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Artificial Intelligence Algorithms for Healthcare

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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 57439

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Special Issue Editors

Dr. Dmytro Chumachenko

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Guest Editor

Mathematical Modelling and Artificial Intelligence Department, National Aerospace University “Kharkiv Aviation Institute”, 61070 Kharkiv, Ukraine
Interests: agent-based simulation; artificial intelligence; multiagent simulation; machine learning; infectious diseases simulation; data-driven medicine
Special Issues, Collections and Topics in MDPI journals

Dr. Sergiy Yakovlev

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Co-Guest Editor

Institute of Information Technology, Lodz University of Technology, 90-924 Lodz, Poland
Interests: mathematical modeling; optimization of complex systems; combinatorial optimization; packing and covering problems; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the area of the development of data-driven healthcare to this Special Issue “Artificial Intelligence Algorithms for Healthcare”. The use of artificial intelligence methods and tools in healthcare is an important modern trend in science. Artificial intelligence technologies are changing the global healthcare system, allowing to improve the system of medical diagnostics, drug development, medical data analysis, and medical decision making, as well as improve the quality of healthcare services while reducing costs for medical institutions.

This Special Issue will include original theoretical and empirical studies and reviews in the field of machine learning, data mining, modeling, information technologies, and artificial intelligence in the context of healthcare. The purpose of this special issue is to educate the community about unique and new research related to the application of artificial intelligence methods and tools to various aspects of healthcare and medical research.

Dr. Dmytro Chumachenko
Prof. Dr. Sergiy Yakovlev
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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.

Keywords

digital health
digital therapeutics
digital twin
remote healthcare monitoring
personalized healthcare
personalized medicine
smart healthcare
medical imaging
telemedicine
medical data analysis
eHealth
smart health applications
remote health surveillance
data-driven monitoring
data-driven surveillance
health informatics
public health informatics
bioinformatics
computer-aided drug design
clinical data science
machine learning
big data
deep learning
artificial intelligence
agent-based simulation
multiagent system

Published Papers (10 papers)

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Editorial

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6 pages, 174 KiB

Open AccessEditorial

Artificial Intelligence Algorithms for Healthcare

by Dmytro Chumachenko and Sergiy Yakovlev

Algorithms 2024, 17(3), 105; https://doi.org/10.3390/a17030105 - 28 Feb 2024

Viewed by 1063

Abstract

In an era where technological advancements are rapidly transforming industries, healthcare is the primary beneficiary of such progress [...] Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

Research

Jump to: Editorial

28 pages, 2207 KiB

Open AccessArticle

Assessing the Impact of Patient Characteristics on Genetic Clinical Pathways: A Regression Approach

by Stefano Alderighi, Paolo Landa, Elena Tànfani and Angela Testi

Algorithms 2024, 17(2), 75; https://doi.org/10.3390/a17020075 - 07 Feb 2024

Viewed by 1004

Abstract

Molecular genetic techniques allow for the diagnosing of hereditary diseases and congenital abnormalities prenatally. A high variability of treatments exists, engendering an inappropriate clinical response, an inefficient use of resources, and the violation of the principle of the equality of treatment for equal needs. The proposed framework is based on modeling clinical pathways that contribute to identifying major causes of variability in treatments justified by the clinical needs’ variability as well as depending on individual characteristics. An electronic data collection method for high-risk pregnant women addressing genetic facilities and laboratories was implemented. The collected data were analyzed retrospectively with two aims. The first is to identify how the whole activity of genetic services can be broken down into different clinical pathways. This was performed by building a flow chart with the help of doctors. The second aim consists of measuring the variability, within and among, the different paths due to individual characteristics. A set of statistical models was developed to determine the impact of the patient characteristics on the clinical pathway and its length. The results show the importance of considering these characteristics together with the clinical information to define the care pathway and the use of resources. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

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17 pages, 3311 KiB

Open AccessArticle

A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis

by A. Khuzaim Alzahrani, Ahmed A. Alsheikhy, Tawfeeq Shawly, Ahmed Azzahrani and Yahia Said

Algorithms 2023, 16(12), 556; https://doi.org/10.3390/a16120556 - 05 Dec 2023

Viewed by 1513

Abstract

Blood cancer occurs due to changes in white blood cells (WBCs). These changes are known as leukemia. Leukemia occurs mostly in children and affects their tissues or plasma. However, it could occur in adults. This disease becomes fatal and causes death if it is discovered and diagnosed late. In addition, leukemia can occur from genetic mutations. Therefore, there is a need to detect it early to save a patient’s life. Recently, researchers have developed various methods to detect leukemia using different technologies. Deep learning approaches (DLAs) have been widely utilized because of their high accuracy. However, some of these methods are time-consuming and costly. Thus, a need for a practical solution with low cost and higher accuracy is required. This article proposes a novel segmentation and classification framework model to discover and categorize leukemia using a deep learning structure. The proposed system encompasses two main parts, which are a deep learning technology to perform segmentation and characteristic extraction and classification on the segmented section. A new UNET architecture is developed to provide the segmentation and feature extraction processes. Various experiments were performed on four datasets to evaluate the model using numerous performance factors, including precision, recall, F-score, and Dice Similarity Coefficient (DSC). It achieved an average 97.82% accuracy for segmentation and categorization. In addition, 98.64% was achieved for F-score. The obtained results indicate that the presented method is a powerful technique for discovering leukemia and categorizing it into suitable groups. Furthermore, the model outperforms some of the implemented methods. The proposed system can assist healthcare providers in their services. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

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20 pages, 7880 KiB

Open AccessArticle

eNightTrack: Restraint-Free Depth-Camera-Based Surveillance and Alarm System for Fall Prevention Using Deep Learning Tracking

by Ye-Jiao Mao, Andy Yiu-Chau Tam, Queenie Tsung-Kwan Shea, Yong-Ping Zheng and James Chung-Wai Cheung

Algorithms 2023, 16(10), 477; https://doi.org/10.3390/a16100477 - 12 Oct 2023

Cited by 1 | Viewed by 1785

Abstract

Falls are a major problem in hospitals, and physical or chemical restraints are commonly used to “protect” patients in hospitals and service users in hostels, especially elderly patients with dementia. However, physical and chemical restraints may be unethical, detrimental to mental health and associated with negative side effects. Building upon our previous development of the wandering behavior monitoring system “eNightLog”, we aimed to develop a non-contract restraint-free multi-depth camera system, “eNightTrack”, by incorporating a deep learning tracking algorithm to identify and notify about fall risks. Our system evaluated 20 scenarios, with a total of 307 video fragments, and consisted of four steps: data preparation, instance segmentation with customized YOLOv8 model, head tracking with MOT (Multi-Object Tracking) techniques, and alarm identification. Our system demonstrated a sensitivity of 96.8% with 5 missed warnings out of 154 cases. The eNightTrack system was robust to the interference of medical staff conducting clinical care in the region, as well as different bed heights. Future research should take in more information to improve accuracy while ensuring lower computational costs to enable real-time applications. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

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25 pages, 1896 KiB

Open AccessArticle

Time-Efficient Identification Procedure for Neurological Complications of Rescue Patients in an Emergency Scenario Using Hardware-Accelerated Artificial Intelligence Models

by Abu Shad Ahammed, Aniebiet Micheal Ezekiel and Roman Obermaisser

Algorithms 2023, 16(5), 258; https://doi.org/10.3390/a16050258 - 18 May 2023

Cited by 1 | Viewed by 1701

Abstract

During an emergency rescue operation, rescuers have to deal with many different health complications like cardiovascular, respiratory, neurological, psychiatric, etc. The identification process of the common health complications in rescue events is not very difficult or time-consuming because the health vital symptoms or primary observations are enough to identify, but it is quite difficult with some complications related to neurology e.g., schizophrenia, epilepsy with non-motor seizures, or retrograde amnesia because they cannot be identified with the trend of health vital data. The symptoms have a wide spectrum and are often non-distinguishable from other types of complications. Further, waiting for results from medical tests like MRI and ECG is time-consuming and not suitable for emergency cases where a quick treatment path is an obvious necessity after the diagnosis. In this paper, we present a novel solution for overcoming these challenges by employing artificial intelligence (AI) models in the diagnostic procedure of neurological complications in rescue situations. The novelty lies in the procedure of generating input features from raw rescue data used in AI models, as the data are not like traditional clinical data collected from hospital repositories. Rather, the data were gathered directly from more than 200,000 rescue cases and required natural language processing techniques to extract meaningful information. A step-by-step analysis of developing multiple AI models that can facilitate the fast identification of neurological complications, in general, is presented in this paper. Advanced data analytics are used to analyze the complete record of 273,183 rescue events in a duration of almost 10 years, including rescuers’ analysis of the complications and their diagnostic methods. To develop the detection model, seven different machine learning algorithms-Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbor (KNN), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were used. Observing the model’s performance, we conclude that the neural network and extreme gradient boosting show the best performance in terms of selected evaluation criteria. To utilize this result in practical scenarios, the paper also depicts the possibility of embedding such machine learning models in hardware like FPGA. The goal is to achieve fast detection results, which is a primary requirement in any rescue mission. An inference time analysis of the selected ML models and VTA AI accelerator of Apache-TVM machine learning compiler used for the FPGA is also presented in this research. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

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20 pages, 3697 KiB

Open AccessArticle

An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management

by Jong-Shin Chen, Chun-Ming Kuo and Ruo-Wei Hung

Algorithms 2023, 16(3), 132; https://doi.org/10.3390/a16030132 - 01 Mar 2023

Cited by 1 | Viewed by 1005

Abstract

Many highly contagious infectious diseases, such as COVID-19, monkeypox, chickenpox, influenza, etc., have seriously affected or currently are seriously affecting human health, economic activities, education, sports, and leisure. Many people will be infected or quarantined when an epidemic spreads in specific areas. These people whose activities must be restricted due to the epidemic are represented by targets in the article. Managing targets by using targeted areas is an effective option for slowing the spread. The Centers for Disease Control (CDC) usually determine management strategies by tracking targets in specific areas. A global navigation satellite system (GNSS) that can provide autonomous geospatial positioning of targets by using tiny electronic receivers can assist in recognition. The recognition of targets within a targeted area is a point-in-polygon (PtInPy) problem in computational geometry. Most previous methods used the method of identifying one target at a time, which made them unable to effectively deal with many targets. An earlier method was able to simultaneously recognize several targets but had the problem of the repeated recognition of the same targets. Therefore, we propose a GNSS coordinate recognition algorithm. This algorithm can efficiently recognize a large number of targets within a targeted area, which can provide assistance in epidemic management. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

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22 pages, 710 KiB

Open AccessArticle

Towards a Flexible Assessment of Compliance with Clinical Protocols Using Fuzzy Aggregation Techniques

by Anna Wilbik, Irene Vanderfeesten, Dennis Bergmans, Serge Heines, Oktay Turetken and Walther van Mook

Algorithms 2023, 16(2), 109; https://doi.org/10.3390/a16020109 - 13 Feb 2023

Cited by 1 | Viewed by 1881

Abstract

In healthcare settings, compliance with clinical protocols and medical guidelines is important to ensure high-quality, safe and effective treatment of patients. How to measure compliance and how to represent compliance information in an interpretable and actionable way is still an open challenge. In this paper, we propose new metrics for compliance assessments. For this purpose, we use two fuzzy aggregation techniques, namely the OWA operator and the Sugeno integral. The proposed measures take into consideration three factors: (i) the degree of compliance with a single activity, (ii) the degree of compliance of a patient, and (iii) the importance of the activities. The proposed measures are applied to two clinical protocols used in practice. We demonstrate that the proposed measures for compliance can further aid clinicians in assessing the aspect of protocol compliance when evaluating the effectiveness of implemented clinical protocols. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

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14 pages, 2116 KiB

Open AccessEditor’s ChoiceArticle

Effective Heart Disease Prediction Using Machine Learning Techniques

by Chintan M. Bhatt, Parth Patel, Tarang Ghetia and Pier Luigi Mazzeo

Algorithms 2023, 16(2), 88; https://doi.org/10.3390/a16020088 - 06 Feb 2023

Cited by 55 | Viewed by 38638

Abstract

The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. This research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This paper proposes a method of k-modes clustering with Huang starting that can improve classification accuracy. Models such as random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), and XGBoost (XGB) are used. GridSearchCV was used to hypertune the parameters of the applied model to optimize the result. The proposed model is applied to a real-world dataset of 70,000 instances from Kaggle. Models were trained on data that were split in 80:20 and achieved accuracy as follows: decision tree: 86.37% (with cross-validation) and 86.53% (without cross-validation), XGBoost: 86.87% (with cross-validation) and 87.02% (without cross-validation), random forest: 87.05% (with cross-validation) and 86.92% (without cross-validation), multilayer perceptron: 87.28% (with cross-validation) and 86.94% (without cross-validation). The proposed models have AUC (area under the curve) values: decision tree: 0.94, XGBoost: 0.95, random forest: 0.95, multilayer perceptron: 0.95. The conclusion drawn from this underlying research is that multilayer perceptron with cross-validation has outperformed all other algorithms in terms of accuracy. It achieved the highest accuracy of 87.28%. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

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17 pages, 3185 KiB

Open AccessArticle

Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform

by Olena Pavliuk, Myroslav Mishchuk and Christine Strauss

Algorithms 2023, 16(2), 77; https://doi.org/10.3390/a16020077 - 01 Feb 2023

Cited by 9 | Viewed by 2934

Abstract

Over the last few years, human activity recognition (HAR) has drawn increasing interest from the scientific community. This attention is mainly attributable to the proliferation of wearable sensors and the expanding role of HAR in such fields as healthcare, sports, and human activity monitoring. Convolutional neural networks (CNN) are becoming a popular approach for addressing HAR problems. However, this method requires extensive training datasets to perform adequately on new data. This paper proposes a novel deep learning model pre-trained on scalograms generated using the continuous wavelet transform (CWT). Nine popular CNN architectures and different CWT configurations were considered to select the best performing combination, resulting in the training and evaluation of more than 300 deep learning models. On the source KU-HAR dataset, the selected model achieved classification accuracy and an F1 score of 97.48% and 97.52%, respectively, which outperformed contemporary state-of-the-art works where this dataset was employed. On the target UCI-HAPT dataset, the proposed model resulted in a maximum accuracy and F1-score increase of 0.21% and 0.33%, respectively, on the whole UCI-HAPT dataset and of 2.82% and 2.89%, respectively, on the UCI-HAPT subset. It was concluded that the usage of the proposed model, particularly with frozen layers, results in improved performance, faster training, and smoother gradient descent on small HAR datasets. However, the use of the pre-trained model on sufficiently large datasets may lead to negative transfer and accuracy degradation. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

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9 pages, 2553 KiB

Open AccessArticle

Pose-Based Gait Analysis for Diagnosis of Parkinson’s Disease

by Tee Connie, Timilehin B. Aderinola, Thian Song Ong, Michael Kah Ong Goh, Bayu Erfianto and Bedy Purnama

Algorithms 2022, 15(12), 474; https://doi.org/10.3390/a15120474 - 12 Dec 2022

Cited by 3 | Viewed by 3439

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder that is more common in elderly people and affects motor control, flexibility, and how easily patients adapt to their walking environments. PD is progressive in nature, and if undetected and untreated, the symptoms grow worse over time. Fortunately, PD can be detected early using gait features since the loss of motor control results in gait impairment. In general, techniques for capturing gait can be categorized as computer-vision-based or sensor-based. Sensor-based techniques are mostly used in clinical gait analysis and are regarded as the gold standard for PD detection. The main limitation of using sensor-based gait capture is the associated high cost and the technical expertise required for setup. In addition, the subjects’ consciousness of worn sensors and being actively monitored may further impact their motor function. Recent advances in computer vision have enabled the tracking of body parts in videos in a markerless motion capture scenario via human pose estimation (HPE). Although markerless motion capture has been studied in comparison with gold-standard motion-capture techniques, it is yet to be evaluated in the prediction of neurological conditions such as PD. Hence, in this study, we extract PD-discriminative gait features from raw videos of subjects and demonstrate the potential of markerless motion capture for PD prediction. First, we perform HPE on the subjects using AlphaPose. Then, we extract and analyse eight features, from which five features are systematically selected, achieving up to 93% accuracy, 96% precision, and 92% recall in arbitrary views. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

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