AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献

{"title":"RDguru: An Intelligent Agent for Rare Diseases.","authors":"Jian Yang, Liqi Shu, Huilong Duan, Haomin Li","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Large language models (LLMs) have shown great promise in clinical medicine, but their adoption in real-world settings has been limited by their tendency to generate incorrect and sometimes even toxic statements. This study presents a reliable rare disease intelligent agent called RDguru, which incorporates authoritative and reliable knowledge sources and tools into the reasoning and response of LLMs. In addition to answering questions about rare diseases more accurately, RDguru can conduct medical consultations to provide differential diagnosis decision support for clinical users. The DQN-based multi-source fusion diagnostic model integrates three diagnostic recommendation strategies, GPT-4, PheLR, and phenotype matching. Testing on 238 real rare disease cases showed that RDguru's top 10 list of recommended diagnoses was able to recall 69.1% of real diagnoses, the top 5 recommended diagnoses were able to recall 63.6% of real diagnoses, and the top ranked diagnosis was able to achieve an accuracy rate of 41.9%.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1275-1283"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144712","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":"Preemptive Forecasting of Symptom Escalation in Cancer Patients Undergoing Chemotherapy.","authors":"Joseph Finkelstein, Aref Smiley, Christina Echeverria, Kathi Mooney","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This study evaluates the utility of machine learning (ML) algorithms in early forecasting of total symptom score changes from daily self-reports of 339 chemotherapy patients. The dataset comprised 12 specific symptoms, with severity and distress for each symptom rated on a 1 to 10 scale, generating a \"total symptom score\" ranging from 0 to 230. To address the challenge of an unbalanced original dataset, where Class I (score change ≥ 5) and Class II (score change < 5) were unevenly represented, we created a balanced dataset specifically for model training. This process involved a stratified sampling technique to ensure equitable representation of both classes, enhancing the predictive analysis. Using the MATLAB® Classification Learner application, we investigated nine ML models, including decision trees, discriminant analysis, support vector machines (SVM), and others, each applying various classifiers. The objective was to predict the total symptom score change based on the preceding 3 to 5 days' symptom data. Models were trained on the balanced dataset to mitigate the original imbalance's impact, with comparative evaluations also conducted on the unbalanced data to assess performance differences. The analysis revealed that certain classifiers, such as SVM, delivered optimal performance on the unbalanced dataset, with an accuracy rate peaking at 82%. Yet, these models tended to frequently misclassify Class I as Class II. In contrast, the Ensemble algorithm equipped with the RUSBoost classifier demonstrated exceptional skill in accurately classifying both classes on both datasets, achieving accuracies of 59%, 59.3%, and 59.4% for data from 3, 4, and 5 days prior, respectively. Notably, these figures slightly improved to 61.16%, 58.41%, and 60.05% upon utilizing the balanced dataset for training. The deployment of a balanced dataset for model training underscores the significant potential of ML algorithms in improving symptom management for chemotherapy patients, offering a path to enhanced patient care and quality of life through targeted, personalized symptom monitoring.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"427-432"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144667","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":"Pregnancy Outcomes in Hidradenitis Suppurativa Patients.","authors":"David P Walsh","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Hidradenitis suppurativa is an autoinflammatory condition resulting in painful cysts, nodules, and sinus tracts in areas of high skin on skin contact. The microenvironment of affected tissues is high in pro-inflammatory cytokines and T-helper 17 cells. Other auto-inflammatory diseases, like psoriasis, have an enhanced risk of systemic inflammation and an elevated risk of spontaneous abortion. A cohort of pregnant patients from Cerner Health Facts® was identified using a Python adaptation of a validated pregnancy identification and classification algorithm. The HS population was identified among the pregnant population and was shown to be statistically significantly associated with outcome type by Chi square. A multinomial logistic regression also indicated a statistically significant increase in the odds of a pregnant patient having a spontaneous abortion over a live birth when controlling for thyroid disease, polycystic ovarian syndrome, antiphospholipid syndrome, other inflammatory diseases, and advanced maternal age.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1169-1175"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144670","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 Generative Foundation Model for Structured Patient Trajectory Data.","authors":"Yu Akagi, Tomohisa Seki, Yoshimasa Kawazoe, Toru Takiguchi, Kazuhiko Ohe","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Advancements in artificial intelligence propelled the implementation of general-purpose multitasking agents called foundation models. However, it has been challenging for foundation models to handle structured longitudinal medical data due to the mixed data types and variable timestamps in these data. Acquiring large training data is another obstacle. This study proposes a generative foundation model to manage patient trajectory data of variable lengths with mixed data types (categorical and continuous variables). Additionally, we propose a data pipeline to supply real-world data large enough to support foundation models. We locally obtained a large clinical dataset with a reproducible data pipeline scheme that leveraged a national HL7 message standard. Our trained model acquired the ability to suggest clinically relevant medical concepts and continuous variables for general purposes. The model also synthesized a database of more than 10,000 realistic patient trajectories. Our results suggest promising future downstream clinical applications of the foundation model.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"124-133"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144686","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 Multi-Task Learning Approach for Segmentation of Breast Arterial Calcifications in Screening Mammograms.","authors":"Aisha Urooj, Theo Dapamede, Bhavika Patel, Chadi Ayoub, Reza Arsanjani, William Charles O'Neill, Hari Trivedi, Imon Banerjee","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Screening mammogram is a standard and cost-efficient imaging procedure to measure breast cancer risk among 45+ year old women. Quantifying breast arterial calcification (BAC) from screening mammograms is a non-invasive and cost-efficient approach to assess the future risk of adverse cardiovascular events among women, such as heart attack and stroke. However, segmentation of breast arterial calcification is an involved task and poses several technical challenges such as extremely small BAC finding, low breast arteries to breast area ratio in the mammogram images, tissue features such as breast folds and heterogeneous density, have very similar imaging appearance. In this work, we aim to address the shortcomings of existing SOTA methods, e.g., SCUNet, and analyze the comparative performance. Given the fact that we will not be able to simply resize mammogram to preserve the microscopic BAC details, we adopted a patch-based methodology for segmentation using the original resolution which may hinder the model understanding of whole mammogram. We propose a multi-task learning approach for patch-based BAC segmentation by adding an auxiliary task of patch position prediction which forces the model to learn breast anatomy to comprehend the locations where BAC will not occur, such as breast boundary. The proposed method achieves state-of-the-art performance compared to the baselines. To demonstrate the utility, we also validate our method on external data and provide survival analysis for adverse cardiac events based on difference in BAC score and provide a comparison with coronary calcium score (CAC).</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1139-1148"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099439/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144688","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":"Changes in Health Information Exchange Use Behavior After Introduction of a Fast Healthcare Interoperability Resources (FHIR) Application.","authors":"Haleigh M Kampman, Rebecca L Rivera, Seho Park, Jason T Schaffer, Amy Hancock, Saurabh Rahurkar, Paul Musey, Diane Kuhn, Joshua R Vest, Titus K Schleyer","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The aim of our study was to characterize emergency department clinicians' health information exchange (HIE) use patterns after the implementation of a Fast Healthcare Interoperability Resources (FHIR) application. Using longitudinal electronic health record log data, we categorized HIE use behavior as: no HIE use (0), Web-based viewer use only (1), FHIR application use only (2), or Web-based viewer and FHIR application use (3). We sequenced HIE use behavior from September 2019 to February 2023, then employed hierarchical agglomerative clustering to identify clinician characteristics associated with each HIE use pattern. Our results showed four usage patterns representing (1) clinicians who \"lagged\" in HIE use and continued as sporadic HIE users (n=66, 46.1%), (2) \"late adopters\" who had more consistent usage over time (n=32, 22.4%), (3) \"legacy users\" whose preferred modality was the Web-based viewer (n=25, 17.5%), and (4) \"mixed modality users\" who displayed frequent changes in HIE access modality (n=20, 14.0%).</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"581-589"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144629","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":"Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning.","authors":"Inko Bovenzi, Adi Carmel, Michael Hu, Rebecca Hurwitz, Fiona McBride, Leo Benac, José Roberto Tello Ayala, Finale Doshi-Velez","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning (IRL) that identifies suboptimal clinician actions based on the actions of their peers. This approach centers two stages of IRL with an intermediate step to prune trajectories displaying behavior that deviates significantly from the consensus. This enables us to effectively identify clinical priorities and values from ICU data containing both optimal and suboptimal clinician decisions. We observe that the benefits of removing suboptimal actions vary by disease and differentially impact certain demographic groups.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"202-211"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099440/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144706","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":"Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Data.","authors":"Sarvesh Soni, Dina Demner-Fushman","doi":"","DOIUrl":"","url":null,"abstract":"<p><p><i>Regular documentation ofprogress notes is one of the main contributors to clinician burden. The abundance of structured chart information in medical records further exacerbates the burden, however, it also presents an opportunity to automate the generation of progress notes. In this paper, we propose a task to automate progress note generation using structured or tabular information present in electronic health records. To this end, we present a novel framework and a large dataset,</i> CHARTPNG, <i>for the task which contains</i> 7089 <i>annotation instances (each having a pair of progress notes and interim structured chart data) across</i> 1616 <i>patients. We establish baselines on the dataset using large language models from general and biomedical domains. We perform both automated (where the best performing Biomistral model achieved a BERTScore F1 of</i> 80.53 <i>and MEDCON score of</i> 19.61<i>) and manual (where we found that the model was able to leverage relevant structured data with</i> 76.9% <i>accuracy) analyses to identify the challenges with the proposed task and opportunities for future research.</i></p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1059-1068"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144820","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 Computationally-guided Qualitative Analysis to Understand User Experiences with Different Types of Mobile Personal Health Records.","authors":"Zainab A Balogun, Pronob K Barman, Bianka K Onwumbiko, Tera L Reynolds","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Mobile personal health records (mPHR) are smartphone apps that grant patients portable and continuous access to their medical records, thereby increasing the potential for patients to play an active role in managing their health. An extensive body of literature has focused on understanding user experiences with web-based tethered PHRs (i.e., patient portals) offered by healthcare organizations. However, patients' opinions of smartphone-based PHRs have received less attention. To address this gap, we used a computationally-guided qualitative analysis approach to analyze user reviews of six tethered and four interconnected mPHR apps available on both Google Play and Apple app stores. This approach resulted in identifying dimensions of user experiences related to usability, usefulness, and important features to users. Our findings reveal many similarities in user experiences for HCO-tethered and HCO-independent interconnected PHRs. However, there are some differences in user experiences between the types of PHRs and the different devices and platforms.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"162-171"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144683","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":"Probabilistic Graphical Models for Evaluating the Utility of Data-Driven ICD Code Categories in Pediatric Sepsis.","authors":"Lourdes A Valdez, Edgar Javier Hernandez, O'Connor Matthews, Matthew Mulvey, Hillary Crandall, Karen Eilbeck","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Electronic health records (EHRs) are information systems designed to collect and manage clinical data in order to support various clinical activities. They have emerged as valuable sources of data for outcomes research, offering vast repositories of patient information for analysis. Definitions for pediatric sepsis diagnosis are ambiguous, resulting in delayed diagnosis and treatment, highlighting the need for precise and efficient patient categorizing techniques. Nevertheless, the use of EHRs in research poses challenges. Although EHRs were originally created to document patient encounters, the medical coding was designed to satisfy billing requirements. As a result, EHR data may lack granularity, potentially leading to misclassification and incomplete representation of patient conditions. We compared data-driven ICD code categories to chart review using probabilistic graphical models (PGMs) due to their ability to handle uncertainty and incorporate prior knowledge. Overall, this paper demonstrates the potential of using PGMs to address these challenges and improve the analysis of ICD codes for sepsis outcomes research.</p>","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1149-1158"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144704","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}