Journal of Biomedical Informatics最新文献

{"title":"Modeling repeated measurements data using the multilevel Bayesian network: A case of child morbidity","authors":"Bezalem Eshetu Yirdaw,&nbsp;Legesse Kassa Debusho","doi":"10.1016/j.jbi.2024.104760","DOIUrl":"10.1016/j.jbi.2024.104760","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>In epidemiological research, studying the long-term dependencies between multiple diseases is important. This study extends the multilevel Bayesian network (MBN) for repeated measures data that can estimate the rate of change in outcomes over time while quantifying the variabilities of these rates across higher-level units through various variance–covariance structures.</div></div><div><h3>Method:</h3><div>The performance and reliability of a model are examined through a simulation study, and its practical application is demonstrated using child morbidity data. This data has a hierarchical structure in which children were randomly selected from clusters (villages) and their conditions were assessed quarterly from March 2015 to May 2016. MBN was used to explore the relationship between outcomes weight-for-age (WAZ), height-for-age (HAZ), the number of days a child suffers from diarrhea (NOD), and flu (NOF), and estimate the rate of change of these outcomes over time. Since the outcomes considered were hybrid in nature, the connected three-parent set block Gibbs sampler with a multilevel generalized Poisson regression, multilevel zero inflated Poisson regression, and linear mixed-effects models were considered during the structure and parametric learning of the MBN.</div></div><div><h3>Result:</h3><div>The simulation study confirmed that a MBN using the time metric <span><math><mi>t</mi></math></span> as a node performed well for repeated measures data. The result from the structure learning of MBN shows a causal relationship between WAZ, HAZ, NOD and NOF. Furthermore, exclusive breastfeeding months and usage of micronutrient powder appeared as a strong predictor for all outcomes considered in this study.</div></div><div><h3>Conclusion:</h3><div>This study reveals that MBN is suitable in modeling repeated measures data to study the relationship between outcomes and estimate rate of change of an outcome over time while quantifying the variability due to higher-level clustering variables. Furthermore, the study highlights the importance of focusing on monitoring children with low WAZ and HAZ scores together with good feeding practices against the frequency of getting flu and diarrhea.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"161 ","pages":"Article 104760"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Repeatable process for extracting health data from HL7 CDA documents","authors":"Harry-Anton Talvik ,&nbsp;Marek Oja ,&nbsp;Sirli Tamm ,&nbsp;Kerli Mooses ,&nbsp;Dage Särg ,&nbsp;Marcus Lõo ,&nbsp;Õie Renata Siimon ,&nbsp;Hendrik Šuvalov ,&nbsp;Raivo Kolde ,&nbsp;Jaak Vilo ,&nbsp;Sulev Reisberg ,&nbsp;Sven Laur","doi":"10.1016/j.jbi.2024.104765","DOIUrl":"10.1016/j.jbi.2024.104765","url":null,"abstract":"<div><h3>Objective</h3><div>This study aims to address the gap in the literature on converting real-world Clinical Document Architecture (CDA) data into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), focusing on the initial steps preceding the mapping phase. We highlight the importance of a repeatable Extract-Transform-Load (ETL) pipeline for health data extraction from HL7 CDA documents in Estonia for research purposes.</div></div><div><h3>Methods</h3><div>We developed a repeatable ETL pipeline to facilitate the extraction, cleaning, and restructuring of health data from CDA documents to OMOP CDM, ensuring a high-quality and structured data format. This pipeline was designed to adapt to continuously updated data exchange format changes and handle various CDA document subsets for different scientific studies.</div></div><div><h3>Results</h3><div>We demonstrated via selected use cases that our pipeline successfully transformed a significant portion of diagnosis codes, body weight and eGFR measurements, and PAP test results from CDA documents into OMOP CDM, showing the ease of extracting structured data. However, challenges such as harmonising diverse coding systems and extracting lab results from free-text sections were encountered. The iterative development of the pipeline facilitated swift error detection and correction, enhancing the process’s efficiency.</div></div><div><h3>Conclusion</h3><div>After a decade of focused work, our research has led to the development of an ETL pipeline that effectively transforms HL7 CDA documents into OMOP CDM in Estonia, addressing key data extraction and transformation challenges. The pipeline’s repeatability and adaptability to various data subsets make it a valuable resource for researchers dealing with health data. While tested on Estonian data, the principles outlined are broadly applicable, potentially aiding in handling health data standards that vary by country. Despite newer health data standards emerging, the relevance of CDA for retrospective health studies ensures the continuing importance of this work.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"161 ","pages":"Article 104765"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reviewer acknowledgement 2024","authors":"","doi":"10.1016/j.jbi.2024.104763","DOIUrl":"10.1016/j.jbi.2024.104763","url":null,"abstract":"","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"161 ","pages":"Article 104763"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142882210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multimodal approach for few-shot biomedical named entity recognition in low-resource languages","authors":"Jian Chen ,&nbsp;Leilei Su ,&nbsp;Yihong Li ,&nbsp;Mingquan Lin ,&nbsp;Yifan Peng ,&nbsp;Cong Sun","doi":"10.1016/j.jbi.2024.104754","DOIUrl":"10.1016/j.jbi.2024.104754","url":null,"abstract":"<div><div>In this study, we revisit named entity recognition (NER) in the biomedical domain from a multimodal perspective, with a particular focus on applications in low-resource languages. Existing research primarily relies on unimodal methods for NER, which limits the potential for capturing diverse information. To address this limitation, we propose a novel method that integrates a cross-modal generation module to transform unimodal data into multimodal data, thereby enabling the use of enriched multimodal information for NER. Additionally, we design a cross-modal filtering module to mitigate the adverse effects of text–image mismatches in multimodal NER. We validate our proposed method on two biomedical datasets specifically curated for low-resource languages. Experimental results demonstrate that our method significantly enhances the performance of NER, highlighting its effectiveness and potential for broader applications in biomedical research and low-resource language contexts.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"161 ","pages":"Article 104754"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual-linguistic Diagnostic Semantic Enhancement for medical report generation","authors":"Jiahong Chen ,&nbsp;Guoheng Huang ,&nbsp;Xiaochen Yuan ,&nbsp;Guo Zhong ,&nbsp;Zhe Tan ,&nbsp;Chi-Man Pun ,&nbsp;Qi Yang","doi":"10.1016/j.jbi.2024.104764","DOIUrl":"10.1016/j.jbi.2024.104764","url":null,"abstract":"<div><div>Generative methods are currently popular for medical report generation, as they automatically generate professional reports from input images, assisting physicians in making faster and more accurate decisions. However, current methods face significant challenges: 1) Lesion areas in medical images are often difficult for models to capture accurately, and 2) even when captured, these areas are frequently not described using precise clinical diagnostic terms. To address these problems, we propose a Visual-Linguistic Diagnostic Semantic Enhancement model (VLDSE) to generate high-quality reports. Our approach employs supervised contrastive learning in the Image and Report Semantic Consistency (IRSC) module to bridge the semantic gap between visual and linguistic features. Additionally, we design the Visual Semantic Qualification and Quantification (VSQQ) module and the Post-hoc Semantic Correction (PSC) module to enhance visual semantics and inter-word relationships, respectively. Experiments demonstrate that our model achieves promising performance on the publicly available IU X-RAY and MIMIC-MV datasets. Specifically, on the IU X-RAY dataset, our model achieves a BLEU-4 score of 18.6%, improving the baseline by 12.7%. On the MIMIC-MV dataset, our model improves the BLEU-1 score by 10.7% over the baseline. These results demonstrate the ability of our model to generate accurate and fluent descriptions of lesion areas.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"161 ","pages":"Article 104764"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142921316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Journal's cover","authors":"","doi":"10.1016/S1532-0464(25)00004-8","DOIUrl":"10.1016/S1532-0464(25)00004-8","url":null,"abstract":"","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"161 ","pages":"Article 104775"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling pathology-related predictive uncertainty of glomerular lesion recognition using prototype learning","authors":"Qiming He ,&nbsp;Yingming Xu ,&nbsp;Qiang Huang ,&nbsp;Yanxia Wang ,&nbsp;Jing Ye ,&nbsp;Yonghong He ,&nbsp;Jing Li ,&nbsp;Lianghui Zhu ,&nbsp;Zhe Wang ,&nbsp;Tian Guan","doi":"10.1016/j.jbi.2024.104745","DOIUrl":"10.1016/j.jbi.2024.104745","url":null,"abstract":"<div><h3>Objective</h3><div>Recognizing glomerular lesions is essential in diagnosing chronic kidney disease. However, deep learning faces challenges due to the lesion heterogeneity, superposition, progression, and tissue incompleteness, leading to uncertainty in model predictions. Therefore, it is crucial to analyze pathology-related predictive uncertainty in glomerular lesion recognition and unveil its relationship with pathological properties and its impact on model performance.</div></div><div><h3>Methods</h3><div>This paper presents a novel framework for pathology-related predictive uncertainty analysis towards glomerular lesion recognition, including prototype learning based predictive uncertainty estimation, pathology-characterized correlation analysis and weight-redistributed prediction rectification. The prototype learning based predictive uncertainty estimation includes deep prototyping, affinity embedding, and multi-dimensional uncertainty fusion. The pathology-characterized correlation analysis is the first to use expert-based and learning- based approach to construct the pathology-related characterization of lesions and tissues. The weight-redistributed prediction rectification module performs reweighting- based lesion recognition.</div></div><div><h3>Results</h3><div>To validate the performance, extensive experiments were conducted. Based on the Spearman and Pearson correlation analysis, the proposed framework enables more efficient correlation analysis, and strong correlation with pathology-related characterization can be achieved (c index &gt; 0.6 and p &lt; 0.01). Furthermore, the prediction rectification module demonstrated improved lesion recognition performance across most metrics, with enhancements of up to 6.36 %.</div></div><div><h3>Conclusion</h3><div>The proposed predictive uncertainty analysis in glomerular lesion recognition offers a valuable approach for assessing computational pathology’s predictive uncertainty from a pathology-related perspective.</div></div><div><h3>Significance</h3><div>The paper provides a solution for pathology-related predictive uncertainty estimation in algorithm development and clinical practice.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"161 ","pages":"Article 104745"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142921240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early multi-cancer detection through deep learning: An anomaly detection approach using Variational Autoencoder","authors":"Innocent Tatchum Sado ,&nbsp;Louis Fippo Fitime ,&nbsp;Geraud Fokou Pelap ,&nbsp;Claude Tinku ,&nbsp;Gaelle Mireille Meudje ,&nbsp;Thomas Bouetou Bouetou","doi":"10.1016/j.jbi.2024.104751","DOIUrl":"10.1016/j.jbi.2024.104751","url":null,"abstract":"<div><div>Cancer is a disease that causes many deaths worldwide. The treatment of cancer is first and foremost a matter of detection, a treatment that is most effective when the disease is detected at an early stage. With the evolution of technology, several computer-aided diagnosis tools have been developed around cancer; several image-based cancer detection methods have been developed too. However, cancer detection faces many difficulties related to early detection which is crucial for patient survival rate. To detect cancer early, scientists have been using transcriptomic data. However, this presents some challenges such as unlabeled data, a large amount of data, and image-based techniques that only focus on one type of cancer. The purpose of this work is to develop a deep learning model that can effectively detect as soon as possible, specifically in the early stages, any type of cancer as an anomaly in transcriptomic data. This model must have the ability to act independently and not be restricted to any specific type of cancer. To achieve this goal, we modeled a deep neural network (a Variational Autoencoder) and then defined an algorithm for detecting anomalies in the output of the Variational Autoencoder. The Variational Autoencoder consists of an encoder and a decoder with a hidden layer. With the TCGA and GTEx data, we were able to train the model for six types of cancer using the Adam optimizer with decay learning for training, and a two-component loss function. As a result, we obtained the lowest value of accuracy 0.950, and the lowest value of recall 0.830. This research leads us to the design of a deep learning model for the detection of cancer as an anomaly in transcriptomic data.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"160 ","pages":"Article 104751"},"PeriodicalIF":4.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to identify patient perception of AI voice robots in the follow-up scenario? A multimodal identity perception method based on deep learning","authors":"Mingjie Liu ,&nbsp;Kuiyou Chen ,&nbsp;Qing Ye ,&nbsp;Hong Wu","doi":"10.1016/j.jbi.2024.104757","DOIUrl":"10.1016/j.jbi.2024.104757","url":null,"abstract":"<div><h3>Objectives</h3><div>Post-discharge follow-up stands as a critical component of post-diagnosis management, and the constraints of healthcare resources impede comprehensive manual follow-up. However, patients are less cooperative with AI follow-up calls or may even hang up once AI voice robots are perceived. To improve the effectiveness of follow-up, alternative measures should be taken when patients perceive AI voice robots. Therefore, identifying how patients perceive AI voice robots is crucial. This study aims to construct a multimodal identity perception model based on deep learning to identify how patients perceive AI voice robots.</div></div><div><h3>Methods</h3><div>Our dataset includes 2030 response audio recordings and corresponding texts from patients. We conduct comparative experiments and perform an ablation study. The proposed model employs a transfer learning approach, utilizing BERT and TextCNN for text feature extraction, AST and LSTM for audio feature extraction, and self-attention for feature fusion.</div></div><div><h3>Results</h3><div>Our model demonstrates superior performance against existing baselines, with a precision of 86.67%, an AUC of 84%, and an accuracy of 94.38%. Additionally, a generalization experiment was conducted using 144 patients’ response audio recordings and corresponding text data from other departments in the hospital, confirming the model’s robustness and effectiveness.</div></div><div><h3>Conclusion</h3><div>Our multimodal identity perception model can identify how patients perceive AI voice robots effectively. Identifying how patients perceive AI not only helps to optimize the follow-up process and improve patient cooperation, but also provides support for the evaluation and optimization of AI voice robots.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"160 ","pages":"Article 104757"},"PeriodicalIF":4.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biomedical document-level relation extraction with thematic capture and localized entity pooling","authors":"Yuqing Li,&nbsp;Xinhui Shao","doi":"10.1016/j.jbi.2024.104756","DOIUrl":"10.1016/j.jbi.2024.104756","url":null,"abstract":"<div><div>In contrast to sentence-level relational extraction, document-level relation extraction poses greater challenges as a document typically contains multiple entities, and one entity may be associated with multiple other entities. Existing methods often rely on graph structures to capture path representations between entity pairs. However, this paper introduces a novel approach called local entity pooling that solely relies on the pre-training model to identify the bridge entity related to the current entity pair and generate the reasoning path representation. This technique effectively mitigates the multi-entity problem. Additionally, the model leverages the multi-entity and multi-label characteristics of the document to acquire the document’s thematic representation, thereby enhancing the document-level relation extraction task. Experimental evaluations conducted on two biomedical datasets, CDR and GDA. Our TCLEP (<strong>T</strong>hematic <strong>C</strong>apture and <strong>L</strong>ocalized <strong>E</strong>ntity <strong>P</strong>ooling) model achieved the Macro-F1 scores of 71.7% and 85.3%, respectively. Simultaneously, we incorporated local entity pooling and thematic capture modules into the state-of-the-art model, resulting in performance improvements of 1.5% and 0.2% on the respective datasets. These results highlight the advanced performance of our proposed approach.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"160 ","pages":"Article 104756"},"PeriodicalIF":4.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}