Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing最新文献_第2页

A Comprehensive Bibliometric Analysis: Celebrating the Thirtieth Anniversary of the Pacific Symposium on Biocomputing. 综合文献计量学分析：庆祝太平洋生物计算研讨会三十周年。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2025-01-01 DOI: 10.1142/9789819807024_0001

Rachit Kumar, Rasika Venkatesh, David Y Zhang, Teri E Klein, Marylyn D Ritchie

引用次数: 0

Using Large Language Models for Efficient Cancer Registry Coding in the Real Hospital Setting: A Feasibility Study. 在真实医院环境中使用大型语言模型进行有效的癌症登记编码：可行性研究。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2025-01-01 DOI: 10.1142/9789819807024_0010

Chen-Kai Wang, Cheng-Rong Ke, Ming-Siang Huang, Inn-Wen Chong, Yi-Hsin Yang, Vincent S Tseng, Hong-Jie Dai

{"title":"Using Large Language Models for Efficient Cancer Registry Coding in the Real Hospital Setting: A Feasibility Study.","authors":"Chen-Kai Wang, Cheng-Rong Ke, Ming-Siang Huang, Inn-Wen Chong, Yi-Hsin Yang, Vincent S Tseng, Hong-Jie Dai","doi":"10.1142/9789819807024_0010","DOIUrl":"10.1142/9789819807024_0010","url":null,"abstract":"The primary challenge in reporting cancer cases lies in the labor-intensive and time-consuming process of manually reviewing numerous reports. Current methods predominantly rely on rule-based approaches or custom-supervised learning models, which predict diagnostic codes based on a single pathology report per patient. Although these methods show promising evaluation results, their biased outcomes in controlled settings may hinder adaption to real-world reporting workflows. In this feasibility study, we focused on lung cancer as a test case and developed an agentic retrieval-augmented generation (RAG) system to evaluate the potential of publicly available large language models (LLMs) for cancer registry coding. Our findings demonstrate that: (1) directly applying publicly available LLMs without fine-tuning is feasible for cancer registry coding; and (2) prompt engineering can significantly enhance the capability of pre-trained LLMs in cancer registry coding. The off-the-shelf LLM, combined with our proposed system architecture and basic prompts, achieved a macro-averaged F-score of 0.637 when evaluated on testing data consisting of patients' medical reports spanning 1.5 years since their first visit. By employing chain of thought (CoT) reasoning and our proposed coding item grouping, the system outperformed the baseline by 0.187 in terms of the macro-averaged F-score. These findings demonstrate the great potential of leveraging LLMs with prompt engineering for cancer registry coding. Our system could offer cancer registrars a promising reference tool to enhance their daily workflow, improving efficiency and accuracy in cancer case reporting.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"121-137"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819409","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

Command line to pipeLine: Cross-biobank analyses with Nextflow. 命令行到管道：跨生物银行分析与Nextflow。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2025-01-01 DOI: 10.1142/9789819807024_0050

Anurag Verma, Zachary Rodriguez, Lindsay Guare, Katie Cardone, Christopher Carson

引用次数: 0

Electronic Health Record Analysis for Personalized Medicine: Predicting Malnutrition-Related Health Outcomes and Secondary Neuropsychiatric Health Concerns. 用于个性化医疗的电子健康记录分析：预测与营养不良相关的健康结果和继发性神经精神健康问题。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2025-01-01 DOI: 10.1142/9789819807024_0043

Pinar Gurkas, Gunnur Karakurt

{"title":"Electronic Health Record Analysis for Personalized Medicine: Predicting Malnutrition-Related Health Outcomes and Secondary Neuropsychiatric Health Concerns.","authors":"Pinar Gurkas, Gunnur Karakurt","doi":"10.1142/9789819807024_0043","DOIUrl":"10.1142/9789819807024_0043","url":null,"abstract":"Malnutrition poses risks regarding cognitive, behavioral, and physical well-being. The aim of this study was to investigate the prevalent health issues associated with malnutrition by utilizing electronic health records (EHR) data. The IBM Watson Health, Explorys platform was used to access the EHR data. Two cohorts were created by two queries; patients with a history of malnutrition (n=5180) and patients without a history of malnutrition diagnosis (n= 413890). The log odds ratio and χ2 statistic were used to identify the statistically significant differences between these two cohorts. We found that there were 35 terms that were more common among the cohort with the malnutrition diagnosis. These terms were categorized under developmental anomalies, infectious agents, respiratory system issues, digestive system issues, pregnancy/prenatal problems, mental, behavioral, or neurodevelopmental disorders, diseases of the ear or mastoid process, diseases of the visual system, and chromosomal anomalies. The management of malnutrition in children is a complex problem that can be addressed with a multifactorial approach. Based on the key themes emerging from among the commonly prevalent terms identified in our study, infection prevention, education in appropriate nutritional solutions for digestive health issues, supportive services to address neurodevelopmental needs, and quality prenatal healthcare would constitute beneficial prevention efforts. Improving our understanding of malnutrition is necessary to develop new interventions for prevention and treatment.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"599-613"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11649008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819518","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

Artificial Allies: Validation of Synthetic Text for Peer Support Tools through Data Augmentation in NLP Model Development. 人工盟友：通过NLP模型开发中的数据增强来验证同伴支持工具的合成文本。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2025-01-01 DOI: 10.1142/9789819807024_0008

Josué Godeme, Julia Hill, Stephen P Gaughan, Wade J Hirschbuhl, Amanda J Emerson, Christian Darabos, Carly A Bobak, Karen L Fortuna

{"title":"Artificial Allies: Validation of Synthetic Text for Peer Support Tools through Data Augmentation in NLP Model Development.","authors":"Josué Godeme, Julia Hill, Stephen P Gaughan, Wade J Hirschbuhl, Amanda J Emerson, Christian Darabos, Carly A Bobak, Karen L Fortuna","doi":"10.1142/9789819807024_0008","DOIUrl":"10.1142/9789819807024_0008","url":null,"abstract":"This study investigates the potential of using synthetic text to augment training data for Natural Language Processing (NLP) models, specifically within the context of peer support tools. We surveyed 22 participants-13 professional peer supporters and 9 AI-proficient individuals-tasked with distinguishing between AI-generated and human-written sentences. Using signal detection theory and confidence-based metrics, we evaluated the accuracy and confidence levels of both groups. The results show no significant differences in rater agreement between the two groups (p = 0.116), with overall classification accuracy falling below chance levels (mean accuracy = 43.10%, p < 0.001). Both groups exhibited a tendency to misclassify low-fidelity sentences as AI-generated, with peer supporters showing a significant bias (p = 0.007). Further analysis revealed a significant negative correlation between errors and confidence among AI-proficient raters (r = -0.429, p < 0.001), suggesting that as their confidence increased, their error rates decreased. Our findings support the feasibility of using synthetic text to mimic human communication, with important implications for improving the fidelity of peer support interventions through NLP model development.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"94-108"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819377","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

Social risk factors and cardiovascular risk in obstructive sleep apnea: a systematic assessment of clinical predictors in community health centers. 阻塞性睡眠呼吸暂停患者的社会风险因素和心血管风险：社区卫生中心临床预测因素的系统评估

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2025-01-01 DOI: 10.1142/9789819807024_0023

Diego R Mazzotti, Ryan Urbanowicz, Marta Jankowska

{"title":"Social risk factors and cardiovascular risk in obstructive sleep apnea: a systematic assessment of clinical predictors in community health centers.","authors":"Diego R Mazzotti, Ryan Urbanowicz, Marta Jankowska","doi":"10.1142/9789819807024_0023","DOIUrl":"10.1142/9789819807024_0023","url":null,"abstract":"We leveraged electronic health record (EHR) data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Research Network (CRN) to identify social risk factor clusters, assess their association with obstructive sleep apnea (OSA), and determine relevant clinical predictors of cardiovascular (CV) outcomes among those experiencing OSA. Geographically informed social indicators were used to define social risk factor clusters via latent class analysis. EHR-wide diagnoses were used as predictors of 5-year incidence of major adverse CV events (MACE) using STREAMLINE, an end-to-end rigorous and interpretable automated machine learning pipeline. Analyses among over 1.4 million individuals revealed three major social risk factor clusters: lowest (35.7%), average (43.6%) and highest (22.7%) social burden. In adjusted analyses, those experiencing highest social burden were less likely to have received a diagnosis of OSA when compared to those experiencing lowest social burden (OR [95%CI]=0.85[0.82-0.88]). Among those with OSA and free of prior CV diseases (N=4,405), performance of predicting incident MACE reached a ROC-AUC of 0.70 [0.03] overall but varied when assessed within each social risk factor cluster. Feature importance also revealed that different clinical factors might explain predictions among each cluster. Results suggest relevant health disparities in the diagnosis of OSA and across clinical predictors of CV diseases among those with OSA, across social risk factor clusters, indicating that tailored interventions geared toward minimizing these disparities are warranted.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"314-329"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819274","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

Session Introduction: Translating Big Data Imaging Genomics Findings to the Individual: Prediction of Risks and Outcomes in Neuropsychiatric Illnesses. 会议简介：将大数据成像基因组学研究成果转化为个人数据：预测神经精神疾病的风险和结果。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2025-01-01 DOI: 10.1142/9789819807024_0042

Peter Kochunov, Li Shen, Zhongming Zhao, Paul M Thompson

引用次数: 0

One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Data. 关注：生物医学数据的可扩展多模态集成。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2025-01-01 DOI: 10.1142/9789819807024_0041

Michal Golovanevsky, Eva Schiller, Akira Nair, Eric Han, Ritambhara Singh, Carsten Eickhoff

{"title":"One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Data.","authors":"Michal Golovanevsky, Eva Schiller, Akira Nair, Eric Han, Ritambhara Singh, Carsten Eickhoff","doi":"10.1142/9789819807024_0041","DOIUrl":"10.1142/9789819807024_0041","url":null,"abstract":"Multimodal models have become increasingly important as they surpass single-modality approaches on diverse tasks ranging from question-answering to disease diagnosis. Despite the importance of multimodal learning, existing efforts focus on vision-language applications, where the number of modalities rarely exceeds four (images, text, audio, video). However, data in healthcare domain, may include many more modalities like X-rays, PET scans, MRIs, genetic screening, genomic data, and clinical notes, creating a need for both efficient and accurate data integration. Many state-of-the-art multimodal models rely on cross-attention or self-attention for effective data integration, which do not scale well for applications with more than two modalities. The complexity per layer of computing attention in either paradigm is, at best, quadratic with respect to the number of modalities, posing a computational bottleneck that impedes broad adoption. To address this, we propose a new attention mechanism, One-Versus-Others (OvO) attention, that scales linearly with the number of modalities, thus offering a significant reduction in computational complexity compared to existing multimodal attention methods. Using three clinical datasets with multiple diverse modalities, we show that our method decreases computation costs while maintaining or increasing performance compared to popular integration techniques. Across all clinical datasets, OvO reduced the number of required floating point operations (FLOPs) by at least 91.98%, demonstrating its significant impact on efficiency and enabling multi-modal predictions in healthcare.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"580-593"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819622","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

LLM-CGM: A Benchmark for Large Language Model-Enabled Querying of Continuous Glucose Monitoring Data for Conversational Diabetes Management. LLM-CGM：大型语言模型支持的连续葡萄糖监测数据查询基准，用于对话式糖尿病管理。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2025-01-01 DOI: 10.1142/9789819807024_0007

Elizabeth Healey, Isaac Kohane

{"title":"LLM-CGM: A Benchmark for Large Language Model-Enabled Querying of Continuous Glucose Monitoring Data for Conversational Diabetes Management.","authors":"Elizabeth Healey, Isaac Kohane","doi":"10.1142/9789819807024_0007","DOIUrl":"10.1142/9789819807024_0007","url":null,"abstract":"Over the past decade, wearable technology has dramatically changed how patients manage chronic diseases. The widespread availability of on-body sensors, such as heart rate monitors and continuous glucose monitoring (CGM) sensors, has allowed patients to have real-time data about their health. Most of these data are readily available on patients' smartphone applications, where patients can view their current and retrospective data. For patients with diabetes, CGM has transformed how their disease is managed. Many sensor devices interface with smartphones to display charts, metrics, and alerts. However, these metrics and plots may be challenging for some patients to interpret. In this work, we explore how large language models (LLMs) can be used to answer questions about CGM data. We produce an open-source benchmark of time-series question-answering tasks for CGM data in diabetes management. We evaluate different LLM frameworks to provide a performance benchmark. Lastly, we highlight the need for more research on how to optimize LLM frameworks to best handle questions about wearable data. Our benchmark is publicly available for future use and development. While this benchmark is specifically designed for diabetes care, our model implementation and several of the statistical tasks can be extended to other wearable device domains.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"82-93"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819620","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

Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers. 评估ASD生物标志物的无监督降维技术。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2025-01-01 DOI: 10.1142/9789819807024_0044

Zachary Jacokes, Ian Adoremos, Arham Rameez Hussain, Benjamin T Newman, Kevin A Pelphrey, John Darrell Van Horn

{"title":"Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.","authors":"Zachary Jacokes, Ian Adoremos, Arham Rameez Hussain, Benjamin T Newman, Kevin A Pelphrey, John Darrell Van Horn","doi":"10.1142/9789819807024_0044","DOIUrl":"10.1142/9789819807024_0044","url":null,"abstract":"Autism Spectrum Disorder (ASD) encompasses a range of developmental disabilities marked by differences in social functioning, cognition, and behavior. Both genetic and environmental factors are known to contribute to ASD, yet the exact etiological factors remain unclear. Developing integrative models to explore the effects of gene expression on behavioral and cognitive traits attributed to ASD can uncover environmental and genetic interactions. A notable aspect of ASD research is the sex-wise diagnostic disparity: males are diagnosed more frequently than females, which suggests potential sex-specific biological influences. Investigating neuronal microstructure, particularly axonal conduction velocity offers insights into the neural basis of ASD. Developing robust models that evaluate the vast multidimensional datasets generated from genetic and microstructural processing poses significant challenges. Traditional feature selection techniques have limitations; thus, this research aims to integrate principal component analysis (PCA) with supervised machine learning algorithms to navigate the complex data space. By leveraging various neuroimaging techniques and transcriptomics data analysis methods, this methodology builds on traditional implementations of PCA to better contextualize the complex genetic and phenotypic heterogeneity linked to sex differences in ASD and pave the way for tailored interventions.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"614-630"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819395","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