relative to four detected key points, including tracheal tube end, carina, and left/right clavicular heads on chest radiographs. We estimated distances from the tube end to tracheal carina and the midpoint of clavicular heads. A DenseNet121 encoder transformed images into embedding features, and a CNN-based decoder generated the probability distributions. Based on four sets of tube-to-carina distance-dependent parameters (i.e., (i) 30–70 mm, (ii) 30–60 mm, (iii) 20–60 mm, and (iv) 20–55 mm), corresponding models were generated, and their accuracy was evaluated through the predicted L1 distance to ground-truth coordinates. Based on tube-to-carina and tube-to-clavicle distances, the highest sensitivity, and specificity of 92.85% and 84.62% respectively, were revealed for 20–55 mm. This implies that tube-to-carina distance between 20 and 55 mm is optimal for an AI-based key point appropriateness detection system and is empirically comparable to physicians’ consensus. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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17 pages, 3180 KiB

Open AccessArticle

Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images

by Mahmoud Ragab, Ashwag Albukhari, Jaber Alyami and Romany F. Mansour

Biology 2022, 11(3), 439; https://doi.org/10.3390/biology11030439 - 14 Mar 2022

Cited by 91 | Viewed by 6342

Abstract

Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist’s experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur’s entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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22 pages, 2048 KiB

Open AccessArticle

PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences

by Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Yijing Zhou and Murray Patterson

Biology 2022, 11(3), 418; https://doi.org/10.3390/biology11030418 - 9 Mar 2022

Cited by 30 | Viewed by 4208

Abstract

The study of host specificity has important connections to the question about the origin of SARS-CoV-2 in humans which led to the COVID-19 pandemic—an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona)viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating, and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is important in determining host specificity, since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among birds, bats, camels, swine, humans, and weasels, to name a few. We propose a feature embedding based on the well-known position weight matrix (PWM), which we call PWM2Vec, and we use it to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications, such as determining protein function and identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs from viral sequences to generate fixed-length feature vector representations, and use them in the context of host classification. The results on real world data show that when using PWM2Vec, machine learning classifiers are able to perform comparably to the baseline models in terms of predictive performance and runtime—in some cases, the performance is better. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the embeddings of the baseline models. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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13 pages, 1163 KiB

Open AccessArticle

Agent-Based Modeling of Autosomal Recessive Deafness 1A (DFNB1A) Prevalence with Regard to Intensity of Selection Pressure in Isolated Human Population

by Georgii P. Romanov, Anna A. Smirnova, Vladimir I. Zamyatin, Aleksey M. Mukhin, Fedor V. Kazantsev, Vera G. Pshennikova, Fedor M. Teryutin, Aisen V. Solovyev, Sardana A. Fedorova, Olga L. Posukh, Sergey A. Lashin and Nikolay A. Barashkov

Biology 2022, 11(2), 257; https://doi.org/10.3390/biology11020257 - 7 Feb 2022

Cited by 2 | Viewed by 2367

Abstract

An increase in the prevalence of autosomal recessive deafness 1A (DFNB1A) in populations of European descent was shown to be promoted by assortative marriages among deaf people. Assortative marriages became possible with the widespread introduction of sign language, resulting in increased genetic fitness of deaf individuals and, thereby, relaxing selection against deafness. However, the effect of this phenomenon was not previously studied in populations with different genetic structures. We developed an agent-based computer model for the analysis of the spread of DFNB1A. Using this model, we tested the impact of different intensities of selection pressure against deafness in an isolated human population over 400 years. Modeling of the “purifying” selection pressure on deafness (“No deaf mating” scenario) resulted in a decrease in the proportion of deaf individuals and the pathogenic allele frequency. Modeling of the “relaxed” selection (“Assortative mating” scenario) resulted in an increase in the proportion of deaf individuals in the first four generations, which then quickly plateaued with a subsequent decline and a decrease in the pathogenic allele frequency. The results of neutral selection pressure modeling (“Random mating” scenario) showed no significant changes in the proportion of deaf individuals or the pathogenic allele frequency after 400 years. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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18 pages, 2861 KiB

Open AccessArticle

Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images

by Mahmoud Ragab, Khalid Eljaaly, Nabil A. Alhakamy, Hani A. Alhadrami, Adel A. Bahaddad, Sayed M. Abo-Dahab and Eied M. Khalil

Biology 2022, 11(1), 43; https://doi.org/10.3390/biology11010043 - 29 Dec 2021

Cited by 16 | Viewed by 2483

Abstract

Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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16 pages, 325 KiB

Open AccessArticle

Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm

by Xingsi Xue, Pei-Wei Tsai and Yucheng Zhuang

Biology 2021, 10(12), 1287; https://doi.org/10.3390/biology10121287 - 7 Dec 2021

Cited by 8 | Viewed by 2418

Abstract

To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment’s quality, which calculate the alignment’s statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm’s convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s biomedical tracks to test aMMOEA’s performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI’s participants show that aMMOEA is able to effectively determine diverse solutions for decision makers. Full article

(This article belongs to the Special Issue Advanced Computational Models for Clinical Decision Support)

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