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AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献

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Development of a Study Protocol for Evaluation of a Novel Measure to Incorporate Information Freshness into Network Analysis of Online Resources for COVID-19. 为 COVID-19 制定研究方案，评估将信息新鲜度纳入在线资源网络分析的新措施。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01
Meredith Abrams, Audrey Wong, Hanae El Kholti, Yunro Chung, Lisa Armitige, Dongwen Wang
{"title":"Development of a Study Protocol for Evaluation of a Novel Measure to Incorporate Information Freshness into Network Analysis of Online Resources for COVID-19.","authors":"Meredith Abrams, Audrey Wong, Hanae El Kholti, Yunro Chung, Lisa Armitige, Dongwen Wang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We proposed a novel measure, Degree of Connectivity with Integration of Freshness (DCIF), to incorporate information freshness into analysis of online resource networks. We conducted a pilot study to apply this new measure to a dataset of online information resources related to COVID-19 risk assessment. Among the 52 nodes, we recorded statistically significant difference between the numerical values of DCIF and the traditional structural measure Degree of Connectivity (DC). Manual reviews of 18 selected nodes showed that DCIF outperformed DC in 11 of them, suggesting potential promise of the proposed new measure. We finalized the protocol for manual review based on the pilot and started a full-scale study. The proposed new measure has the potential to provide quantitative assessment on information freshness for timely and effective dissemination of clinical evidence. Further research is required to address the limitations of this pilot study and to examine the generalization of the findings.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200533","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}
引用次数: 0
Low-Cost Histopathological Mitosis Detection for Microscope-acquired Images. 利用显微镜获取的图像进行低成本组织病理学有丝分裂检测
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01
Bilal Shabbir, Saira Saleem, Iffat Aleem, Nida Babar, Hammad Farooq, Asif Loya, Hammad Naveed
{"title":"Low-Cost Histopathological Mitosis Detection for Microscope-acquired Images.","authors":"Bilal Shabbir, Saira Saleem, Iffat Aleem, Nida Babar, Hammad Farooq, Asif Loya, Hammad Naveed","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Cancer outcomes are poor in resource-limited countries owing to high costs and insufficient pathologist-population ratio. The advent of digital pathology has assisted in improving cancer outcomes, however, Whole Slide Image scanners are expensive and not affordable in low-income countries. Microscope-acquired images on the other hand are cheap to collect and can be more viable for automation of cancer detection. In this study, we propose LCH-Network, a novel method to identify the cancer mitotic count from microscope-acquired images. We introduced Label Mix, and also synthesized images using GANs to handle data imbalance. Moreover, we applied progressive resolution to handle different image scales for mitotic localization. We achieved F1-Score of 0.71 and outperformed other existing techniques. Our findings enable mitotic count estimation from microscopic images with a low-cost setup. Clinically, our method could help avoid presumptive treatment without a confirmed cancer diagnosis.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201187","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}
引用次数: 0
FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation. FERI：基于多任务的公平实现算法，应用于公平器官移植。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2024-05-31 eCollection Date: 2024-01-01
Can Li, Dejian Lai, Xiaoqian Jiang, Kai Zhang
{"title":"FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation.","authors":"Can Li, Dejian Lai, Xiaoqian Jiang, Kai Zhang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes such as age group, gender, and race/ethnicity. Machine learning models for outcome prediction can introduce additional biases. Therefore, we introduce <b>F</b>airness through the <b>E</b>quitable <b>R</b>ate of <b>I</b>mprovement in Multitask Learning (FERI) algorithm for fair predictions of graft failure risk in liver transplant patients. FERI constrains subgroup loss by balancing learning rates and preventing subgroup dominance in the training process. Our results show that FERI maintained high predictive accuracy with AUROC and AUPRC comparable to baseline models. More importantly, FERI demonstrated an ability to improve fairness without sacrificing accuracy. Specifically, for the gender, FERI reduced the demographic parity disparity by 71.74%, and for the age group, it decreased the equalized odds disparity by 40.46%. Therefore, the FERI algorithm advanced fairness-aware predictive modeling in healthcare and provides an invaluable tool for equitable healthcare systems.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200786","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}
引用次数: 0
Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research 牙科研究的对比语言图像检索搜索
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-27 DOI: 10.48550/arXiv.2306.15651
Tanjida Kabir, Luyao Chen, M. Walji, L. Giancardo, Xiaoqian Jiang, Shayan Shams
{"title":"Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research","authors":"Tanjida Kabir, Luyao Chen, M. Walji, L. Giancardo, Xiaoqian Jiang, Shayan Shams","doi":"10.48550/arXiv.2306.15651","DOIUrl":"https://doi.org/10.48550/arXiv.2306.15651","url":null,"abstract":"Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, utilizes periapical radiographs and associated clinical details such as periodontal diagnosis, demographic information to retrieve the best-matched images based on the text query. We applied a contrastive representation learning method to find images described by the user's text by maximizing the similarity score of positive pairs (true pairs) and minimizing the score of negative pairs (random pairs). Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We also designed a graphical user interface that allows researchers to verify the model's performance with interactions.","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80494339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparing Amyloid Imaging Normalization Strategies for Alzheimer's Disease Classification using an Automated Machine Learning Pipeline. 使用自动机器学习管道比较阿尔茨海默病分类的淀粉样蛋白成像归一化策略。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01
Boning Tong, Shannon L Risacher, Jingxuan Bao, Yanbo Feng, Xinkai Wang, Marylyn D Ritchie, Jason H Moore, Ryan Urbanowicz, Andrew J Saykin, Li Shen
{"title":"Comparing Amyloid Imaging Normalization Strategies for Alzheimer's Disease Classification using an Automated Machine Learning Pipeline.","authors":"Boning Tong, Shannon L Risacher, Jingxuan Bao, Yanbo Feng, Xinkai Wang, Marylyn D Ritchie, Jason H Moore, Ryan Urbanowicz, Andrew J Saykin, Li Shen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Amyloid imaging has been widely used in Alzheimer's disease (AD) diagnosis and biomarker discovery through detecting the regional amyloid plaque density. It is essential to be normalized by a reference region to reduce noise and artifacts. To explore an optimal normalization strategy, we employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the AD diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models. In this work, we perform a comparative study to evaluate the prediction performance and biomarker discovery capability of three amyloid imaging measures, including one original measure and two normalized measures using two reference regions (i.e., the whole cerebellum and the composite reference region). Our AutoML results indicate that the composite reference region normalization dataset yields a higher balanced accuracy, and identifies more AD-related regions based on the fractioned feature importance ranking.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283108/pdf/2306.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9711834","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}
引用次数: 0
Developing an LSTM Model to Identify Surgical Site Infections using Electronic Healthcare Records. 开发 LSTM 模型，利用电子医疗记录识别手术部位感染。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01
Amber C Kiser, Karen Eilbeck, Brian T Bucher
{"title":"Developing an LSTM Model to Identify Surgical Site Infections using Electronic Healthcare Records.","authors":"Amber C Kiser, Karen Eilbeck, Brian T Bucher","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Recently, hospitals and healthcare providers have made efforts to reduce surgical site infections as they are a major cause of surgical complications, a prominent reason for hospital readmission, and associated with significantly increased healthcare costs. Traditional surveillance methods for SSI rely on manual chart review, which can be laborious and costly. To assist the chart review process, we developed a long short-term memory (LSTM) model using structured electronic health record data to identify SSI. The top LSTM model resulted in an average precision (AP) of 0.570 [95% CI 0.567, 0.573] and area under the receiver operating characteristic curve (AUROC) of 0.905 [95% CI 0.904, 0.906] compared to the top traditional machine learning model, a random forest, which achieved 0.552 [95% CI 0.549, 0.555] AP and 0.899 [95% CI 0.898, 0.900] AUROC. Our LSTM model represents a step toward automated surveillance of SSIs, a critical component of quality improvement mechanisms.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283140/pdf/2161.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9711839","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}
引用次数: 0
Generalizing through Forgetting - Domain Generalization for Symptom Event Extraction in Clinical Notes. 在遗忘中归纳--临床笔记中症状事件提取的领域归纳。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01
Sitong Zhou, Kevin Lybarger, Meliha Yetisgen, Mari Ostendorf
{"title":"Generalizing through Forgetting - Domain Generalization for Symptom Event Extraction in Clinical Notes.","authors":"Sitong Zhou, Kevin Lybarger, Meliha Yetisgen, Mari Ostendorf","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extraction approaches that can handle clinical language variation across different institutions and specialties are needed. In this paper, we present domain generalization for symptom extraction using pretraining and fine-tuning data that differs from the target domain in terms of institution and/or specialty and patient population. We extract symptom events using a transformer-based joint entity and relation extraction method. To reduce reliance on domain-specific features, we propose a domain generalization method that dynamically masks frequent symptoms words in the source domain. Additionally, we pretrain the transformer language model (LM) on task-related unlabeled texts for better representation. Our experiments indicate that masking and adaptive pretraining methods can significantly improve performance when the source domain is more distant from the target domain.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283109/pdf/2329.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9715630","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}
引用次数: 0
Understanding Barriers to the Collection of Mobile and Wearable Device Data to Monitor Health and Cognition in Older Adults: A Scoping Review. 了解收集移动和可穿戴设备数据以监测老年人健康和认知情况的障碍：范围审查》。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01
Ibukun E Fowe, Edie C Sanders, Walter R Boot
{"title":"Understanding Barriers to the Collection of Mobile and Wearable Device Data to Monitor Health and Cognition in Older Adults: A Scoping Review.","authors":"Ibukun E Fowe, Edie C Sanders, Walter R Boot","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Advances in technology have made continuous/remote monitoring of digital health data possible, which can enable the early detection and treatment of age-related cognitive and health declines. Using Arksey and O'Malley's methodology, this scoping review evaluated potential barriers to the collection of mobile and wearable device data to monitor health and cognitive status in older adults with and without mild cognitive impairment (MCI). Selected articles were US based and focused on experienced or perceived barriers to the collection of mobile and wearable device data by adults 55 years of age or older. Fourteen articles met the study's inclusion criteria. Identified themes included barriers related to usability, users' prior experiences with health technologies, first and second level digital divide, aesthetics, comfort, adherence, and attitudinal barriers. Addressing these barriers will be crucial for effective digital data-collection among older adults to achieve goals of improving quality of life and reducing care costs.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283138/pdf/2117.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9712655","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}
引用次数: 0
Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research. 牙科 CLAIRES：用于牙科研究的对比性图像检索（Contrastive LAnguage Image REtrieval Search）。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01
Tanjida Kabir, Luyao Chen, Muhammad F Walji, Luca Giancardo, Xiaoqian Jiang, Shayan Shams
{"title":"Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research.","authors":"Tanjida Kabir, Luyao Chen, Muhammad F Walji, Luca Giancardo, Xiaoqian Jiang, Shayan Shams","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework, <b>C</b>ontrastive <b>LA</b>nguage <b>I</b>mage <b>RE</b>trieval <b>S</b>earch for dental research, Dental CLAIRES, utilizes periapical radiographs and associated clinical details such as periodontal diagnosis, demographic information to retrieve the best-matched images based on the text query. We applied a contrastive representation learning method to find images described by the user's text by maximizing the similarity score of positive pairs (true pairs) and minimizing the score of negative pairs (random pairs). Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We also designed a graphical user interface that allows researchers to verify the model's performance with interactions.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283104/pdf/2343.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10070913","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}
引用次数: 0
Investigating Three Classification Methods for Per/Poly-Fluoroalkyl Substance (PFAS) Exposure from Electronic Health Records And Potential for Bias. 研究电子健康记录中全氟/多氟烷基物质(PFAS)暴露的三种分类方法及其潜在的偏差。
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Pub Date : 2023-06-16 eCollection Date: 2023-01-01
Lena M Davidson, Mary Regina Boland
{"title":"Investigating Three Classification Methods for Per/Poly-Fluoroalkyl Substance (PFAS) Exposure from Electronic Health Records And Potential for Bias.","authors":"Lena M Davidson, Mary Regina Boland","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Per-/poly-fluoroalkyl substances (PFAS) are a group of manmade compounds with known human toxicity and evidence of contamination in drinking water throughout the US. We augmented our electronic health record data with geospatial information to classify PFAS exposure for our patients living in New Jersey. We explored the utility of three different methods for classifying PFAS exposure that are popularly used in the literature, resulting in different boundary types: public water supplier service area boundary, municipality, and ZIP code. We also explored the intersection of the three boundaries. To study the potential for bias, we investigated known PFAS exposure-disease associations, specifically hypertension, thyroid disease and parathyroid disease. We found that both the significance of the associations and the effect size varied by the method for classifying PFAS exposure. This has important implications in knowledge discovery and also environmental justice as across cohorts, we found a larger proportion of Black/African-American patients PFAS-exposed.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283112/pdf/2417.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9712654","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}
引用次数: 0
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