Special Issue "Computing and Artificial Intelligence Techniques for Healthcare Applications"

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (1 February 2021).

Special Issue Editor

Dr. Ahmed Farouk
Website
Guest Editor
Department of Physics and Computer Science, Faculty of Science, Wilfrid Laurier University, Waterloo, Canada
Interests: quantum information and computation; information security and privacy
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid growth of biological data in the recent years, data-driven computational methods are increasingly needed to quickly and accurately analyze large-scale biological data. Especially, biological and medical technologies have been providing us with explosive volumes of biological and physiological data, such as medical images, electroencephalography signals, genomic and protein sequences. Learning from these data will facilitate our understanding of human health and disease. Accordingly, computation and machine learning techniques have recently emerged in both academia and industry as “intelligent” methods in many specific healthcare areas to gain insight from medical and biological data. To expand the scope and ease of the applicability of machine learning, it is highly desirable to make learning algorithms less dependent on handcrafted feature engineering, so that novel applications can be constructed faster and, more importantly, progress toward artificial intelligence (AI) can be made.

This Special Issue aims to target recent computation and machine learning techniques as well as some of the state-of-the-art applications in the healthcare areas such as bioinformatics, bioprocess systems, biomedical systems, biomedical physics, and bioecological system. This Special Issue will consider original research articles and review articles on computational and intelligent methods in healthcare and their applications. We wish to gather relevant contributions introducing new techniques for the study of complex healthcare systems driven by computational methods. Papers on interdisciplinary applications are particularly welcome. We also encourage authors to make their codes and experimental data available to the public, so that our Special Issue can be more infusive and attractive.

Dr. Ahmed Farouk
Guest Editor

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 papers will be 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. Healthcare 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

  • Quantum Machine Learning
  • Supervised learning algorithms
  • Unsupervised learning algorithms
  • Imbalanced learning algorithms
  • Multi-view feature learning
  • Deep learning-based feature learning strategies
  • Feature representation optimization algorithms
  • Handcrafted feature representation algorithms
  • Computational and Mathematical Techniques
  • Image and signal processing
  • Bioinformatics
  • Mental Health
  • Bioprocess systems
  • Biomedical systems
  • Biomedical physics
  • Bioecological system

Published Papers (6 papers)

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Research

Open AccessArticle
ECG Enhancement and R-Peak Detection Based on Window Variability
Healthcare 2021, 9(2), 227; https://doi.org/10.3390/healthcare9020227 - 18 Feb 2021
Viewed by 176
Abstract
In ECG applications, the correct recognition of R-peaks is extremely important for detecting abnormalities, such as arrhythmia and ventricular hypertrophy. In this work, a novel ECG enhancement and R-peak detection method based on window variability is presented, and abbreviated as SQRS. Firstly, the [...] Read more.
In ECG applications, the correct recognition of R-peaks is extremely important for detecting abnormalities, such as arrhythmia and ventricular hypertrophy. In this work, a novel ECG enhancement and R-peak detection method based on window variability is presented, and abbreviated as SQRS. Firstly, the ECG signal corrupted by various high or low-frequency noises is denoised by moving-average filtering. Secondly, the window variance transform technique is used to enhance the QRS complex and suppress the other components in the ECG, such as P/T waves and noise. Finally, the signal, converted by window variance transform, is applied to generate the R-peaks candidates, and the decision rules, including amplitude and kurtosis adaptive thresholds, are applied to determine the R-peaks. A special squared window variance transform (SWVT) is proposed to measure the signal variability in a certain time window, and this technique reduces false detection rate caused by the various types of interference presented in ECG signals. For the MIT-BIH arrhythmia database, the sensitivity of R-peak detection can reach 99.6% using the proposed method. Full article
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Open AccessArticle
A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram
Healthcare 2021, 9(2), 169; https://doi.org/10.3390/healthcare9020169 - 05 Feb 2021
Viewed by 305
Abstract
The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation [...] Read more.
The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort. Full article
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Open AccessArticle
Computer Tomography in the Diagnosis of Ovarian Cysts: The Role of Fluid Attenuation Values
Healthcare 2020, 8(4), 398; https://doi.org/10.3390/healthcare8040398 - 14 Oct 2020
Viewed by 432
Abstract
Pathological analysis of ovarian cysts shows specific fluid characteristics that cannot be standardly evaluated on computer tomography (CT) examinations. This study aimed to assess the ovarian cysts’ fluid attenuation values on the native (Np), arterial (Ap), and venous (Vp) contrast phases of seventy [...] Read more.
Pathological analysis of ovarian cysts shows specific fluid characteristics that cannot be standardly evaluated on computer tomography (CT) examinations. This study aimed to assess the ovarian cysts’ fluid attenuation values on the native (Np), arterial (Ap), and venous (Vp) contrast phases of seventy patients with ovarian cysts who underwent CT examinations and were retrospectively included in this study. Patients were divided according to their final diagnosis into the benign group (n = 32) and malignant group (n = 38; of which 27 were primary and 11 were secondary lesions). Two radiologists measured the fluid attenuation values on each contrast phase, and the average values were used to discriminate between benign and malignant groups and primary tumors and metastases via univariate, multivariate, multiple regression, and receiver operating characteristics analyses. The Ap densities (p = 0.0002) were independently associated with malignant cysts. Based on the densities measured on all three phases, neoplastic lesions could be diagnosed with 89.47% sensitivity and 62.5% specificity. The Np densities (p = 0.0005) were able to identify metastases with 90.91% sensitivity and 70.37% specificity, while the combined densities of all three phases diagnosed secondary lesions with 72.73% sensitivity and 92.59% specificity. The ovarian cysts’ fluid densities could function as an adjuvant criterion to the classic CT evaluation of ovarian cysts. Full article
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Open AccessArticle
Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method
Healthcare 2020, 8(4), 392; https://doi.org/10.3390/healthcare8040392 - 10 Oct 2020
Viewed by 506
Abstract
This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of possible statistical [...] Read more.
This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of possible statistical weighting schemes to combine with each element of WE vectors, which were term frequency (TF), inverse document frequency (IDF) and class probability (CP) methods. Thus, we built a multiclass classification model using a bidirectional long short-term memory (BLSTM) with deep neural networks for all investigated operations of feature vector combinations. We used MIMIC III and the PubMed dataset for the developing language model. To evaluate the performance of our weighted feature representation approaches, we conducted a set of experiments for examining multiclass classification performance with the deep neural network model and other state-of-the-art machine learning (ML) approaches. In all experiments, we used the OHSUMED-400 dataset, which includes PubMed abstracts related with specifically one class over 23 cardiovascular disease categories. Afterwards, we presented the results obtained from experiments and provided a comparison with related research in the literature. The results of the experiment showed that our BLSTM model with the weighting techniques outperformed the baseline and other machine learning approaches in terms of validation accuracy. Finally, our model outperformed the scores of related studies in the literature. This study shows that weighted feature representation improves the performance of the multiclass classification. Full article
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Open AccessArticle
Consistency of Medical Data Using Intelligent Neuron Faster R-CNN Algorithm for Smart Health Care Application
Healthcare 2020, 8(2), 185; https://doi.org/10.3390/healthcare8020185 - 25 Jun 2020
Viewed by 1046
Abstract
The purpose of this study is to increase interest in health as human life is extended in modern society. Hence, many people in hospitals produce much medical data (EMR, PACS, OCS, EHR, MRI, X-ray) after treatment. Medical data are stored as structured and [...] Read more.
The purpose of this study is to increase interest in health as human life is extended in modern society. Hence, many people in hospitals produce much medical data (EMR, PACS, OCS, EHR, MRI, X-ray) after treatment. Medical data are stored as structured and unstructured data. However, many medical data are causing errors, omissions and mistakes in the process of reading. This behavior is very important in dealing with human life and sometimes leads to medical accidents due to physician errors. Therefore, this research is conducted through the CNN intelligent agent cloud architecture to verify errors in reading existing medical image data. To reduce the error rule when reading medical image data, a faster R-CNN intelligent agent cloud architecture is proposed. It shows the result of increasing errors of existing error reading by more than 1.4 times (140%). In particular, it is an algorithm that analyses data stored by actual existing medical data through Conv feature map using deep ConvNet and ROI Projection. The data were verified using about 120,000 databases. It uses data to examine human lungs. In addition, the experimental environment established an environment that can handle GPU’s high performance and NVIDIA SLI multi-OS and multiple Quadro GPUs were used. In this experiment, the verification data composition was verified and randomly extracted from about 120,000 medical records and the similarity compared to the original data were measured by comparing about 40% of the extracted images. Finally, we want to reduce and verify the error rate of medical data reading. Full article
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Open AccessArticle
Influencing Factors and Countermeasures of the Health of Residents in the City Clusters along the Middle Reaches of the Yangtze River
Healthcare 2020, 8(2), 93; https://doi.org/10.3390/healthcare8020093 - 10 Apr 2020
Cited by 1 | Viewed by 1057
Abstract
This paper introduces several factors, namely, environmental pollution, medical level and environmental governance, into the Grossman’s production function for health. Then, an empirical analysis was conducted based on the 2004–2016 panel data of the city clusters along the middle reaches of the Yangtze [...] Read more.
This paper introduces several factors, namely, environmental pollution, medical level and environmental governance, into the Grossman’s production function for health. Then, an empirical analysis was conducted based on the 2004–2016 panel data of the city clusters along the middle reaches of the Yangtze River. Through the analysis, the author evaluated and compared how different factors affect the health of residents in the three city clusters: Changsha-Zhuzhou-Xiangtan (CZT) city cluster, Wuhan city cluster and circum-Poyang Lake (CPL) city cluster. The results show that: (1) In all three city clusters, economic growth can effectively improve the health of residents, and environmental pollution is also a key influencing factor of the health of residents. (2) Medical level has a close correlation with the health of residents. In the CZT city cluster, the medical level is positively correlated with the health of residents; in the CPL city cluster, the correlation is negative and takes the shape of an inverted U in the long run. (3) In all three city clusters, the environmental governance has an inverted U-shape correlation with the health of residents, indicating that environmental governance is not enough to promote the health of residents. Finally, several countermeasures were put forward to enhance the health of residents in the study area. The research findings shed new light on policymaking for the health of residents. Full article
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