Journal of healthcare informatics research最新文献

{"title":"Novel Videographic-Free Framework for Tracking Anatomical Structures Using Swallowing Accelerometer Signals and Multi-task Transformers.","authors":"Ayman Anwar, Wuqi Li, Amanda S Mahoney, Yassin Khalifa, James L Coyle, Ervin Sejdic","doi":"10.1007/s41666-025-00201-z","DOIUrl":"10.1007/s41666-025-00201-z","url":null,"abstract":"<p><p>High-resolution cervical auscultation (HRCA) has emerged as a promising noninvasive method for instrumental swallowing assessment, utilizing accelerometry signals from the patient's throat during swallowing. Compared to a traditional gold standard assessment technique, such as videofluoroscopic swallowing studies, HRCA offers reduced radiation exposure risks and increased accessibility. While previous studies have demonstrated the potential of machine learning in HRCA-based swallowing kinematics assessment, the accurate tracking of anatomical landmarks has remained a challenge. In this study, we propose a deep learning multi-task model that addresses this limitation by detecting the displacement of multiple anatomical structures during swallowing. By leveraging transformer encoders as sequential models, the proposed model tracks the displacement of the hyoid bone, laryngeal base, and hyolaryngeal approximation (HLA), which is the distance between the center of the hyoid bone and the laryngeal base and plays a crucial role in achieving safe and efficient swallows. For hyoid bone tracking, our model achieved an average relative overlapping (ROP) area exceeding 85%, exceeding the state-of-the-art by more than 30%. Additionally, the proposed model accurately tracks the laryngeal base with an average ROP exceeding 80% and predicts HLA distance in all frames with an average accuracy exceeding 95%, highlighting the transformative potential of our approach in encoding spatial information and the effect of the multi-task learning on tracking correlated structures. Our findings demonstrate significant promise in integrating HRCA into swallowing assessment protocols, marking a substantial advancement towards a noninvasive and comprehensive swallowing assessment method.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"9 3","pages":"297-319"},"PeriodicalIF":4.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Few-Shot Learning-Based Framework for Predicting Likelihood of In-patient's Mortality.","authors":"Josh Jia-Ching Ying, Jia-Xuan Yu, Chang-Lun Huang, Yu-An Chen","doi":"10.1007/s41666-025-00202-y","DOIUrl":"10.1007/s41666-025-00202-y","url":null,"abstract":"<p><p>In recent years, with the evolution of technology and electronic health records (EHR), the digitization of medical information enables the collection and analysis of vast amounts of data, encompassing physiological records, disease history, and medication records. This research aims to enhance healthcare delivery based on EHR, particularly predicting the likelihood of in-patient's mortality and length of stay. However, this digitization also brings challenges in data analysis, including managing high dimensionality, sparsity, imbalance, and unstructured features of data. Furthermore, the model's generality and the handling method of indicators with high correlation must be considered in the research. An efficient approach is required to deal with situations where limited data or task-related scenarios could benefit from information sharing. Despite the challenges posed by EHR's high dimensionality, sparsity, imbalance, and unstructured features, digital health technologies offer significant potential for enhancing medical services, and the quality and effectiveness of medical services are improved especially when these problems are solved successfully. A deep multitask and few-shot learning-based framework is proposed in this research. A mixed-expert neural network is adopted for the framework for multitask learning and predicting both likelihood of in-patient's mortality and length of stay. Additionally, the network architecture of the framework is used as an encoder for few-shot learning, enhancing the model's adaptability. Experimental results reveal a framework accuracy of 72.41%, slightly lower than the SVM model's 90% accuracy. However, the framework demonstrates a remarkable recall of 51.75%, which significantly exceeds the SVM model's recall of 4.35%. Moreover, it achieves an impressive F1-score of 60.40%, surpassing the performance of other models.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"9 3","pages":"320-358"},"PeriodicalIF":4.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Zero-Shot Extraction of Seizure Outcomes from Clinical Notes Using Generative Pretrained Transformers.","authors":"William K S Ojemann, Kevin Xie, Kevin Liu, Ellie Chang, Dan Roth, Brian Litt, Colin A Ellis","doi":"10.1007/s41666-025-00198-5","DOIUrl":"10.1007/s41666-025-00198-5","url":null,"abstract":"<p><p>Emerging evidence has shown that pre-trained encoder transformer models can extract information from unstructured clinic note text but require manual annotation for supervised fine-tuning. Large, Generative Pre-trained Transformer (GPT) models may streamline this process. In this study, we explore GPTs in zero- and few-shot learning scenarios to analyze clinical health records. We prompt-engineered Llama2 13B to optimize performance in extracting seizure freedom from epilepsy clinic notes and compared it against zero-shot and fine-tuned Bio + ClinicalBERT (BERT) models. Our evaluation encompasses different prompting paradigms, including one-word answers, elaboration-based responses, prompts with date formatting instructions, and prompts with dates in context. We found promising median accuracy rates in seizure freedom classification for zero-shot GPTs: one-word-62%, elaboration-50%, prompts with formatted dates-62%, and prompts with dates in context-74%. These outperform the zero-shot BERT model (25%) but fall short of the fully fine-tuned BERT model (84%). Furthermore, in sparse contexts, such as notes from general neurologists, the best performing GPT (76%) surpasses the fine-tuned BERT model (67%) in extracting seizure freedom. This study demonstrates the potential of GPTs in extracting clinically relevant information from unstructured EHR text, offering insights into population trends in seizure management, drug effects, risk factors, and healthcare disparities. Moreover, GPTs exhibit superiority over task-specific models in contexts with the potential to include less precise descriptions of epilepsy and seizures, highlighting their versatility. Additionally, simple prompt engineering techniques enhance model accuracy, presenting a framework for leveraging EHR data with zero clinical annotation.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s41666-025-00198-5.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"9 3","pages":"380-400"},"PeriodicalIF":3.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Effectiveness of Automatic Speech Recognition Technology in Emergency Medicine Settings: a Comparative Study of Four AI-Powered Engines.","authors":"Xiao Luo, Le Zhou, Kathleen Adelgais, Zhan Zhang","doi":"10.1007/s41666-025-00193-w","DOIUrl":"10.1007/s41666-025-00193-w","url":null,"abstract":"<p><p>This study investigates the potential of advanced automatic speech recognition (ASR) technology for transcribing and recognizing medical information during patient encounters, with the aim of enabling real-time clinical documentation to alleviate clinicians' workload. While ASR holds promise, its effectiveness in noisy and dynamic medical settings, such as emergency medical services (EMS), remains underexplored. To address this, four ASR engines-Google Speech-to-Text Clinical Conversation, OpenAI Speech-to-Text, Amazon Transcribe Medical, and Azure Speech-to-Text-were evaluated using 40 EMS simulation recordings. Transcriptions were analyzed for accuracy across 23 electronic health record (EHR) categories relevant to EMS. Google Speech-to-Text Clinical Conversation showed the best overall performance, excelling in categories such as \"mental state\" (F1 = 1.0), \"allergies\" (F1 = 0.912), and \"electrolytes\" (F1 = 1.0). However, all engines struggled with critical EMS categories like \"airway\" (F1 = 0.524) and \"pupils\" (F1 = 0.542). These findings highlight the limitations of current ASR technologies and the need for further advancements to improve accuracy and usability in time-sensitive and high-pressure medical environments.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s41666-025-00193-w.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"9 3","pages":"494-512"},"PeriodicalIF":4.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adapting Generative Large Language Models for Information Extraction from Unstructured Electronic Health Records in Residential Aged Care: A Comparative Analysis of Training Approaches.","authors":"Dinithi Vithanage, Chao Deng, Lei Wang, Mengyang Yin, Mohammad Alkhalaf, Zhenyu Zhang, Yunshu Zhu, Ping Yu","doi":"10.1007/s41666-025-00190-z","DOIUrl":"10.1007/s41666-025-00190-z","url":null,"abstract":"<p><p>Information extraction (IE) of unstructured electronic health records is challenging due to the semantic complexity of textual data. Generative large language models (LLMs) offer promising solutions to address this challenge. However, identifying the best training methods to adapt LLMs for IE in residential aged care settings remains underexplored. This research addresses this challenge by evaluating the effects of zero-shot and few-shot learning, both with and without parameter-efficient fine-tuning (PEFT) and retrieval-augmented generation (RAG) using Llama 3.1-8B. The study performed named entity recognition (NER) to nursing notes from Australian aged care facilities (RACFs), focusing on agitation in dementia and malnutrition risk factors. Performance evaluation includes accuracy, macro-averaged precision, recall, and F1 score. We used non-parametric statistical methods to compare if the differences were statistically significant. Results show that zero-shot and few-shot learning, whether combined with PEFT or RAG, achieve comparable performance across the clinical domains when the same prompting template is used. Few-shot learning significantly outperforms zero-shot learning when neither PEFT nor RAG is applied. Notably, PEFT significantly improves model performance in both zero-shot and few-shot learning; however, RAG significantly improves performance only in few-shot learning. After PEFT, the performance of zero-shot learning reaches a comparable level with few-shot learning. However, few-shot learning with RAG significantly outperforms zero-shot learning with RAG. We also found a similar level of performance between few-shot learning with RAG and zero-shot learning with PEFT. These findings provide valuable insights for researchers, practitioners, and stakeholders to optimize the use of generative LLMs in clinical IE.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s41666-025-00190-z.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"9 2","pages":"191-219"},"PeriodicalIF":4.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12037947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-technical Skills for Urology Trainees: A Double-Blinded Study of ChatGPT4 AI Benchmarking Against Consultant Interaction.","authors":"Matthew Pears, Karan Wadhwa, Stephen R Payne, Vishwanath Hanchanale, Mamoun Hamid Elmamoun, Sunjay Jain, Stathis Th Konstantinidis, Mark Rochester, Ruth Doherty, Kenneth Spearpoint, Oliver Ng, Lachlan Dick, Steven Yule, Chandra Shekhar Biyani","doi":"10.1007/s41666-024-00180-7","DOIUrl":"10.1007/s41666-024-00180-7","url":null,"abstract":"<p><p>Non-technical skills (NTS) are crucial in healthcare, encompassing cognitive and social skills that support technical ability. Traditional NTS training is evolving with the emergence of artificial intelligence (AI) models that can intelligently converse with their users, known as large language models (LLMs). This study investigated the capabilities and limitations of a popular model named generative pre-trained transformer 4 (GPT-4) in NTS training, comparing its performance to that of human evaluators. Urology trainees identified NTS events in simulated scenarios and discussed them in blinded feedback sessions with AI and human consultants. Experts assessed the blinded interaction data, providing quantitative ratings and qualitative evaluations using annotated transcripts. Wilcoxon signed-rank tests compared pre- and post-intervention ratings, whilst Mann-Whitney <i>U</i> tests compared post-intervention ratings between AI and human feedback. Thematic analysis identified strengths, limitations, and differences between AI and human feedback approaches. The AI model demonstrated significant strengths in reinforcing knowledge gathering (<i>p</i> = 0.04), providing accurate and evidence-based feedback (<i>p</i> = 0.013), conveying empathy (<i>p</i> = 0.021), and tailoring explanations to complexity (<i>p</i> = 0.002). However, human feedback excelled in language terminology (<i>p</i> = 0.003), complexity (<i>p</i> = 0.020), and fact-based feedback (<i>p</i> = 0.025). The study highlights the potential for AI to augment assessment of NTS training in healthcare. A blended approach utilising AI and human expertise may boost training efficacy.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"9 1","pages":"103-118"},"PeriodicalIF":4.6,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11782744/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis.","authors":"Huizi Yu, Lizhou Fan, Lingyao Li, Jiayan Zhou, Zihui Ma, Lu Xian, Wenyue Hua, Sijia He, Mingyu Jin, Yongfeng Zhang, Ashvin Gandhi, Xin Ma","doi":"10.1007/s41666-024-00171-8","DOIUrl":"10.1007/s41666-024-00171-8","url":null,"abstract":"<p><p>Large language models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), potentially enabling new ways to analyze data, treat patients, and conduct research. This study aims to provide a comprehensive overview of LLM applications in BHI, highlighting their transformative potential and addressing the associated ethical and practical challenges. We reviewed 1698 research articles from January 2022 to December 2023, categorizing them by research themes and diagnostic categories. Additionally, we conducted network analysis to map scholarly collaborations and research dynamics. Our findings reveal a substantial increase in the potential applications of LLMs to a variety of BHI tasks, including clinical decision support, patient interaction, and medical document analysis. Notably, LLMs are expected to be instrumental in enhancing the accuracy of diagnostic tools and patient care protocols. The network analysis highlights dense and dynamically evolving collaborations across institutions, underscoring the interdisciplinary nature of LLM research in BHI. A significant trend was the application of LLMs in managing specific disease categories, such as mental health and neurological disorders, demonstrating their potential to influence personalized medicine and public health strategies. LLMs hold promising potential to further transform biomedical research and healthcare delivery. While promising, the ethical implications and challenges of model validation call for rigorous scrutiny to optimize their benefits in clinical settings. This survey serves as a resource for stakeholders in healthcare, including researchers, clinicians, and policymakers, to understand the current state and future potential of LLMs in BHI.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"8 4","pages":"658-711"},"PeriodicalIF":4.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11499577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142515866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks.","authors":"Soheila Molaei, Nima Ghanbari Bousejin, Ghadeer O Ghosheh, Anshul Thakur, Vinod Kumar Chauhan, Tingting Zhu, David A Clifton","doi":"10.1007/s41666-024-00169-2","DOIUrl":"10.1007/s41666-024-00169-2","url":null,"abstract":"<p><p>Electronic Health Records (EHRs) play a crucial role in shaping predictive are models, yet they encounter challenges such as significant data gaps and class imbalances. Traditional Graph Neural Network (GNN) approaches have limitations in fully leveraging neighbourhood data or demanding intensive computational requirements for regularisation. To address this challenge, we introduce CliqueFluxNet, a novel framework that innovatively constructs a patient similarity graph to maximise cliques, thereby highlighting strong inter-patient connections. At the heart of CliqueFluxNet lies its stochastic edge fluxing strategy - a dynamic process involving random edge addition and removal during training. This strategy aims to enhance the model's generalisability and mitigate overfitting. Our empirical analysis, conducted on MIMIC-III and eICU datasets, focuses on the tasks of mortality and readmission prediction. It demonstrates significant progress in representation learning, particularly in scenarios with limited data availability. Qualitative assessments further underscore CliqueFluxNet's effectiveness in extracting meaningful EHR representations, solidifying its potential for advancing GNN applications in healthcare analytics.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"8 3","pages":"555-575"},"PeriodicalIF":5.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11310186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MELEP: A Novel Predictive Measure of Transferability in Multi-label ECG Diagnosis.","authors":"Cuong V Nguyen, Hieu Minh Duong, Cuong D Do","doi":"10.1007/s41666-024-00168-3","DOIUrl":"10.1007/s41666-024-00168-3","url":null,"abstract":"<p><p>In practical electrocardiography (ECG) interpretation, the scarcity of well-annotated data is a common challenge. Transfer learning techniques are valuable in such situations, yet the assessment of transferability has received limited attention. To tackle this issue, we introduce MELEP, which stands for <i>Muti-label Expected Log of Empirical Predictions</i>, a measure designed to estimate the effectiveness of knowledge transfer from a pre-trained model to a downstream multi-label ECG diagnosis task. MELEP is generic, working with new target data with different label sets, and computationally efficient, requiring only a single forward pass through the pre-trained model. To the best of our knowledge, MELEP is the first transferability metric specifically designed for multi-label ECG classification problems. Our experiments show that MELEP can predict the performance of pre-trained convolutional and recurrent deep neural networks, on small and imbalanced ECG data. Specifically, we observed strong correlation coefficients (with absolute values exceeding 0.6 in most cases) between MELEP and the actual average F1 scores of the fine-tuned models. Our work highlights the potential of MELEP to expedite the selection of suitable pre-trained models for ECG diagnosis tasks, saving time and effort that would otherwise be spent on fine-tuning these models.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"8 3","pages":"506-522"},"PeriodicalIF":4.6,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11310184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DDE: Deep Dynamic Epidemiological Modeling for Infectious Illness Development Forecasting in Multi-level Geographic Entities.","authors":"Ruhan Liu, Jiajia Li, Yang Wen, Huating Li, Ping Zhang, Bin Sheng, David Dagan Feng","doi":"10.1007/s41666-024-00167-4","DOIUrl":"10.1007/s41666-024-00167-4","url":null,"abstract":"<p><p>Understanding and addressing the dynamics of infectious diseases, such as coronavirus disease 2019, are essential for effectively managing the current situation and developing intervention strategies. Epidemiologists commonly use mathematical models, known as epidemiological equations (EE), to simulate disease spread. However, accurately estimating the parameters of these models can be challenging due to factors like variations in social distancing policies and intervention strategies. In this study, we propose a novel method called deep dynamic epidemiological modeling (DDE) to address these challenges. The DDE method combines the strengths of EE with the capabilities of deep neural networks to improve the accuracy of fitting real-world data. In DDE, we apply neural ordinary differential equations to solve variant-specific equations, ensuring a more precise fit for disease progression in different geographic regions. In the experiment, we tested the performance of the DDE method and other state-of-the-art methods using real-world data from five diverse geographic entities: the USA, Colombia, South Africa, Wuhan in China, and Piedmont in Italy. Compared to the state-of-the-art method, DDE significantly improved accuracy, with an average fitting Pearson coefficient exceeding 0.97 across the five geographic entities. In summary, the DDE method enhances the accuracy of parameter fitting in epidemiological models and provides a foundation for constructing simpler models adaptable to different geographic areas.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":"8 3","pages":"478-505"},"PeriodicalIF":3.7,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11310392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}