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26 pages, 1823 KiB

Open AccessArticle

Predicting Peritoneal Dialysis Failure Within the Next Three Months Based on Deep Learning and Important Features Analysis

by Fang-Yu Hsu, Ren-Hung Hwang, Ming-Hsien Tsai and Jing-Tong Wang

Information 2024, 15(12), 776; https://doi.org/10.3390/info15120776 - 5 Dec 2024

Viewed by 756

Abstract

This study aims to develop a deep learning model to predict peritoneal dialysis (PD) failure within the next three months using data from the preceding three months. Background: PD patients typically perform treatments at home and visit the clinic only once per month, [...] Read more.

This study aims to develop a deep learning model to predict peritoneal dialysis (PD) failure within the next three months using data from the preceding three months. Background: PD patients typically perform treatments at home and visit the clinic only once per month, leading to significant gaps in clinical care and increased risks of PD failure, which may necessitate a transition to hemodialysis (HD). Current studies on PD patients largely focus on predicting PD failure, mortality risk, and hospitalization through traditional machine learning methods, with limited application of deep learning for this purpose. Methods: We collected comprehensive patient data, including demographic information, comorbidities, medication history, biochemical test results, dialysis prescriptions, and peritoneal equilibrium test outcomes. After preprocessing, we employed time-series deep learning models to train and make predictions. Results: The model achieved a prediction accuracy of 89% and an AUROC of 92%, outperforming previous methods used for PD failure prediction. Conclusion: This approach not only improves prediction accuracy but also identifies key features that can aid clinicians in developing more precise treatment plans and enhancing patient care. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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27 pages, 2255 KiB

Open AccessArticle

Harnessing AI in Anxiety Management: A Chatbot-Based Intervention for Personalized Mental Health Support

by Alexia Manole, Răzvan Cârciumaru, Rodica Brînzaș and Felicia Manole

Information 2024, 15(12), 768; https://doi.org/10.3390/info15120768 - 2 Dec 2024

Cited by 1 | Viewed by 6448

Abstract

Anxiety disorders represent one of the most widespread mental health challenges globally, yet access to traditional therapeutic interventions remains constrained, particularly in resource-limited settings. This study evaluated the effectiveness of an AI-powered chatbot, developed using ChatGPT, in managing anxiety symptoms through evidence-based cognitive-behavioral [...] Read more.

Anxiety disorders represent one of the most widespread mental health challenges globally, yet access to traditional therapeutic interventions remains constrained, particularly in resource-limited settings. This study evaluated the effectiveness of an AI-powered chatbot, developed using ChatGPT, in managing anxiety symptoms through evidence-based cognitive-behavioral therapy (CBT) techniques. Fifty participants with mild to moderate anxiety symptoms engaged with the chatbot over two observational phases, each lasting seven days. The chatbot delivered personalized interventions, including mindfulness exercises, cognitive restructuring, and breathing techniques, and was accessible 24/7 to provide real-time support during emotional distress. The findings revealed a significant reduction in anxiety symptoms in both phases, with an average improvement of 21.15% in Phase 1 and 20.42% in Phase 2. Enhanced engagement in Phase 2 suggested the potential for sustained usability and familiarity with the chatbot’s functions. While participants reported high satisfaction with the accessibility and personalization of the chatbot, its inability to replicate human empathy underscored the importance of integrating AI tools with human oversight for optimal outcomes. This study highlights the potential of AI-driven interventions as valuable complements to traditional therapy, providing scalable and accessible mental health support, particularly in regions with limited access to professional services. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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14 pages, 3867 KiB

Open AccessArticle

Improving Consumer Health Search with Field-Level Learning-to-Rank Techniques

by Hua Yang and Teresa Gonçalves

Information 2024, 15(11), 695; https://doi.org/10.3390/info15110695 - 3 Nov 2024

Viewed by 722

Abstract

In the area of consumer health search (CHS), there is an increasing concern about returning topically relevant and understandable health information to the user. Besides being used to rank topically relevant documents, Learning to Rank (LTR) has also been used to promote understandability [...] Read more.

In the area of consumer health search (CHS), there is an increasing concern about returning topically relevant and understandable health information to the user. Besides being used to rank topically relevant documents, Learning to Rank (LTR) has also been used to promote understandability ranking. Traditionally, features coming from different document fields are joined together, limiting the performance of standard LTR, since field information plays an important role in promoting understandability ranking. In this paper, a novel field-level Learning-to-Rank (f-LTR) approach is proposed, and its application in CHS is investigated by developing thorough experiments on CLEF’ 2016–2018 eHealth IR data collections. An in-depth analysis of the effects of using f-LTR is provided, with experimental results suggesting that in LTR, title features are more effective than other field features in promoting understandability ranking. Moreover, the fused f-LTR model is compared to existing work, confirming the effectiveness of the methodology. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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32 pages, 4351 KiB

Open AccessArticle

Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis

by Kavinda Ashan Kulasinghe Wasalamuni Dewage, Raza Hasan, Bacha Rehman and Salman Mahmood

Information 2024, 15(10), 653; https://doi.org/10.3390/info15100653 - 18 Oct 2024

Cited by 4 | Viewed by 2295

Abstract

Brain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to [...] Read more.

Brain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to address these issues by incorporating interpretability techniques and strategies to mitigate class imbalance. We trained and evaluated four CNN models (proposed CNN, ResNetV2, DenseNet201, and VGG16) using a brain tumor MRI dataset, with oversampling techniques and class weighting employed during training. Our proposed CNN achieved an accuracy of 94.51%, outperforming other models in regard to precision, recall, and F1-Score. Furthermore, interpretability was enhanced through gradient-based attribution methods and saliency maps, providing valuable insights into the model’s decision-making process and fostering collaboration between AI systems and clinicians. This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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18 pages, 3557 KiB

Open AccessArticle

Toward Ensuring Data Quality in Multi-Site Cancer Imaging Repositories

by Alexandra Kosvyra, Dimitrios T. Filos, Dimitris Th. Fotopoulos, Olga Tsave and Ioanna Chouvarda

Information 2024, 15(9), 533; https://doi.org/10.3390/info15090533 - 2 Sep 2024

Cited by 2 | Viewed by 1641

Abstract

Cancer remains a major global health challenge, affecting diverse populations across various demographics. Integrating Artificial Intelligence (AI) into clinical settings to enhance disease outcome prediction presents notable challenges. This study addresses the limitations of AI-driven cancer care due to low-quality datasets by proposing [...] Read more.

Cancer remains a major global health challenge, affecting diverse populations across various demographics. Integrating Artificial Intelligence (AI) into clinical settings to enhance disease outcome prediction presents notable challenges. This study addresses the limitations of AI-driven cancer care due to low-quality datasets by proposing a comprehensive three-step methodology to ensure high data quality in large-scale cancer-imaging repositories. Our methodology encompasses (i) developing a Data Quality Conceptual Model with specific metrics for assessment, (ii) creating a detailed data-collection protocol and a rule set to ensure data homogeneity and proper integration of multi-source data, and (iii) implementing a Data Integration Quality Check Tool (DIQCT) to verify adherence to quality requirements and suggest corrective actions. These steps are designed to mitigate biases, enhance data integrity, and ensure that integrated data meets high-quality standards. We applied this methodology within the INCISIVE project, an EU-funded initiative aimed at a pan-European cancer-imaging repository. The use-case demonstrated the effectiveness of our approach in defining quality rules and assessing compliance, resulting in improved data integration and higher data quality. The proposed methodology can assist the deployment of big data centralized or distributed repositories with data from diverse data sources, thus facilitating the development of AI tools. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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17 pages, 3897 KiB

Open AccessArticle

Fattybot: Designing a Hormone-Morphic Chatbot with a Hormonal and Immune System

by Gonzalo Montero Albacete, Juan Murillo Murillo, Jorge Trasobares and Rafael Lahoz-Beltra

Information 2024, 15(8), 457; https://doi.org/10.3390/info15080457 - 1 Aug 2024

Viewed by 1625

Abstract

Currently, AI-designed systems in which, given a certain input or prompt, the system returns an output or response are becoming very popular. A chatbot is an example of this kind of system. However, human beings, besides processing the input stimuli or information adequately, [...] Read more.

Currently, AI-designed systems in which, given a certain input or prompt, the system returns an output or response are becoming very popular. A chatbot is an example of this kind of system. However, human beings, besides processing the input stimuli or information adequately, are also capable of simultaneously expressing an emotional response to that input information. This is a major factor in the survival of our species. For years, bio-inspired AI models have been proposed in order to make AI systems more human-like. Paradigms, such as neuromorphic computing, represent an example of this trend. In this paper, we propose a new approach that we have termed hormone-morphic by designing a chatbot, Fattybot, with which it is possible to have a conversation. However, since Fattybot is endowed with both a hormonal and immune system, it can feel anxiety or some other altered condition during a conversation, which induces the chatbot to eat compulsively. The ultimate goal of the work is to propose AI systems that not only process information but also experience some of the emotional traits of human beings. In this paper, several simulation experiments are performed showing the usefulness of this approach, for example, in the simulation of a virtual patient. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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16 pages, 3249 KiB

Open AccessArticle

Explainable Artificial Intelligence and Deep Learning Methods for the Detection of Sickle Cell by Capturing the Digital Images of Blood Smears

by Neelankit Gautam Goswami, Niranjana Sampathila, Giliyar Muralidhar Bairy, Anushree Goswami, Dhruva Darshan Brp Siddarama and Sushma Belurkar

Information 2024, 15(7), 403; https://doi.org/10.3390/info15070403 - 12 Jul 2024

Cited by 3 | Viewed by 2644

Abstract

A digital microscope plays a crucial role in the better and faster diagnosis of an abnormality using various techniques. There has been significant development in this domain of digital pathology. Sickle cell disease (SCD) is a genetic disorder that affects hemoglobin in red [...] Read more.

A digital microscope plays a crucial role in the better and faster diagnosis of an abnormality using various techniques. There has been significant development in this domain of digital pathology. Sickle cell disease (SCD) is a genetic disorder that affects hemoglobin in red blood cells. The traditional method for diagnosing sickle cell disease involves preparing a glass slide and viewing the slide using the eyepiece of a manual microscope. The entire process thus becomes very tedious and time consuming. This paper proposes a semi-automated system that can capture images based on a predefined program. It has an XY stage for moving the slide horizontally or vertically and a Z stage for focus adjustments. The case study taken here is of SCD. The proposed hardware captures SCD slides, which are further used to classify them with respect to normal. They are processed using deep learning models such as Darknet-19, ResNet50, ResNet18, ResNet101, and GoogleNet. The tested models demonstrated strong performance, with most achieving high metrics across different configurations varying with an average of around 97%. In the future, this semi-automated system will benefit pathologists and can be used in rural areas, where pathologists are in short supply. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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15 pages, 465 KiB

Open AccessArticle

Optimized Ensemble Learning Approach with Explainable AI for Improved Heart Disease Prediction

by Ibomoiye Domor Mienye and Nobert Jere

Information 2024, 15(7), 394; https://doi.org/10.3390/info15070394 - 8 Jul 2024

Cited by 27 | Viewed by 5533

Abstract

Recent advances in machine learning (ML) have shown great promise in detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent and explainable. Therefore, this research introduces an approach that [...] Read more.

Recent advances in machine learning (ML) have shown great promise in detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent and explainable. Therefore, this research introduces an approach that integrates the robustness of ensemble learning algorithms with the precision of Bayesian optimization for hyperparameter tuning and the interpretability offered by Shapley additive explanations (SHAP). The ensemble classifiers considered include adaptive boosting (AdaBoost), random forest, and extreme gradient boosting (XGBoost). The experimental results on the Cleveland and Framingham datasets demonstrate that the optimized XGBoost model achieved the highest performance, with specificity and sensitivity values of 0.971 and 0.989 on the Cleveland dataset and 0.921 and 0.975 on the Framingham dataset, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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Review

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10 pages, 545 KiB

Open AccessReview

Applications of Generative Artificial Intelligence in Electronic Medical Records: A Scoping Review

by Leo Morjaria, Bhavya Gandhi, Nabil Haider, Matthew Mellon and Matthew Sibbald

Information 2025, 16(4), 284; https://doi.org/10.3390/info16040284 - 1 Apr 2025

Viewed by 585

Abstract

Electronic Medical Records (EMRs) are central to the modern healthcare system. Recent advances in artificial intelligence (AI), particularly generative artificial intelligence (GenAI), have opened new opportunities for the advancement of EMRs. This scoping review aims to explore the current real-world applications of GenAI [...] Read more.

Electronic Medical Records (EMRs) are central to the modern healthcare system. Recent advances in artificial intelligence (AI), particularly generative artificial intelligence (GenAI), have opened new opportunities for the advancement of EMRs. This scoping review aims to explore the current real-world applications of GenAI within EMRs to support an understanding of AI applications in healthcare. A literature search was conducted following PRISMA-ScR guidelines. The search was conducted using Ovid MEDLINE, up to 28 October 2024, using a peer-reviewed search strategy. Overall, 55 studies were included. A list of five themes was generated by human reviewers based on the literature review: data manipulation (24), patient communication (9), clinical decision making (8), clinical prediction (8), summarization (4), and other (2). The majority of studies originated from the United States (35). Both proprietary and commercially available models were tested, with ChatGPT being the most commonly referenced LLM. As these models continue to be developed, their diverse use cases within EMRs have the potential to improve patient outcomes, enhance access to medical data, streamline hospital workflows, and reduce physician workload. However, continued problems surrounding data privacy, trust, bias, model hallucinations, and the need for robust evaluation remain. Further research considering the ethical, medical, and societal implications of GenAI applications in EMRs is essential to validate these findings and address existing limitations to support healthcare advancement. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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17 pages, 523 KiB

Open AccessReview

Artificial Intelligence in Bronchopulmonary Dysplasia: A Review of the Literature

by Tony Jha, Sana Suhail, Janet Northcote and Alvaro G. Moreira

Information 2025, 16(4), 262; https://doi.org/10.3390/info16040262 - 24 Mar 2025

Viewed by 394

Abstract

Bronchopulmonary dysplasia (BPD) is a neonatal lung condition predominantly affecting preterm infants. Researchers have turned to computational tools, such as artificial intelligence (AI) and machine learning (ML), to better understand, diagnose, and manage BPD in patients. This study aims to provide a comprehensive [...] Read more.

Bronchopulmonary dysplasia (BPD) is a neonatal lung condition predominantly affecting preterm infants. Researchers have turned to computational tools, such as artificial intelligence (AI) and machine learning (ML), to better understand, diagnose, and manage BPD in patients. This study aims to provide a comprehensive summary of current AI applications in BPD risk stratification, treatment, and management and seeks to guide future research towards developing practical and effective computational tools in neonatal care. This review highlights breakthroughs in predictive modeling using clinical-, genetic-, biomarker-, and imaging-based markers. AI has helped advance BPD management strategies by optimizing treatment pathways and prognostic predictions through computational modeling. While these developments become increasingly clinically applicable, numerous challenges remain in data standardization, external validation, and the equitable integration of AI solutions into clinical practice. Addressing ethical considerations, such as data privacy and demographic representation, as well as other practical considerations will be essential to ensure the proper implementation of AI clinical tools. Future research should focus on prospective, multicenter studies, leveraging multimodal data integration to enhance early diagnosis, personalized interventions, and long-term outcomes for neonates at risk of BPD. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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22 pages, 2064 KiB

Open AccessReview

Prescribing the Future: The Role of Artificial Intelligence in Pharmacy

by Hesham Allam

Information 2025, 16(2), 131; https://doi.org/10.3390/info16020131 - 11 Feb 2025

Cited by 1 | Viewed by 3417

Abstract

Integrating artificial intelligence (AI) into pharmacy operations and drug discovery represents a groundbreaking milestone in healthcare, offering unparalleled opportunities to revolutionize medication management, accelerate drug development, and deliver truly personalized patient care. This review examines the pivotal impact of AI in critical domains, [...] Read more.

Integrating artificial intelligence (AI) into pharmacy operations and drug discovery represents a groundbreaking milestone in healthcare, offering unparalleled opportunities to revolutionize medication management, accelerate drug development, and deliver truly personalized patient care. This review examines the pivotal impact of AI in critical domains, including drug discovery and development, drug repurposing, clinical trials, and pharmaceutical productivity enhancement. By significantly reducing human workload, improving precision, and shortening timelines, AI empowers the pharmaceutical industry to achieve ambitious objectives efficiently. This study delves into tools and methodologies enabling AI implementation, addressing ongoing challenges such as data privacy, algorithmic transparency, and ethical considerations while proposing actionable strategies to overcome these barriers. Furthermore, it offers insights into the future of AI in pharmacy, highlighting its potential to foster innovation, enhance efficiency, and improve patient outcomes. This research is grounded in a rigorous methodology, employing advanced data collection techniques. A comprehensive literature review was conducted using platforms such as PubMed, Semantic Scholar, and multidisciplinary databases, with AI-driven algorithms refining the retrieval of relevant and up-to-date studies. Systematic data scoping incorporated diverse perspectives from medical, pharmaceutical, and computer science domains, leveraging natural language processing for trend analysis and thematic content coding to identify patterns, challenges, and emerging applications. Modern visualization tools synthesized the findings into explicit graphical representations, offering a comprehensive view of the key role of AI in shaping the future of pharmacy and healthcare. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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19 pages, 618 KiB

Open AccessReview

Public Health Using Social Network Analysis During the COVID-19 Era: A Systematic Review

by Stanislava Gardasevic, Aditi Jaiswal, Manika Lamba, Jena Funakoshi, Kar-Hai Chu, Aekta Shah, Yinan Sun, Pallav Pokhrel and Peter Washington

Information 2024, 15(11), 690; https://doi.org/10.3390/info15110690 - 2 Nov 2024

Viewed by 2526

Abstract

Social network analysis (SNA), or the application of network analysis techniques to social media data, is an increasingly prominent approach used in computational public health research. We conducted a systematic review to investigate trends around SNA applied to social media data for public [...] Read more.

Social network analysis (SNA), or the application of network analysis techniques to social media data, is an increasingly prominent approach used in computational public health research. We conducted a systematic review to investigate trends around SNA applied to social media data for public health and epidemiology while outlining existing ethical practices. Following PRISMA guidelines, we reviewed articles from Web of Science and PubMed published between January 2019 and February 2024, leading to a total of 51 papers surveyed. The majority of analyzed research (69%) involved studying Twitter/X, followed by Sina Weibo (16%). The most prominent topics in this timeframe were related to COVID-19, while other papers explored public health topics such as citizen science, public emergencies, behavior change, and various medical conditions. We surveyed the methodological approaches and network characteristics commonly employed in public health SNA studies, finding that most studies applied only basic network metrics and algorithms such as layout, community detection, and standard centrality measures. We highlight the ethical concerns related to the use of social media data, such as privacy and consent, underscoring the potential of integrating ethical SNA with more inclusive, human-centered practices to enhance the effectiveness and community buy-in of emerging computational public health efforts. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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