Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics最新文献_第8页

DeepVS

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545554

Zongxing Xie, Hanrui Wang, Song Han, Elinor R. Schoenfeld, Fan Ye

{"title":"DeepVS","authors":"Zongxing Xie, Hanrui Wang, Song Han, Elinor R. Schoenfeld, Fan Ye","doi":"10.1145/3535508.3545554","DOIUrl":"https://doi.org/10.1145/3535508.3545554","url":null,"abstract":"Vital signs (e.g., heart and respiratory rate) are indicative for health status assessment. Efforts have been made to extract vital signs using radio frequency (RF) techniques (e.g., Wi-Fi, FMCW, UWB), which offer a non-touch solution for continuous and ubiquitous monitoring without users' cooperative efforts. While RF-based vital signs monitoring is user-friendly, its robustness faces two challenges. On the one hand, the RF signal is modulated by the periodic chest wall displacement due to heartbeat and breathing in a nonlinear manner. It is inherently hard to identify the fundamental heart and respiratory rates (HR and RR) in the presence of higher order harmonics of them and intermodulation between HR and RR, especially when they have overlapping frequency bands. On the other hand, the inadvertent body movements may disturb and distort the RF signal, overwhelming the vital signals, thus inhibiting the parameter estimation of the physiological movement (i.e., heartbeat and breathing). In this paper, we propose DeepVS, a deep learning approach that addresses the aforementioned challenges from the non-linearity and inadvertent movements for robust RF-based vital signs sensing in a unified manner. DeepVS combines 1D CNN and attention models to exploit local features and temporal correlations. Moreover, it leverages a two-stream scheme to integrate features from both time and frequency domains. Additionally, DeepVS unifies the estimation of HR and RR with a multi-head structure, which only adds limited extra overhead (<1%) to the existing model, compared to doubling the overhead using two separate models for HR and RR respectively. Our experiments demonstrate that DeepVS achieves 80-percentile HR/RR errors of 7.4/4.9 beat/breaths per minute (bpm) on a challenging dataset, as compared to 11.8/7.3 bpm of a non-learning solution. Besides, an ablation study has been conducted to quantify the effectiveness of DeepVS.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125154202","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: 1

Identification of COVID-19 severity and associated genetic biomarkers based on scRNA-seq data 基于scRNA-seq数据的COVID-19严重程度和相关遗传生物标志物鉴定

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545519

Aekansh Goel, Z. Mudge, Sarah Bi, C. Brenner, Nicholas Huffman, F. Giuste, Benoit Marteau, Wenqi Shi, May D. Wang

引用次数: 2

Session details: Health monitoring & phenotyping 会议细节:健康监测和表型分析

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3552473

Zongxing Xie

引用次数: 0

An Italian lexicon-based sentiment analysis approach for medical applications 基于意大利语词典的医疗应用情感分析方法

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545594

Maria Chiara Martinis, C. Zucco, M. Cannataro

引用次数: 2

Session details: Machine learning & drug design 会议细节:机器学习和药物设计

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3552475

Amine M. Remita

引用次数: 0

Computational framework for generating synthetic signal peptides 合成信号肽的计算框架

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545530

T. Johnsten, Aishwarya Prakash, G. Daly, Ryan G. Benton, Tristan Clark

引用次数: 0

Modeling long-term dependencies and short-term correlations in patient journey data with temporal attention networks for health prediction 用时间注意力网络对病人旅程数据进行长期依赖关系和短期相关性建模，用于健康预测

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-07-13 DOI: 10.1145/3535508.3545535

Yuxi Liu, Zhenhao Zhang, A. Yepes, Flora D. Salim

{"title":"Modeling long-term dependencies and short-term correlations in patient journey data with temporal attention networks for health prediction","authors":"Yuxi Liu, Zhenhao Zhang, A. Yepes, Flora D. Salim","doi":"10.1145/3535508.3545535","DOIUrl":"https://doi.org/10.1145/3535508.3545535","url":null,"abstract":"Building models for health prediction based on Electronic Health Records (EHR) has become an active research area. EHR patient journey data consists of patient time-ordered clinical events/visits from patients. Most existing studies focus on modeling long-term dependencies between visits, without explicitly taking short-term correlations between consecutive visits into account, where irregular time intervals, incorporated as auxiliary information, are fed into health prediction models to capture latent progressive patterns of patient journeys. We present a novel deep neural network with four modules to take into account the contributions of various variables for health prediction: i) the Stacked Attention module strengthens the deep semantics in clinical events within each patient journey and generates visit embeddings, ii) the Short-Term Temporal Attention module models short-term correlations between consecutive visit embeddings while capturing the impact of time intervals within those visit embeddings, iii) the Long-Term Temporal Attention module models long-term dependencies between visit embeddings while capturing the impact of time intervals within those visit embeddings, iv) and finally, the Coupled Attention module adaptively aggregates the outputs of Short-Term Temporal Attention and Long-Term Temporal Attention modules to make health predictions. Experimental results on MIMIC-III demonstrate superior predictive accuracy of our model compared to existing state-of-the-art methods, as well as the interpretability and robustness of this approach. Furthermore, we found that modeling short-term correlations contributes to local priors generation, leading to improved predictive modeling of patient journeys.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128737555","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: 4

Distribution-based sketching of single-cell samples 基于分布的单细胞样品绘制

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-06-30 DOI: 10.1145/3535508.3545539

Vishal Baskaran, Jolene S Ranek, Siyuan Shan, N. Stanley, Junier B. Oliva

{"title":"Distribution-based sketching of single-cell samples","authors":"Vishal Baskaran, Jolene S Ranek, Siyuan Shan, N. Stanley, Junier B. Oliva","doi":"10.1145/3535508.3545539","DOIUrl":"https://doi.org/10.1145/3535508.3545539","url":null,"abstract":"Modern high-throughput single-cell immune profiling technologies, such as flow and mass cytometry and single-cell RNA sequencing can readily measure the expression of a large number of protein or gene features across the millions of cells in a multi-patient cohort. While bioinformatics approaches can be used to link immune cell heterogeneity to external variables of interest, such as, clinical outcome or experimental label, they often struggle to accommodate such a large number of profiled cells. To ease this computational burden, a limited number of cells are typically sketched or subsampled from each patient. However, existing sketching approaches fail to adequately subsample rare cells from rare cell-populations, or fail to preserve the true frequencies of particular immune cell-types. Here, we propose a novel sketching approach based on Kernel Herding that selects a limited subsample of all cells while preserving the underlying frequencies of immune cell-types. We tested our approach on three flow and mass cytometry datasets and on one single-cell RNA sequencing dataset and demonstrate that the sketched cells (1) more accurately represent the overall cellular landscape and (2) facilitate increased performance in downstream analysis tasks, such as classifying patients according to their clinical outcome. An implementation of sketching with Kernel Herding is publicly available at https://github.com/vishalathreya/Set-Summarization.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125175036","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: 8

Predicting the need for blood transfusion in intensive care units with reinforcement learning 用强化学习预测重症监护病房的输血需求

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-06-26 DOI: 10.1145/3535508.3545523

Yuqing Wang, Yun Zhao, Linda Petzold

{"title":"Predicting the need for blood transfusion in intensive care units with reinforcement learning","authors":"Yuqing Wang, Yun Zhao, Linda Petzold","doi":"10.1145/3535508.3545523","DOIUrl":"https://doi.org/10.1145/3535508.3545523","url":null,"abstract":"As critically ill patients frequently develop anemia or coagulopathy, transfusion of blood products is a frequent intervention in the Intensive Care Units (ICU). However, inappropriate transfusion decisions made by physicians are often associated with increased risk of complications and higher hospital costs. In this work, we aim to develop a decision support tool that uses available patient information for transfusion decision-making on three common blood products (red blood cells, platelets, and fresh frozen plasma). To this end, we adopt an off-policy batch reinforcement learning (RL) algorithm, namely, discretized Batch Constrained Q-learning, to determine the best action (transfusion or not) given observed patient trajectories. Simultaneously, we consider different state representation approaches and reward design mechanisms to evaluate their impacts on policy learning. Experiments are conducted on two real-world critical care datasets: the MIMIC-III and the UCSF. Results demonstrate that policy recommendations on transfusion achieved comparable matching against true hospital policies via accuracy and weighted importance sampling evaluations on the MIMIC-III dataset. Furthermore, a combination of transfer learning (TL) and RL on the data-scarce UCSF dataset can provide up to 17.02% improvement in terms of accuracy, and up to 18.94% and 21.63% improvement in jump-start and asymptotic performance in terms of weighted importance sampling averaged over three transfusion tasks. Finally, simulations on transfusion decisions suggest that the transferred RL policy could reduce patients' estimated 28-day mortality rate by 2.74% and decreased acuity rate by 1.18% on the UCSF dataset. In short, RL with appropriate patient state encoding and reward designs shows promise in treatment recommendations for blood transfusion and further optimizes the real-time treatment strategies by improving patients' clinical outcomes.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125898049","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: 1

Contextual embedding and model weighting by fusing domain knowledge on biomedical question answering 基于领域知识融合的生物医学问答情境嵌入与模型加权

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-06-26 DOI: 10.1145/3535508.3545508

Yuxuan Lu, Jingya Yan, Zhixuan Qi, Zhongzheng Ge, Yongping Du

引用次数: 0