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Healthcare
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Clinical Informatics and Data Analysis in Healthcare
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Clinical Informatics and Data Analysis in Healthcare

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 10077

Special Issue Editor

Prof. Dr. Aleksander Owczarek

E-Mail Website
Guest Editor
Health Promotion and Obesity Management Unit, Department of Pathophysiology, Medical University of Silesia, 40-055 Katowice, Poland
Interests: biostatistics; epidemiology; public health; cardiology; chronic inflammation

Special Issue Information

Dear Colleagues,

Biomedical informatics, as well as biostatistical methods of data analysis, are of significant importance for scientific knowledge in healthcare. Such methods allow for the study of the variability of health phenomena including verification of hypotheses, forecasting, identification of health determinants, the study of different causal relationships, and the yield of clinically useful tools in health care management. In the proposed Special Issue of the journal, we are interested in the application of classical as well as new informatics technologies and statistical methods of biomedical data analysis, including artificial intelligence, machine learning, wearables devices, and signal processing. Both original research and review articles dealing with the application of mentioned methods to practical problems in healthcare are expected.

Prof. Dr. Aleksander Owczarek
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 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. Healthcare 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 2700 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

  • biomedical and clinical data analysis and management
  • biomedical informatics and technologies
  • biomedical signal and image processing
  • artificial intelligence
  • machine learning
  • wearables devices

Published Papers (7 papers)

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Research

16 pages, 513 KiB  
Article
Usage Patterns of Traditional Chinese Medicine for Patients with Bipolar Disorder: A Population-Based Study in Taiwan
by Shu-Ping Chen, Su-Tso Yang, Kai-Chieh Hu, Senthil Kumaran Satyanarayanan and Kuan-Pin Su
Healthcare 2024, 12(4), 490; https://doi.org/10.3390/healthcare12040490 - 18 Feb 2024
Viewed by 1150
Abstract
Background: Patients with bipolar disorder (BD) receive traditional Chinese medicine (TCM) for clinical needs unmet with psychotropic medications. However, the clinical characteristics of practices and outcomes of TCM in BD are not fully understood. This cohort study investigated the clinical characteristics, principal diagnoses, [...] Read more.
Background: Patients with bipolar disorder (BD) receive traditional Chinese medicine (TCM) for clinical needs unmet with psychotropic medications. However, the clinical characteristics of practices and outcomes of TCM in BD are not fully understood. This cohort study investigated the clinical characteristics, principal diagnoses, TCM interventions, and TCM prescriptions in patients with BD. Methods: Data for a total of 12,113 patients with BD between 1996 and 2013 were withdrawn from Taiwan’s longitudinal health insurance database 2000 (LHID 2000). The chi-square test was used for categorical variables, and the independent t-test was used for continuous variables. A p-value less than 0.05 indicated significance. Results: One thousand three hundred nineteen patients who visited TCM clinics after the diagnosis of BD were in the TCM group, while those who never visited TCM were in the non-TCM group (n = 1053). Compared to the non-TCM group, patients in the TCM group had younger average age, a higher percentage of female individuals, more comorbidities of anxiety and alcohol use disorders, and higher mood stabilizer usage rates. The TCM group exhibited pain-related indications, including joint pain, myalgia, myositis, headache, and sleep disturbances. Corydalis yanhusuo and Shu-Jing-Huo-Xue-Tang were the most useful single herbs and herbal formulae. Conclusions: Physicians need to be aware of the use of TCM in patients with BD. Full article
(This article belongs to the Special Issue Clinical Informatics and Data Analysis in Healthcare)
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11 pages, 858 KiB  
Article
Association of Vitamin B12 Levels with Sleep Quality, Insomnia, and Sleepiness in Adult Primary Healthcare Users in Greece
by Izolde Bouloukaki, Maria Lampou, Konstantina Maria Raouzaiou, Eirini Lambraki, Sophia Schiza and Ioanna Tsiligianni
Healthcare 2023, 11(23), 3026; https://doi.org/10.3390/healthcare11233026 - 23 Nov 2023
Viewed by 1053
Abstract
Despite vitamin B12’s recognized importance for the nervous system, there is still a lack of research on the association between vitamin B12 and sleep, especially in primary care settings. We assessed vitamin B12 levels in adult primary healthcare users and investigated correlations with [...] Read more.
Despite vitamin B12’s recognized importance for the nervous system, there is still a lack of research on the association between vitamin B12 and sleep, especially in primary care settings. We assessed vitamin B12 levels in adult primary healthcare users and investigated correlations with sleep quality, insomnia, and sleepiness. In this cross-sectional study, 512 consecutive participants were included. Information regarding anthropometrics, socio-demographics, and medical history was obtained. The Epworth Sleepiness Scale (ESS), Athens Insomnia Scale (AIS), and Pittsburg Sleep Quality Index (PSQI) were used to quantify excessive daytime sleepiness (EDS), insomnia symptoms, and sleep quality, respectively. The median vitamin B12 was 342 (266, 446) pg/mL. After adjustments, vitamin B12 levels < 342 pg/mL showed significant associations with insomnia symptoms [OR (95% CI) 2.434 (1.331–4.452), p = 0.004], especially in elderly, non-obese, and female participants, with EDS only in obese participants [OR (95% CI) 3.996, (1.006–15.876), p = 0.039]. Nonetheless, there was no significant association between B12 levels and poor sleep quality (OR 1.416, 95% CI 0.678–2.958, p = 0.354). In conclusion, our results show that lower vitamin B12 was associated with insomnia symptoms and sleepiness in specific groups of participants. However, further research with objective measurements of sleep is crucial to assess the relationship between sleep and vitamin B12. Full article
(This article belongs to the Special Issue Clinical Informatics and Data Analysis in Healthcare)
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9 pages, 1208 KiB  
Article
Effect of Neonatal Hearing Screening Results on the Lost to Follow-Up at the Diagnostic Level
by Grażyna Greczka, Piotr Dąbrowski, Monika Zych and Witold Szyfter
Healthcare 2023, 11(12), 1770; https://doi.org/10.3390/healthcare11121770 - 15 Jun 2023
Cited by 1 | Viewed by 724
Abstract
(1) Background: An important part of any neonatal hearing screening program is monitoring diagnostic visits to confirm or exclude the presence of hearing loss. In addition, time plays an important role in the diagnosis. We identified the number of children who came for [...] Read more.
(1) Background: An important part of any neonatal hearing screening program is monitoring diagnostic visits to confirm or exclude the presence of hearing loss. In addition, time plays an important role in the diagnosis. We identified the number of children who came for a diagnostic visit and analyzed the time of the first audiological visit, depending on the result of the hearing screening test performed in the first days of a child’s life and the presence or absence of risk factors of hearing impairment. (2) Methods: We analyzed 6,580,524 children, of which 8.9% required further diagnostics. The mean time of follow-up diagnostic visit in the analyzed group was 130 days and differed due to the presence or absence of risk factors for hearing loss before and after the neonatal period. (3) Results: Although the risk of hearing loss in children with risk factors is 2.31 to 6.38 times higher than in children without risk factors depending on the result of the screening test, more than 40% of parents do not report to scheduled audiological visits. (4) Conclusions: Doctors, nurses, and midwives who screen hearing at the neonatological level play an important role in educating parents about the possibility of hearing loss in a child and the need for an audiological examination. Full article
(This article belongs to the Special Issue Clinical Informatics and Data Analysis in Healthcare)
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16 pages, 3894 KiB  
Article
Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images
by Turki Althaqafi, Abdullah S. AL-Malaise AL-Ghamdi and Mahmoud Ragab
Healthcare 2023, 11(9), 1204; https://doi.org/10.3390/healthcare11091204 - 22 Apr 2023
Cited by 7 | Viewed by 1676
Abstract
Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology [...] Read more.
Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches. Full article
(This article belongs to the Special Issue Clinical Informatics and Data Analysis in Healthcare)
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24 pages, 2796 KiB  
Article
Longitudinal Data to Enhance Dynamic Stroke Risk Prediction
by Wenyao Zheng, Yun-Hsuan Chen and Mohamad Sawan
Healthcare 2022, 10(11), 2134; https://doi.org/10.3390/healthcare10112134 - 27 Oct 2022
Viewed by 1958
Abstract
Stroke risk prediction based on electronic health records is currently an important research topic. Previous research activities have generally used single-time physiological data to build static models and have focused on algorithms to improve prediction accuracy. Few studies have considered historical measurements from [...] Read more.
Stroke risk prediction based on electronic health records is currently an important research topic. Previous research activities have generally used single-time physiological data to build static models and have focused on algorithms to improve prediction accuracy. Few studies have considered historical measurements from a data perspective to construct dynamic models. Since it is a chronic disease, the risk of having a stroke increases and the corresponding risk factors become abnormal when healthy people are diagnosed with a stroke. Therefore, in this paper, we applied longitudinal data, with the backward joint model, to the Chinese Longitudinal Healthy Longevity and Happy Family Study’s dataset to monitor changes in individuals’ health status precisely on time and to increase the prediction accuracy of the model. The three-year prediction accuracy of our model, considering three measurements of longitudinal parameters, is 0.926. This is higher than the traditional Cox proportional hazard model, which has a 0.833 prediction accuracy. The results obtained in this study verified that longitudinal data improves stroke risk prediction accuracy and is promising for dynamic stroke risk prediction and prevention. Our model also verified that the frequency of fruit consumption, erythrocyte hematocrit, and glucose are potential stroke-related factors. Full article
(This article belongs to the Special Issue Clinical Informatics and Data Analysis in Healthcare)
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11 pages, 833 KiB  
Article
Analysis of Factors Hindering the Dissemination of Medical Information Standards
by Masami Mukai and Katsuhiko Ogasawara
Healthcare 2022, 10(7), 1248; https://doi.org/10.3390/healthcare10071248 - 04 Jul 2022
Cited by 2 | Viewed by 1412
Abstract
Many medical information standards are not widely used in Japan, and this hinders the promotion of the use of real-world data. However, the complex intertwining of many factors hindering the dissemination of medical information standards makes it difficult to solve this problem. This [...] Read more.
Many medical information standards are not widely used in Japan, and this hinders the promotion of the use of real-world data. However, the complex intertwining of many factors hindering the dissemination of medical information standards makes it difficult to solve this problem. This study analyzed and visualized relationships among factors that inhibit the dissemination of medical information standards. Five medical informatics experts affiliated with universities and hospitals were interviewed about the factors that hinder the dissemination of medical information standards in Japan. The presented factors were analyzed using the interpretive structural modeling (ISM) method and the decision-making trial and evaluation laboratory (DEMATEL) method. We found that “legislation” and “reliability” were important inhibiting factors for the dissemination of medical information standards in Japan. We also found a six-layered structure in which “reliability” was satisfied when “legislation” was in place and “expectations” and “personal information” were resolved. The DEMATEL analysis indicated the relationships and classifications of factors hindering the dissemination of medical information standards. Since the adoption of medical information standards does not directly lead to revenue for medical institutions, it is possible to meet the “expectation” of improving the quality of medical care by ensuring “legislation” and “reliability”, that is, ensuring the dependability of medical treatment. The results of this study visually show the structure of the factors and will help solve the problems that hinder the effective and efficient spread of standards. Solving these problems may support the efficient use of real-world data. Full article
(This article belongs to the Special Issue Clinical Informatics and Data Analysis in Healthcare)
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13 pages, 477 KiB  
Article
Factors Affecting Korean Medicine Health Care Use for Functional Dyspepsia: Analysis of the Korea Health Panel Survey 2017
by Boram Lee, Changsop Yang and Mi Hong Yim
Healthcare 2022, 10(7), 1192; https://doi.org/10.3390/healthcare10071192 - 25 Jun 2022
Cited by 2 | Viewed by 1332
Abstract
Functional dyspepsia (FD) significantly reduces quality of life, and Korean medicine treatment, including herbal medicine, is frequently used in the clinical setting. We aimed to analyze the factors affecting Korean medicine health care (KMHC) use for FD. Data from the Korea Health Panel [...] Read more.
Functional dyspepsia (FD) significantly reduces quality of life, and Korean medicine treatment, including herbal medicine, is frequently used in the clinical setting. We aimed to analyze the factors affecting Korean medicine health care (KMHC) use for FD. Data from the Korea Health Panel Survey 2017 were analyzed. Individuals aged > 19 years who were diagnosed with FD and used outpatient care were included. Multiple logistic regression analyses were performed to investigate the association of predisposing, enabling, and need factors with KMHC use for FD, based on Andersen’s behavioral model. The best subsets of factors affecting KMHC use for FD were selected using a stepwise procedure. Participants aged 65 years or older were less likely to use KMHC to treat FD than those aged 19 to 34 years (odds ratio (OR), 0.14; 95% confidence interval (CI), 0.02–0.93). Residents of Busan, Daegu, Ulsan, or Gyeongsang tended to use more KMHC to treat FD than those of Seoul, Gyeonggi, or Incheon (OR, 2.45; 95% CI, 1.02–5.88). Participants with private health insurance were more likely to use KMHC to treat FD than those without private health insurance (OR, 3.41; 95% CI, 1.02–11.42). The prediction model of KMHC use for FD selected sex, age, private health insurance, and stress as the best subset of factors (AUC, 0.709; 95% CI, 0.637–0.781). The results of this study will aid in the decision making of clinicians, researchers, and policymakers. Full article
(This article belongs to the Special Issue Clinical Informatics and Data Analysis in Healthcare)
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