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16 pages, 1471 KiB

Open AccessArticle

Leveraging Machine Learning Techniques to Predict Cardiovascular Heart Disease

by Remzi Başar, Öznur Ocak, Alper Erturk and Marcelle de la Roche

Information 2025, 16(8), 639; https://doi.org/10.3390/info16080639 - 27 Jul 2025

Viewed by 426

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of death globally, underscoring the urgent need for data-driven early diagnostic tools. This study proposes a multilayer artificial neural network (ANN) model for heart disease prediction, developed using a real-world clinical dataset comprising 13,981 patient records. [...] Read more.

Cardiovascular diseases (CVDs) remain the leading cause of death globally, underscoring the urgent need for data-driven early diagnostic tools. This study proposes a multilayer artificial neural network (ANN) model for heart disease prediction, developed using a real-world clinical dataset comprising 13,981 patient records. Implemented on the Orange data mining platform, the ANN was trained using backpropagation and validated through 10-fold cross-validation. Dimensionality reduction via principal component analysis (PCA) enhanced computational efficiency, while Shapley additive explanations (SHAP) were used to interpret model outputs. Despite achieving 83.4% accuracy and high specificity, the model exhibited poor sensitivity to disease cases, identifying only 76 of 2233 positive samples, with a Matthews correlation coefficient (MCC) of 0.058. Comparative benchmarks showed that random forest and support vector machines significantly outperformed the ANN in terms of discrimination (AUC up to 91.6%). SHAP analysis revealed serum creatinine, diabetes, and hemoglobin levels to be the dominant predictors. To address the current study’s limitations, future work will explore LIME, Grad-CAM, and ensemble techniques like XGBoost to improve interpretability and balance. This research emphasizes the importance of explainability, data representativeness, and robust evaluation in the development of clinically reliable AI tools for heart disease detection. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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14 pages, 2075 KiB

Open AccessArticle

Performance Evaluation of Large Language Model Chatbots for Radiation Therapy Education

by Jae-Hong Jung, Daegun Kim, Kyung-Bae Lee and Youngjin Lee

Information 2025, 16(7), 521; https://doi.org/10.3390/info16070521 - 22 Jun 2025

Viewed by 542

Abstract

This study aimed to develop a large language model (LLM) chatbot for radiation therapy education and compare the performance of portable document format (PDF)- and webpage-based question-and-answer (Q&A) chatbots. An LLM chatbot was created using the EmbedChain framework, OpenAI GPT-3.5-Turbo API, and Gradio [...] Read more.

This study aimed to develop a large language model (LLM) chatbot for radiation therapy education and compare the performance of portable document format (PDF)- and webpage-based question-and-answer (Q&A) chatbots. An LLM chatbot was created using the EmbedChain framework, OpenAI GPT-3.5-Turbo API, and Gradio UI. The performance of both chatbots was evaluated based on 10 questions and their corresponding answers, using the parameters of accuracy, semantic similarity, consistency, and response time. The accuracy scores were 0.672 and 0.675 for the PDF- and webpage-based Q&A chatbots, respectively. The semantic similarity between the two chatbots was 0.928 (92.8%). The consistency score was one for both chatbots. The average response time was 3.3 s and 2.38 s for the PDF- and webpage-based chatbots, respectively. The LLM chatbot developed in this study demonstrates the potential to provide reliable responses for radiation therapy education. However, its reliability and efficiency must be further optimized to be effectively utilized as an educational tool. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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14 pages, 264 KiB

Open AccessArticle

A CPSO-BPNN-Based Analysis of Factors Influencing the Mental Health of Urban Youth

by Hu Xiang and Yong-Hong Lan

Information 2025, 16(6), 505; https://doi.org/10.3390/info16060505 - 17 Jun 2025

Viewed by 304

Abstract

The fast-paced lifestyle, high-pressure work environment, crowded traffic, and polluted air of urban environments often have a negative impact on urban youth’s mental health.Understanding the factors in urban environments that influence the mental health of young people and the differences among groups can [...] Read more.

The fast-paced lifestyle, high-pressure work environment, crowded traffic, and polluted air of urban environments often have a negative impact on urban youth’s mental health.Understanding the factors in urban environments that influence the mental health of young people and the differences among groups can help improve the adaptability and mental health of urban youth. Based on the 2024 report on the health status of urban youth in China, this paper first analyzes this through a combination of multiple linear regression and automated machine learning methods. The key influencing factors of different living styles and environments on the mental health of urban youth and the priority of influencing factors are evaluated. The results are obtained by using the chaos particle swarm optimization-based back propagation neural network (CPSO-BPNN) model. Then, the heterogeneity of the different types of urban youth groups is analyzed. Finally, the conclusions and recommendations of this article are presented. This study provides theoretical support and a scientific decision-making reference for improving the adaptability and health of urban youth. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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36 pages, 1318 KiB

Open AccessArticle

Traversable Ledger for Responsible Data Sharing and Access Control in Health Research

by Sunanda Bose and Dusica Marijan

Information 2024, 15(12), 815; https://doi.org/10.3390/info15120815 - 18 Dec 2024

Viewed by 1006

Abstract

Healthcare institutions and health registries often store patients’ health data. In order to ensure privacy, sensitive medical information is stored separately from the identifying information of the patient. Generally, institutions anonymize medical information while sharing it for external use. However, internal users may [...] Read more.

Healthcare institutions and health registries often store patients’ health data. In order to ensure privacy, sensitive medical information is stored separately from the identifying information of the patient. Generally, institutions anonymize medical information while sharing it for external use. However, internal users may also use it for identifying inaccuracies or missing information. Even though internal users may be legally permitted to access sensitive medical information, such access may lead to the identification of the patient, which can be vulnerable to patient privacy. Ensuring the accountability and responsibility of the internal users may lead to tractability in case of adversarial access with malicious intentions. Therefore, a secure system must be developed for the storage and retrieval of health data. To this end, in this paper, we propose a ledger-based system that cryptographically ensures that all access to health data must be logged into a ledger. Nevertheless, the ledger entries must be protected against adversarial access, too. At the same time, the ledger must be traversable by the patients as well as internal users. To address these needs, we propose techniques for the construction of a ledger to permit both internal users and patients to securely traverse and view only the entries to which they are linked. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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19 pages, 1452 KiB

Open AccessArticle

Electronic Health (eHealth) Literacy and Self-Care Behaviors—Results from a Survey of University Students in a Developing Country

by Salman Bin Naeem, Anthony Faiola, Aziz Ur-Rehman and Maged N. Kamel Boulos

Information 2024, 15(10), 636; https://doi.org/10.3390/info15100636 - 14 Oct 2024

Viewed by 2999

Abstract

eHealth literacy (eHL) is directly linked to disease prevention, health promotion, and improved healthcare outcomes. The objectives of this study are to assess undergraduate university students’ knowledge and perceived skills of finding, appraising, and applying electronic health information to health-related problems, as well [...] Read more.

eHealth literacy (eHL) is directly linked to disease prevention, health promotion, and improved healthcare outcomes. The objectives of this study are to assess undergraduate university students’ knowledge and perceived skills of finding, appraising, and applying electronic health information to health-related problems, as well as to assess the association of eHL with physical, psychological, and emotional self-care. Methods: The measurement model, comprising four correlated factors based on the 28 valid items from two reliable and valid tests, the ‘eHealth literacy scale (eHEALS)’ and ‘the self-care assessment tool (SCAT)’, was estimated using confirmatory factor analysis (CFA) among a sample of 1557 undergraduate university students in Pakistan. Results: The mean value of the eHEALS ranges between 2.90 and 3.33, indicating that the majority of the respondents had moderate levels of eHL skills. Female respondents and respondents from urban areas have greater levels of perceived eHL skills compared with their male and rural counterparts. The CFA model fit indices show that the goodness of fit values are acceptable: x² = 7.727, p = 0.000; RMSEA = 0.065; TLI = 0.930, CFI = 0.936, IFI = 0.936, GFI = 0.890, NFI = 0.928, RFI = 0.920, PGFI = 0.754. Conclusion: Electronic health (eHealth) literacy has a strong positive association with physical, psychological, and emotional self-care. However, perceived eHL skills among undergraduate university students are moderate, making them potentially susceptible to health risks. Implications: Our study has several practical implications. Its findings can be used to devise eHealth literacy programs for developing relevant skills among undergraduate university students based on their identified needs. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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18 pages, 1793 KiB

Open AccessArticle

An Open Data-Based Omnichannel Approach for Personalized Healthcare

by Ailton Moreira and Manuel Filipe Santos

Information 2024, 15(7), 415; https://doi.org/10.3390/info15070415 - 18 Jul 2024

Cited by 1 | Viewed by 1715

Abstract

Currently, telemedicine and telehealth have grown, prompting healthcare institutions to seek innovative ways to incorporate them into their services. Challenges such as resource allocation, system integration, and data compatibility persist in healthcare. Utilizing an open data approach in a versatile mobile platform holds [...] Read more.

Currently, telemedicine and telehealth have grown, prompting healthcare institutions to seek innovative ways to incorporate them into their services. Challenges such as resource allocation, system integration, and data compatibility persist in healthcare. Utilizing an open data approach in a versatile mobile platform holds great promise for addressing these challenges. This research focuses on adopting such an approach for a mobile platform catering to personalized care services. It aims to bridge identified gaps in healthcare, including fragmented communication channels and limited real-time data access, through an open data approach. This study builds upon previous research in omnichannel healthcare using prototyping to design a mobile companion for personalized care. By combining an omnichannel mobile companion with open data principles, this research successfully tackles key healthcare gaps, enhancing patient-centered care and improving data accessibility and integration. The strategy proves effective despite encountering challenges, although additional issues in personalized care services warrant further exploration and consideration. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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16 pages, 2688 KiB

Open AccessArticle

A Multimethod Approach for Healthcare Information Sharing Systems: Text Analysis and Empirical Data

by Amit Malhan, Robert Pavur, Lou E. Pelton and Ava Hajian

Information 2024, 15(6), 319; https://doi.org/10.3390/info15060319 - 29 May 2024

Cited by 1 | Viewed by 2098

Abstract

This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of [...] Read more.

This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of EHR implementation? What is the role of monitoring in EHR system implementation? What are the current themes and people’s attitudes toward EHR systems? This paper includes two empirical studies. Study 1 presents a research model based on data collected from four different archival datasets. Drawing upon the resource advantage theory, this paper uses archival data from 200 Texas hospitals, thus mitigating potential response bias and enhancing the validity of the findings. Study 2 includes a text analysis of 5154 textual data, sentiment analysis, and topic modeling. Study 1’s findings reveal that joint ventures and ownership are the two main enablers of adopting EHR systems in 200 Texas hospitals. Moreover, the results offer a moderating role of monitoring in strengthening the relationship between joint-venture capability and the implementation of EHR systems. Study 2’s results indicate a positive attitude toward EHR systems. The U.S. was unique in the sample due to its slower adoption of EHR systems than other developed countries. Physician burnout also emerged as a significant concern in the context of EHR adoption. Topic modeling identified three themes: training, healthcare interoperability, and organizational barriers. In a multimethod design, this paper contributes to prior work by offering two new EHR antecedents: hospital ownership and joint-venture capability. Moreover, this paper suggests that the monitoring mechanism moderates the adoption of EHR systems in Texas hospitals. Moreover, this paper contributes to prior EHR works by performing text analysis of textual data to carry out sentiment analysis and topic modeling. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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31 pages, 8370 KiB

Open AccessArticle

Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

by Fahim Sufi

Information 2024, 15(5), 264; https://doi.org/10.3390/info15050264 - 6 May 2024

Cited by 7 | Viewed by 4657

Abstract

This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT). In response to the limitations posed by a dearth of labeled medical data, our approach involves [...] Read more.

This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT). In response to the limitations posed by a dearth of labeled medical data, our approach involves the synthetic generation of comprehensive patient discharge messages, setting a new standard in the field with GPT autonomously generating 20 fields. Through a meticulous review of the existing literature, we systematically explore GPT’s aptitude for synthetic data generation and feature extraction, providing a robust foundation for subsequent phases of the research. The empirical demonstration showcases the transformative potential of our proposed solution, presenting over 70 patient discharge messages with synthetically generated fields, including severity and chances of hospital re-admission with justification. Moreover, the data had been deployed in a mobile solution where regression algorithms autonomously identified the correlated factors for ascertaining the severity of patients’ conditions. This study not only establishes a novel and comprehensive methodology but also contributes significantly to medical machine learning, presenting the most extensive patient discharge summaries reported in the literature. The results underscore the efficacy of GPT in overcoming data scarcity challenges and pave the way for future research to refine and expand the application of GPT in diverse medical contexts. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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23 pages, 6157 KiB

Open AccessArticle

Advancing Tuberculosis Detection in Chest X-rays: A YOLOv7-Based Approach

by Rabindra Bista, Anurag Timilsina, Anish Manandhar, Ayush Paudel, Avaya Bajracharya, Sagar Wagle and Joao C. Ferreira

Information 2023, 14(12), 655; https://doi.org/10.3390/info14120655 - 10 Dec 2023

Cited by 11 | Viewed by 6223

Abstract

In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is [...] Read more.

In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is capable of accurate object detection, which provides a bounding box denoting the area in the X-rays that shows some possibility of TB (tuberculosis). The system makes use of CNNs (Convolutional Neural Networks) and YOLO models for the detection of the consolidation of cavitary patterns of the lesions and their detection, respectively. For this study, we experimented on the TBX11K dataset, which is a publicly available dataset. In our experiment, we employed class weights and data augmentation techniques to address the data imbalance present in the dataset. This technique shows a promising improvement in the model’s performance and thus better generalization. In addition, it also shows that the developed model achieved promising results with a mAP (mean average precision) of 0.587, addressing class imbalance and yielding a robust performance for both obsolete pulmonary TB and active TB detection. Thus, our CAD system, rooted in state-of-the-art deep-learning and computer vision methodologies, not only advances diagnostic accuracy but also contributes to the mitigation of TB transmission risks. The substantial improvement in the model’s performance and the ability to handle class imbalance underscore the potential of our approach for real-world TB detection applications. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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13 pages, 1870 KiB

Open AccessArticle

Semantic Integration of BPMN Models and FHIR Data to Enable Personalized Decision Support for Malignant Melanoma

by Catharina Lena Beckmann, Daniel Keuchel, Wa Ode Iin Arliani Soleman, Sylvia Nürnberg and Britta Böckmann

Information 2023, 14(12), 649; https://doi.org/10.3390/info14120649 - 6 Dec 2023

Cited by 4 | Viewed by 3205 | Correction

Abstract

With digital patient data increasing due to new diagnostic methods and technology, showing the right data in the context of decision support at the point of care becomes an even greater challenge. Standard operating procedures (SOPs) modeled in BPMN (Business Process Model and [...] Read more.

With digital patient data increasing due to new diagnostic methods and technology, showing the right data in the context of decision support at the point of care becomes an even greater challenge. Standard operating procedures (SOPs) modeled in BPMN (Business Process Model and Notation) contain evidence-based treatment guidance for all phases of a certain diagnosis, while physicians need the parts relevant to a specific patient at a specific point in the clinical process. Therefore, integration of patient data from electronic health records (EHRs) providing context to clinicians is needed, which is stored and communicated in HL7 (Health Level Seven) FHIR (Fast Healthcare Interoperability Resources). To address this issue, we propose a method combining an integration of stored data into BPMN and a loss-free transformation from BPMN into FHIR, and vice versa. Based on that method, an identification of the next necessary decision point in a specific patient context is possible. We verified the method for treatment of malignant melanoma by using an extract of a formalized SOP document with predefined decision points and validated FHIR references with real EHR data. The patient data could be stored and integrated into the BPMN element ‘DataStoreReference’. Our loss-free transformation process therefore is the foundation for combining evidence-based knowledge from formalized clinical guidelines or SOPs and patient data from EHRs stored in FHIR. Processing the SOP with the available patient data can then lead to the next upcoming decision point, which will be displayed to the physician integrated with the corresponding data. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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19 pages, 2438 KiB

Open AccessArticle

A Deep Learning Approach for Predictive Healthcare Process Monitoring

by Ulises Manuel Ramirez-Alcocer, Edgar Tello-Leal, Gerardo Romero and Bárbara A. Macías-Hernández

Information 2023, 14(9), 508; https://doi.org/10.3390/info14090508 - 16 Sep 2023

Cited by 7 | Viewed by 3126

Abstract

In this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources since each trace linked to a [...] Read more.

In this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources since each trace linked to a case shows the consecutive execution of events in a healthcare process. The predictive model is based on a long short-term memory (LSTM) neural network that achieves high accuracy in the training and testing stages. In addition, a framework to implement the LSTM neural network is proposed, comprising stages from the preprocessing of the raw data to selecting the best LSTM model. The effectiveness of the prediction method is evaluated through four real-life event logs that contain historical information on the execution of the processes of patient transfer orders between hospitals, sepsis care cases, billing of medical services, and patient care management. In the test stage, the LSTM model reached values of 0.98, 0.91, 0.85, and 0.81 in the accuracy metric, and in the evaluation of the prediction of the next event using the 10-fold cross-validation technique, values of 0.94, 0.88, 0.84, and 0.81 were obtained for the four previously mentioned event logs. In addition, the performance of the LSTM prediction model was evaluated with the precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) metrics, obtaining high scores very close to 1. The experimental results suggest that the proposed method achieves acceptable measures in predicting the next event regardless of whether an input event or a set of input events is used. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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14 pages, 1838 KiB

Open AccessArticle

Digital-Reported Outcome from Medical Notes of Schizophrenia and Bipolar Patients Using Hierarchical BERT

by Rezaul K. Khandker, Md Rakibul Islam Prince, Farid Chekani, Paul Richard Dexter, Malaz A. Boustani and Zina Ben Miled

Information 2023, 14(9), 471; https://doi.org/10.3390/info14090471 - 22 Aug 2023

Viewed by 1840

Abstract

Patient-reported (PRO) and clinician-reported (CRO) outcomes are assessment instruments that are completed by patients and trained healthcare professionals, respectively. A PRO is a report of the direct experience of the patient with a given disease condition. A CRO is an assessment of the [...] Read more.

Patient-reported (PRO) and clinician-reported (CRO) outcomes are assessment instruments that are completed by patients and trained healthcare professionals, respectively. A PRO is a report of the direct experience of the patient with a given disease condition. A CRO is an assessment of the condition of the patient by the healthcare provider. PROs may not be accessible to all patients, especially those suffering from severe disease conditions. CROs are time-consuming and therefore administered infrequently. In the present study, we introduce a new form of assessment, the digital-reported outcome (DRO), which is automatically derived from the medical notes of the patient. DROs have a low overhead and can be generated at each patient’s visit to complement other outcome-assessment instruments and enhance clinical decision support by identifying at-risk patients. In this study, a DRO is developed to evaluate the functional impairment in the daily activities of two cohorts of patients suffering from bipolar disorder and schizophrenia. The input of the DRO is a single medical note from the electronic medical record of the patient. This note is submitted to a hierarchical bidirectional encoder representations from transformers (BERT) model. First, a sentence-level embedding is produced for each sentence in the note using a token-level attention mechanism. Second, an embedding for the entire note is constructed using a sentence-level attention mechanism. Third, the final embedding is classified using a feed-forward neural network. The model is trained to classify patients into moderate or severe functioning impairment levels according to the general assessment of functioning (GAF) scale, a CRO instrument for the assessment of the impact of mental illness on the daily activities of the patient. The DRO is validated using medical notes that were labeled by multiple healthcare providers from different healthcare institutions. The results indicate that a general DRO is able to classify patients from the two cohorts according to the two functioning impairment levels (severe versus moderate) prior to the onset of disease with an AUC of 76%. Disease-specific DROs are only applicable after the onset of the disease and produced AUCs of nearly 85%. The methodology introduced in the present paper is practical and can support the automated monitoring of the severity of the functioning impairment of bipolar and schizophrenia patients. Extending the proposed DRO to other psychiatric conditions and types of impairments is the subject of ongoing research work. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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41 pages, 2180 KiB

Open AccessSystematic Review

On the Application of Artificial Intelligence and Cloud-Native Computing to Clinical Research Information Systems: A Systematic Literature Review

by Isabel Bejerano-Blázquez and Miguel Familiar-Cabero

Information 2025, 16(8), 684; https://doi.org/10.3390/info16080684 - 10 Aug 2025

Viewed by 71

Abstract

The pharmaceutical and biotechnology sector is an intricate and rapidly evolving industry encompassing the full lifecycle of drugs, medicines, and clinical devices. Its growth is driven by factors such as the aging population, the rise in chronic diseases, and the increasing focus on [...] Read more.

The pharmaceutical and biotechnology sector is an intricate and rapidly evolving industry encompassing the full lifecycle of drugs, medicines, and clinical devices. Its growth is driven by factors such as the aging population, the rise in chronic diseases, and the increasing focus on personalized medicine. Nevertheless, it also faces significant challenges due to rising costs, increased complexity, and regulatory hurdles. Through a systematic literature review (SLR) as a research method combined with a comprehensive market analysis, this paper explores how several leading early-adopter healthcare companies are increasing their investments in computer-based clinical research information systems (CRISs) to sustain productivity, particularly through the adoption of artificial intelligence (AI) and cloud-native computing. As an extension of this research, a novel 360-degree reference blueprint is proposed for the domain analysis of medical features within AI-powered CRIS applications. This theoretical framework specifically targets clinical trial management systems (CRIS-CTMSs). Additionally, a detailed review is presented of the leading commercial solutions, assessing their portfolios and business maturity, while highlighting major open innovation collaborations with prominent pharmaceutical and biotechnology companies. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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