Journal of the American Medical Informatics Association最新文献

{"title":"Alert design in the real world: a cross-sectional analysis of interruptive alerting at 9 academic pediatric health systems.","authors":"Swaminathan Kandaswamy, Julia K W Yarahuan, Elizabeth A Dobler, Matthew J Molloy, Lindsey A Knake, Sean M Hernandez, Anne A Fallon, Lauren M Hess, Allison B McCoy, Regine M Fortunov, Eric S Kirkendall, Naveen Muthu, Evan W Orenstein, Adam C Dziorny, Juan D Chaparro","doi":"10.1093/jamia/ocaf013","DOIUrl":"https://doi.org/10.1093/jamia/ocaf013","url":null,"abstract":"<p><strong>Objective: </strong>To assess the prevalence of recommended design elements in implemented electronic health record (EHR) interruptive alerts across pediatric care settings.</p><p><strong>Materials and methods: </strong>We conducted a 3-phase mixed-methods cross-sectional study. Phase 1 involved developing a codebook for alert content classification. Phase 2 identified the most frequently interruptive alerts at participating sites. Phase 3 applied the codebook to classify alerts. Inter-rater reliability (IRR) for the codebook and descriptive statistics for alert design contents were reported.</p><p><strong>Results: </strong>We classified alert content on design elements such as the rationale for the alert's appearance, the hazard of ignoring it, directive versus informational content, administrative purpose, and whether it aligned with one of the Institute of Medicine's (IOM) domains of healthcare quality. Most design elements achieved an IRR above 0.7, with the exceptions for identifying directive content outside of an alert (IRR 0.58) and whether an alert was for administrative purposes only (IRR 0.36). IRR was poor for all IOM domains except equity. Institutions varied widely in the number of unique alerts and their designs. 78% of alerts stated their purpose, over half were directive, and 13% were informational. Only 2%-20% of alerts explained the consequences of inaction.</p><p><strong>Discussion: </strong>This study raises important questions about the optimal balance of alert functions and desirable features of alert representation.</p><p><strong>Conclusion: </strong>Our study provides the first multi-center analysis of EHR alert design elements in pediatric care settings, revealing substantial variation in content and design. These findings underline the need for future research to experimentally explore EHR alert design best practices to improve efficiency and effectiveness.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191202","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":"Using dataflow diagrams to support research informed consent data management communications: participant perspectives.","authors":"Brian J McInnis, Ramona Pindus, Daniah H Kareem, Julie Cakici, Daniela G Vital, Eric Hekler, Camille Nebeker","doi":"10.1093/jamia/ocaf004","DOIUrl":"10.1093/jamia/ocaf004","url":null,"abstract":"<p><strong>Objectives: </strong>Digital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent.</p><p><strong>Materials and methods: </strong>We conducted eight semi-structured focus groups to explore whether dataflow diagrams (DFD) can complement informed consent and improve participants' understanding of data management and associated risks (N = 34 participants).</p><p><strong>Results: </strong>Our analysis found that DFDs could supplement text-based information about data management and sharing practices, such as by helping raise new questions that prompt conversation between prospective participants and members of a research team. Participants in the study emphasized the need for clear, simple, and accessible diagrams that are participant centered. Third-party access to data and sharing of sensitive health data were identified as high-risk areas requiring thorough explanation. Participants generally agreed that the design process should be led by the research team, but it should incorporate many diverse perspectives to ensure the diagram was meaningful to potential participants who are likely unfamiliar with data management. Nearly all participants rejected the idea that artificial intelligence could identify risks during the design process, but most were comfortable with it being used as a tool to format and simplify the diagram. In short, DFDs may complement standard text-based informed consent documents, but they are not a replacement.</p><p><strong>Discussion: </strong>Prospective research participants value diverse ways of learning about study risks and benefits. Our study highlights the value of incorporating information visualizations, such as DFDs, into the informed consent procedures to participate in research.</p><p><strong>Conclusion: </strong>Future research should explore other ways of visualizing consent information in ways that help people to overcome digital and data literacy barriers to participating in research. However, creating a DFD requires significant time and effort from research teams. To alleviate these costs, research sponsors can support the creation of shared infrastructure, communities of practice, and incentivize researchers to develop better consent procedures.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191205","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":"Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support.","authors":"Laura G Militello, Julie Diiulio, Debbie L Wilson, Khoa A Nguyen, Christopher A Harle, Walid Gellad, Wei-Hsuan Lo-Ciganic","doi":"10.1093/jamia/ocae291","DOIUrl":"10.1093/jamia/ocae291","url":null,"abstract":"<p><strong>Objectives: </strong>To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS).</p><p><strong>Materials and methods: </strong>This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS.</p><p><strong>Results: </strong>The role of design in biasing user behavior is well documented in behavioral economics and other disciplines. We offer an example of how UI designs play a role in how bias manifests in our machine learning-based CDS development.</p><p><strong>Discussion: </strong>Much discussion on bias in AI revolves around data quality and algorithm design; less attention is given to how UI design can exacerbate or mitigate limitations of AI-based applications.</p><p><strong>Conclusion: </strong>This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"398-403"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683165","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":"Distributed, immutable, and transparent biomedical limited data set request management on multi-capacity network.","authors":"Yufei Yu, Maxim Edelson, Anh Pham, Jonathan E Pekar, Brian Johnson, Kai Post, Tsung-Ting Kuo","doi":"10.1093/jamia/ocae288","DOIUrl":"10.1093/jamia/ocae288","url":null,"abstract":"<p><strong>Objective: </strong>Our study aimed to expedite data sharing requests of Limited Data Sets (LDS) through the development of a streamlined platform that allows distributed, immutable management of network activities, provides transparent and intuitive auditing of data access history, and systematically evaluated it on a multi-capacity network setting for meaningful efficiency metrics.</p><p><strong>Materials and methods: </strong>We developed a blockchain-based system with six types of smart contracts to automate the LDS sharing process among major stakeholders. Our workflow included metadata initialization, access-request processing, and audit-log querying. We evaluated our system using synthetic data on three machines with varying specifications to emulate real-world scenarios. The data employed included ∼1000 researcher requests and ∼360 000 log queries.</p><p><strong>Results: </strong>On average, it took ∼2.5 s to register and respond to a researcher access request. The average runtime for an audit-log query with non-empty output was ∼3 ms. The runtime metrics at each institution showed general trends affiliated with their computational capacity.</p><p><strong>Discussion: </strong>Our system can reduce the LDS sharing request time from potentially hours to seconds, while enhancing data access transparency in a multi-institutional setting. There were variations in performance across sites that could be attributed to differences in hardware specifications. The performance gains became marginal beyond certain hardware thresholds, pointing to the influence of external factors such as network speeds.</p><p><strong>Conclusion: </strong>Our blockchain-based system can potentially accelerate clinical research by strengthening the data access process, expediting access and delivery of data links, increasing transparency with clear audit trails, and reinforcing trust in medical data management. Our smart contracts are available at: https://github.com/graceyufei/LDS-Request-Management.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"296-307"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683182","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":"Hot topics in artificial intelligence.","authors":"Suzanne Bakken, Eric Poon","doi":"10.1093/jamia/ocae324","DOIUrl":"10.1093/jamia/ocae324","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":"32 2","pages":"265-267"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015037","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":"myAURA: a personalized health library for epilepsy management via knowledge graph sparsification and visualization.","authors":"Rion Brattig Correia, Jordan C Rozum, Leonard Cross, Jack Felag, Michael Gallant, Ziqi Guo, Bruce W Herr, Aehong Min, Jon Sanchez-Valle, Deborah Stungis Rocha, Alfonso Valencia, Xuan Wang, Katy Börner, Wendy Miller, Luis M Rocha","doi":"10.1093/jamia/ocaf012","DOIUrl":"10.1093/jamia/ocaf012","url":null,"abstract":"<p><strong>Objectives: </strong>Report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and clinicians in making decisions about self-management and care.</p><p><strong>Materials and methods: </strong>myAURA rests on an unprecedented collection of epilepsy-relevant heterogeneous data resources, such as biomedical databases, social media, and electronic health records (EHRs). We use a patient-centered biomedical dictionary to link the collected data in a multilayer knowledge graph (KG) computed with a generalizable, open-source methodology.</p><p><strong>Results: </strong>Our approach is based on a novel network sparsification method that uses the metric backbone of weighted graphs to discover important edges for inference, recommendation, and visualization. We demonstrate by studying drug-drug interaction from EHRs, extracting epilepsy-focused digital cohorts from social media, and generating a multilayer KG visualization. We also present our patient-centered design and pilot-testing of myAURA, including its user interface.</p><p><strong>Discussion: </strong>The ability to search and explore myAURA's heterogeneous data sources in a single, sparsified, multilayer KG is highly useful for a range of epilepsy studies and stakeholder support.</p><p><strong>Conclusion: </strong>Our stakeholder-driven, scalable approach to integrating traditional and nontraditional data sources enables both clinical discovery and data-powered patient self-management in epilepsy and can be generalized to other chronic conditions.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076198","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":"Information-blocking trends following regulatory action.","authors":"Jordan Everson, Daniel Healy","doi":"10.1093/jamia/ocaf007","DOIUrl":"https://doi.org/10.1093/jamia/ocaf007","url":null,"abstract":"<p><strong>Objective: </strong>To describe the prevalence of and trends in practices that interfere with the exchange of patient health information (potential information blocking) 2 years after implementation of information-blocking regulations.</p><p><strong>Materials and methods: </strong>Drawing from the American Hospital Association Information Technology (IT) Supplement and a national survey of health information organizations (HIOs), we described rates and methods of potential information blocking from these organizations' perspectives in 2023 and compared them to prior years.</p><p><strong>Results: </strong>Twenty-seven percent of hospitals sometimes or often observed potential information blocking by any actor in 2023, down from 42% in 2021 and 33% in 2022. Thirty percent of HIOs routinely observed potential information blocking by health IT developers, down from 50% in 2015. 13% of HIOs routinely observed potential information blocking by hospitals and health systems, down from 25% in 2015. According to both hospitals and HIOs, the most prevalent method of potential information blocking by developers in 2023 was through price, while the most prevalent by healthcare providers/health systems was by focusing exchange on strategic affiliations. Few hospitals and HIOs that experienced potential information blocking said that they had reported it to the Department of Health and Human Services.</p><p><strong>Discussion: </strong>Hospitals and HIOs perceived lower rates of potential information blocking in 2023 than in prior years indicating some impact of regulations addressing information blocking. However, both respondent types reported that substantial potential information blocking persisted in 2023 and negatively impacted the exchange of information.</p><p><strong>Conclusion: </strong>While potential information-blocking practices have decreased, they have not been eliminated, indicating the value of continued and robust enforcement of information-blocking regulations.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069001","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":"Long-term care plan recommendation for older adults with disabilities: a bipartite graph transformer and self-supervised approach.","authors":"Chunlong Miao, Jingjing Luo, Yan Liang, Hong Liang, Yuhui Cen, Shijie Guo, Hongliu Yu","doi":"10.1093/jamia/ocae327","DOIUrl":"https://doi.org/10.1093/jamia/ocae327","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirements of these individuals make developing effective long-term care plans heavily reliant on professional nursing staff, and even experienced caregivers may make mistakes or face confusion during the care plan development process. Consequently, there is a rigid demand for intelligent systems that can recommend comprehensive long-term care plans for older adults with disabilities who have stable clinical conditions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to utilize deep learning methods to recommend comprehensive care plans for the older adults with disabilities.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We model the care data of older adults with disabilities using a bipartite graph. Additionally, we employ a prediction-based graph self-supervised learning (SSL) method to mine deep representations of graph nodes. Furthermore, we propose a novel graph Transformer architecture that incorporates eigenvector centrality to augment node features and uses graph structural information as references for the self-attention mechanism. Ultimately, we present the Bipartite Graph Transformer (BiT) model to provide personalized long-term care plan recommendation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We constructed a bipartite graph comprising of 1917 nodes and 195 240 edges derived from real-world care data. The proposed model demonstrates outstanding performance, achieving an overall F1 score of 0.905 for care plan recommendations. Each care service item reached an average F1 score of 0.897, indicating that the BiT model is capable of accurately selecting services and effectively balancing the trade-off between incorrect and missed selections.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;The BiT model proposed in this paper demonstrates strong potential for improving long-term care plan recommendations by leveraging bipartite graph modeling and graph SSL. This approach addresses the challenges of manual care planning, such as inefficiency, bias, and errors, by offering personalized and data-driven recommendations. While the model excels in common care items, its performance on rare or complex services could be enhanced with further refinement. These findings highlight the model's ability to provide scalable, AI-driven solutions to optimize care planning, though future research should explore its applicability across diverse healthcare settings and service types.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Compared to previous research, the novel model proposed in this article effectively learns latent topology in bipartite graphs and achieves superior recommendation performance. Our study demonstrates the applicability of SSL and graph transformers in recommending long-term care plans for older adults with disabilitie","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069022","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":"Fast and interpretable mortality risk scores for critical care patients.","authors":"Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin","doi":"10.1093/jamia/ocae318","DOIUrl":"https://doi.org/10.1093/jamia/ocae318","url":null,"abstract":"<p><strong>Objective: </strong>Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.</p><p><strong>Material and methods: </strong>We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them. For evaluation, we leveraged the largest existing public ICU monitoring datasets (MIMIC III and eICU).</p><p><strong>Results: </strong>Models produced by GroupFasterRisk outperformed OASIS and SAPS II scores and performed similarly to APACHE IV/IVa while using at most a third of the parameters. For patients with sepsis/septicemia, acute myocardial infarction, heart failure, and acute kidney failure, GroupFasterRisk models outperformed OASIS and SOFA. Finally, different mortality prediction ML approaches performed better based on variables selected by GroupFasterRisk as compared to OASIS variables.</p><p><strong>Discussion: </strong>Group Faster Risk's models performed better than risk scores currently used in hospitals, and on par with black box ML models, while being orders of magnitude sparser. Because GroupFasterRisk produces a variety of risk scores, it allows design flexibility-the key enabler of practical model creation.</p><p><strong>Conclusion: </strong>Group Faster Risk is a fast, accessible, and flexible procedure that allows learning a diverse set of sparse risk scores for mortality prediction.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054068","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":"Evaluating robustly standardized explainable anomaly detection of implausible variables in cancer data.","authors":"Philipp Röchner, Franz Rothlauf","doi":"10.1093/jamia/ocaf011","DOIUrl":"https://doi.org/10.1093/jamia/ocaf011","url":null,"abstract":"<p><strong>Objectives: </strong>Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalous.</p><p><strong>Materials and methods: </strong>The dataset analyzed consists of 18 587 truncated real-world cancer registry records containing 8 categorical variables describing patients diagnosed with bladder and lung tumors. We identified 800 anomalous records using an autoencoder's per-record reconstruction error, which is a common neural network-based anomaly detection approach. For each variable of a record, we determined a robust explanation score, which indicates how anomalous the variable is. A variable's robust explanation score is the autoencoder's per-variable reconstruction error measured by cross-entropy and robustly standardized across records; that is, large reconstruction errors have a small effect on standardization. To evaluate the explanation scores, medical coders identified the implausible variables of the anomalous records. We then compare the explanation scores to the medical coders' validation in a classification and ranking setting. As baselines, we identified anomalous variables using the raw autoencoder's per-variable reconstruction error, the non-robustly standardized per-variable reconstruction error, the empirical frequency of implausible variables according to the medical coders' validation, and random selection or ranking of variables.</p><p><strong>Results: </strong>When we sort the variables by their robust explanation scores, on average, the 2.37 highest-ranked variables contain all implausible variables. For the baselines, on average, the 2.84, 2.98, 3.27, and 4.91 highest-ranked variables contain all the variables that made a record implausible.</p><p><strong>Discussion: </strong>We found that explanations based on robust explanation scores were better than or as good as the baseline explanations examined in the classification and ranking settings. Due to the international standardization of cancer data coding, we expect our results to generalize to other cancer types and registries. As we anticipate different magnitudes of per-variable autoencoder reconstruction errors in data from other medical registries and domains, these may also benefit from robustly standardizing the reconstruction errors per variable. Future work could explore methods to identify subsets of anomalous variables, addressing whether individual variables or their combinations contribute to anomalies. This direction aims to improve the interpretability and utility of anomaly detection systems.</p><p><strong>Conclusions: </strong>Robust explanation scores can improve explanations for identifying implausible variables in cancer data.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054062","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}