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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 (30 April 2024) | Viewed by 19991

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Special Issue Editors

Dr. José Alberto Benítez Andrades

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Guest Editor

Salud Bienestar Ingeniería y Sostenibilidad Sociosanitaria (SALBIS) Research Group, Department of Electric, Systems and Automatics Engineering, University of León, Campus of Vegazana s/n, 24071 León, Spain
Interests: knowledge engineering; ontologies; artificial intelligence; machine learning; natural language processing; knowledge graphs; eHealth; public health
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Prof. Dr. Maria-Esther Vidal

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1. L3S Research Center, Leibniz University of Hannover, 30167 Hannover, Germany
2. TIB-Leibniz Information for Centre for Science and Technology, 30167 Hannover, Germany
Interests: data science, semantic web, knowledge graphs and big data analytics; databases, federated query optimization and processing; information systems, data integration, linked data; semantic data management for biomedicine and life sciences; omics data integration and analytics; research infrastructures

Prof. Dr. Alejandro Rodríguez-González

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Guest Editor

Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: medical informatics; artificial intelligence; disease networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health informatics and the analysis of massive data in the health field through techniques related to artificial intelligence are helping to create new procedures and applications. These techniques allow, for example, the early detection of diseases or help to create interventions considered necessary after extensively studying the data obtained in a given population. Currently, the COVID-19 pandemic has focused attention on the problem of infectious risk. Still, it is not the only health problem that requires attention and that can be improved with artificial intelligence or health informatics techniques. Thanks to these disciplines, systems are being developed that help to predict diseases with non-intrusive methods through the analysis of audio, text and images after applying artificial intelligence models, and to carry out interventions with computer tools that make it possible to advance and improve different problems, also known as eHealth.

In line with these premises, this Special Issue calls for manuscripts addressing health big data analysis and health informatics topics. Original articles and reviews will be considered on any advances in the field of artificial intelligence and knowledge engineering in the field of health, solving problems of a biomedical nature or closer to solving public health problems, as long as there is a significant component of data analyzed in such research using artificial intelligence techniques. Papers that address knowledge engineering issues, especially those that use knowledge graphs, linked data and predictive machine learning techniques for health problems, are welcome.

This Special Issue aims to provide an up-to-date overview of health informatics techniques applied to different public health problems, as well as the use of artificial intelligence on large amounts of data to help improve, among other things, the prediction and diagnosis of diseases or the improvement of public health.

Dr. José Alberto Benítez Andrades
Prof. Dr. Maria-Esther Vidal
Prof. Dr. Alejandro Rodríguez-González
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. 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

health informatics
eHealth
knowledge engineering in healthcare
knowledge graphs in healthcare
knowledge extraction and representation of health-related topics
artificial intelligence in healthcare
machine learning in healthcare
deep learning in healthcare
data analysis in healthcare data

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Published Papers (9 papers)

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10 pages, 515 KiB

Open AccessArticle

Quality of Care in Hospitals and the Use of Mobile-Based Personal Health Record Applications: An Exploratory Study Using National Hospital Evaluation Data

by Young-Taek Park, Mi-Joon Lee and Sang Mi Kim

Healthcare 2024, 12(11), 1064; https://doi.org/10.3390/healthcare12111064 - 23 May 2024

Cited by 1 | Viewed by 1025

Abstract

The use of mobile-based personal health record (m-PHR) applications at the hospital level has been minimally studied. This study aimed to investigate the relationship between m-PHR use and quality of care. A cross-sectional study design was employed, analyzing data from 99 hospitals. Two data sources were utilized: a previous m-PHR investigation conducted from 26 May to 30 June 2022 and a hospital evaluation dataset on quality of care. The use of m-PHR applications was measured by the number of m-PHR application downloads. Three independent variables were assessed: quality of care in the use of antibiotic drugs, injection drugs, and polypharmacy with ≥6 drugs. A generalized linear model was used for the analysis. The hospitals providing high-quality care, as evaluated based on the rate of antibiotic prescription (relative risk [RR], 3.328; 95% confidence interval [CI], 1.840 to 6.020; p < 0.001) and polypharmacy (RR, 2.092; 95% CI, 1.027 to 4.261; p = 0.042), showed an increased number of m-PHR downloads. Among the hospital covariates, public foundation status and being part of multi-hospital systems were associated with the number of m-PHR downloads (p < 0.05). This exploratory study found a positive relationship between quality of care and m-PHR use. Hospitals providing high-quality care may also excel in various activities, including m-PHR application use. Full article

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics)

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26 pages, 8424 KiB

Open AccessArticle

When Unstructured Big Text Corpus Meets Text Network Analysis: Social Reality Conceptualization and Visualization Graph of Big Interview Data of Heavy Drug Addicts of Skid Row

by Israel Fisseha Feyissa and Nan Zhang

Healthcare 2023, 11(17), 2439; https://doi.org/10.3390/healthcare11172439 - 31 Aug 2023

Cited by 1 | Viewed by 1906

Abstract

Relying on user-generated content narrating individual experiences and personalized contextualization of location-specific realities, this study introduced a novel methodological approach and analysis tool that can aid health informatics in understanding the social reality of people with a substance-use disorder in Skid Row, Los Angeles. The study also highlighted analysis possibilities for big unstructured interview text corpus using InfraNodus, a text network analysis tool. InfraNodus, which is a text graph analysis tool, identifies pathways for meaning circulation within unstructured interview data and has the potential to classify topical clusters and generate contextualized analysis results for big narrative textual datasets. Using InfraNodus, we analyzed a 1,103,528-word unstructured interview transcript from 315 interview sessions with people with a substance-use disorder, who narrated their respective social realities. Challenging the overgeneralization of onlookers, the conceptualization process identified topical clusters and pathways for meaning circulation within the narrative data, generating unbiased contextualized meaning for the collective social reality. Our endeavors in this research, along with our methodological setting and selection, might contribute to the methodological efforts of health informatics or the conceptualization and visualization needs of any big text corpus. Full article

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics)

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13 pages, 2134 KiB

Open AccessArticle

A Comparative Study on Deep Learning Models for COVID-19 Forecast

by Ziyuan Guo, Qingyi Lin and Xuhui Meng

Healthcare 2023, 11(17), 2400; https://doi.org/10.3390/healthcare11172400 - 26 Aug 2023

Cited by 1 | Viewed by 1424

Abstract

The COVID-19 pandemic has led to a global health crisis with significant morbidity, mortality, and socioeconomic disruptions. Understanding and predicting the dynamics of COVID-19 are crucial for public health interventions, resource allocation, and policy decisions. By developing accurate models, informed public health strategies can be devised, resource allocation can be optimized, and virus transmission can be reduced. Various mathematical and computational models have been developed to estimate transmission dynamics and forecast the pandemic’s trajectories. However, the evolving nature of COVID-19 demands innovative approaches to enhance prediction accuracy. The machine learning technique, particularly the deep neural networks (DNNs), offers promising solutions by leveraging diverse data sources to improve prevalence predictions. In this study, three typical DNNs, including the Long Short-Term Memory (LSTM) network, Physics-informed Neural Network (PINN), and Deep Operator Network (DeepONet), are employed to model and forecast COVID-19 spread. The training and testing data used in this work are the global COVID-19 cases in the year of 2021 from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. A seven-day moving average as well as the normalization techniques are employed to stabilize the training of deep learning models. We systematically investigate the effect of the number of training data on the predicted accuracy as well as the capability of long-term forecast in each model. Based on the relative

L_{2}

errors between the predictions from deep learning models and the reference solutions, the DeepONet, which is capable of learning hidden physics given the training data, outperforms the other two approaches in all test cases, making it a reliable tool for accurate forecasting the dynamics of COVID-19. Full article

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics)

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20 pages, 2770 KiB

Open AccessArticle

The Impact of Opioid Prescribing Limits on Drug Usage in South Carolina: A Novel Geospatial and Time Series Data Analysis

by Amirreza Sahebi-Fakhrabad, Amir Hossein Sadeghi, Eda Kemahlioglu-Ziya, Robert Handfield, Hossein Tohidi and Iman Vasheghani-Farahani

Healthcare 2023, 11(8), 1132; https://doi.org/10.3390/healthcare11081132 - 14 Apr 2023

Cited by 5 | Viewed by 2043

Abstract

The opioid crisis in the United States has had devastating effects on communities across the country, leading many states to pass legislation that limits the prescription of opioid medications in an effort to reduce the number of overdose deaths. This study investigates the impact of South Carolina’s prescription limit law (S.C. Code Ann. 44-53-360), which aims to reduce opioid overdose deaths, on opioid prescription rates. The study utilizes South Carolina Reporting and Identification Prescription Tracking System (SCRIPTS) data and proposes a distance classification system to group records based on proximity and evaluates prescription volumes in each distance class. Prescription volumes were found to be highest in classes with pharmacies located further away from the patient. An Interrupted Time Series (ITS) model is utilized to assess the policy impact, with benzodiazepine prescriptions as a control group. The ITS models indicate an overall decrease in prescription volume, but with varying impacts across the different distance classes. While the policy effectively reduced opioid prescription volumes overall, an unintended consequence was observed as prescription volume increased in areas where prescribers were located at far distances from patients, highlighting the limitations of state-level policies on doctors. These findings contribute to the understanding of the effects of prescription limit laws on opioid prescription rates and the importance of considering location and distance in policy design and implementation. Full article

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics)

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9 pages, 260 KiB

Open AccessArticle

A Survey on Scabies Inpatients in South Korea Based on Health Insurance Claims Data from 2010 to 2019

by Hyung-Seon Kim, Jji-Ya Bang and Kyung-Sook Cha

Healthcare 2023, 11(6), 841; https://doi.org/10.3390/healthcare11060841 - 13 Mar 2023

Cited by 1 | Viewed by 1622

Abstract

Due to the growing aging population and the increased number of long-term patients staying in nursing facilities, the prevalence of scabies has recently been increasing, even in developed countries. This study aimed to identify the actual status of hospitalized patients with scabies in South Korea using the national health insurance claims data. From 2010 to 2019, 2586 patients were hospitalized with scabies (B86) as the primary diagnosis. There were more females than males (χ² = 31.960, p < 0.001) and patients aged 80 years or older in long-term care hospitals (χ² = 431.410, p < 0.001). Scabies patients were mainly hospitalized in internal medicine, family medicine, and dermatology for all provider types (χ² = 170.033, p < 0.001). In long-term care hospitals, the rate of accompanying dementia was 31.9% (χ² = 193.418, p < 0.001), cerebral infarction was 10.4% (χ² = 106.271, p < 0.001), and cancer was 2.1% (χ² = 17.963, p < 0.001), which was higher than other provider types. Additionally, 20.6% in general hospitals (χ² = 198.952, p < 0.001) had an indwelling catheter, while 49.1% in hospitals and 41.1% in general hospitals were administered steroids (χ² = 214.440, p < 0.001). The KOH smear test was performed in 11.3% of all inpatients with scabies. We suggest recognizing these characteristics of scabies patients and thoroughly checking the skin lesions during physical examination for early diagnosis and prevention of scabies infection. Full article

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics)

18 pages, 7003 KiB

Open AccessEditor’s ChoiceArticle

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept

by José Miguel Ramírez-Sanz, José Luis Garrido-Labrador, Alicia Olivares-Gil, Álvaro García-Bustillo, Álvar Arnaiz-González, José-Francisco Díez-Pastor, Maha Jahouh, Josefa González-Santos, Jerónimo J. González-Bernal, Marta Allende-Río, Florita Valiñas-Sieiro, Jose M. Trejo-Gabriel-Galan and Esther Cubo

Healthcare 2023, 11(4), 507; https://doi.org/10.3390/healthcare11040507 - 9 Feb 2023

Cited by 3 | Viewed by 2670

Abstract

The consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson’s disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs. Full article

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics)

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22 pages, 4778 KiB

Open AccessArticle

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources

by Abdulkadir Atalan, Hasan Şahin and Yasemin Ayaz Atalan

Healthcare 2022, 10(10), 1920; https://doi.org/10.3390/healthcare10101920 - 30 Sep 2022

Cited by 20 | Viewed by 3218

Abstract

A healthcare resource allocation generally plays a vital role in the number of patients treated (p_nt) and the patient waiting time (w_t) in healthcare institutions. This study aimed to estimate p_nt and w_t as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δ_i) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δ_i of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for p_nt; 0.9514, 0.9517, 0.9514, and 0.9514 for w_t, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ_0.2 (AB had a better accuracy at 0.8709 based on the value of δ_0.2 for p_nt) in the test stage. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for p_nt; 0.8820, 0.8821, 0.8819, and 0.8818 for w_t in the training phase, respectively. All scenarios created by the δ_i coefficient should be preferred for ED since the income provided by the p_nt value to the hospital was more than the cost of healthcare resources. On the contrary, the w_t estimation results of ML algorithms based on the δ_i coefficient differed. Although w_t values in all ML algorithms with δ_0.0 and δ_0.1 coefficients reduced the cost of the hospital, w_t values based on δ_0.2 and δ_0.3 increased the cost of the hospital. Full article

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics)

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21 pages, 2208 KiB

Open AccessArticle

Repositioning Drugs for Rare Diseases Based on Biological Features and Computational Approaches

by Belén Otero-Carrasco, Lucía Prieto Santamaría, Esther Ugarte Carro, Juan Pedro Caraça-Valente Hernández and Alejandro Rodríguez-González

Healthcare 2022, 10(9), 1784; https://doi.org/10.3390/healthcare10091784 - 16 Sep 2022

Cited by 2 | Viewed by 2812

Abstract

Rare diseases are a group of uncommon diseases in the world population. To date, about 7000 rare diseases have been documented. However, most of them do not have a known treatment. As a result of the relatively low demand for their treatments caused by their scarce prevalence, the pharmaceutical industry has not sufficiently encouraged the research to develop drugs to treat them. This work aims to analyse potential drug-repositioning strategies for this kind of disease. Drug repositioning seeks to find new uses for existing drugs. In this context, it seeks to discover if rare diseases could be treated with medicines previously indicated to heal other diseases. Our approaches tackle the problem by employing computational methods that calculate similarities between rare and non-rare diseases, considering biological features such as genes, proteins, and symptoms. Drug candidates for repositioning will be checked against clinical trials found in the scientific literature. In this study, 13 different rare diseases have been selected for which potential drugs could be repositioned. By verifying these drugs in the scientific literature, successful cases were found for 75% of the rare diseases studied. The genetic associations and phenotypical features of the rare diseases were examined. In addition, the verified drugs were classified according to the anatomical therapeutic chemical (ATC) code to highlight the types with a higher predisposition to be repositioned. These promising results open the door for further research in this field of study. Full article

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics)

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9 pages, 936 KiB

Open AccessStudy Protocol

Real-World Outcomes of Systemic Therapy in Japanese Patients with Cancer (Tokushukai REAl-World Data Project: TREAD): Study Protocol for a Nationwide Cohort Study

by Rai Shimoyama, Yoshinori Imamura, Kiyoaki Uryu, Takahiro Mase, Yoshiaki Fujimura, Maki Hayashi, Megu Ohtaki, Keiko Ohtani, Nobuaki Shinozaki and Hironobu Minami

Healthcare 2022, 10(11), 2146; https://doi.org/10.3390/healthcare10112146 - 28 Oct 2022

Cited by 9 | Viewed by 1818

Abstract

Cohort studies using large-scale databases have become increasingly important in recent years. The Tokushukai Medical Group is a leading medical group in Japan that includes 71 general hospitals nationwide from Hokkaido to Okinawa, with a total of 18,000 beds, and a unified electronic medical record system. This retrospective cohort study aims to evaluate the real-world outcomes of systemic therapy for Japanese patients with cancer using this merit of scale. All adult patients with cancer who received systemic therapy using a centrally registered chemotherapy protocol system at 46 hospitals from April 2010 to March 2020 will be identified (~48,850 patients). Key exclusion criteria include active double cancer and inadequate data extraction. Data will be obtained through electronic medical records, diagnosis procedure combination data, medical prescription data, and the national cancer registration system that includes sociodemographic variables, diagnostic and laboratory tests, concomitant drug prescriptions, cost, and overall survival. Kaplan–Meier estimates will be calculated for time-to-event analyses. Stratified/conventional Cox proportional hazards regression analyses will be conducted to examine the relationships between overall survival and related factors. Our findings provide important insights for future research directions, policy initiatives, medical guidelines, and clinical decision-making. Full article

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics)

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