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23rd Meeting of the ACL Special Interest Group on biomedical Natural Language Processing, BioNLP 2024

作者： Socrates, Vimig Huang, Thomas Ai, Xuguang Fereydooni, Soraya Chen, Qingyu Taylor, R. Andrew Chartash, David Department of Biomedical Informatics and Data Science Yale University School of Medicine United States Department of Emergency Medicine Yale School of Medicine United States Program of Computational Biology and Bioinformatics Yale University United States School of Medicine University College Dublin National University of Ireland Dublin Ireland

ISBN: (纸本)9798891761308

In this work, we propose our top-ranking (2nd place) pipeline for the generation of discharge summary subsections as a part of the BioNLP 2024 Shared Task 2: "Discharge Me!". We evaluate both encoder-decoder and state-of-the-art decoder-only language models on the generation of two key sections of the discharge summary. To evaluate the ability of NLP methods to further alleviate the documentation burden on physicians, we also design a novel pipeline to generate the brief hospital course directly from structured information found in the EHR. Finally, we evaluate a constrained beam search approach to inject external knowledge about relevant patient problems into the text generation process. We find that a BioBART model fine-tuned on a larger fraction of the data without constrained beam search outperforms all other models.. ©2024 Association for Computational Linguistics.

关键词： Decoding

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Cardiovascular risk of gonadotropin-releasing hormone antagonist versus agonist in men with prostate cancer: an observational study in Taiwan (Jun, 10.1038/s41391-022-00555-0, 2022)

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PROSTATE CANCER AND PROSTATIC DISEASES 2023年第3期26卷 631-632页

作者： Shao, Yu-Hsuan Joni Hong, Jian-Hua Chen, Chun-Kai Huang, Chao-Yuan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Department of Urology National Taiwan University Hospital College of Medicine National Taiwan University Taipei Taiwan Institute of Biomedical Engineering National Taiwan University Taipei Taiwan Division of Cardiology Department of Internal Medicine National Taiwan University Hospital Hsinchu Branch Hsinchu Taiwan

Background The impact of gonadotropin-releasing hormone (GnRH) antagonist and agonist (GnRHa) treatment on cardiovascular disease (CVD) risk in prostate cancer (PCa) remains inconclusive due to conflicting findings. We compared the effects of GnRH antagonist and GnRHa treatments on CVD risk in patients with PCa and pre-existing CVD, in a Taiwan population-based *** We assessed the risk of major adverse CV events (MACE: ischemic heart disease [IHD], stroke, congestive heart failure [CHF] or all cause deaths) and composite CV events (IHD, stroke, CHF or CV deaths) occurring ≥90 days after androgen deprivation therapy (ADT) initiation in patients with PCa after 90 days of treatment with either GnRH antagonist (degarelix; n = 499) or GnRHa (goserelin, leuprolide, triptorelin; n = 15,127). Patients identified with pre-existing CVD had received cardiac therapy for IHD, reported a stroke or CHF within a year before ADT initiation. Adjusted hazard ratios (aHR) and 95% confidence interval (CI) were obtained for MACE and composite CV events risk after adjusting for age, baseline status of diabetes, hypertension and treatments *** All GnRH antagonist-treated patients showed lower risk of composite CV events than the GnRHa-treated patients. The lower composite CV events risk associated with GnRH antagonist was also observed in patients with metastasis at diagnosis (aHR 0.16; 95% CI, 0.04–0.38; p = 0.013) and those receiving ADT for more than six months (aHR 0.30; 95% CI, 0.16–0.54; p < 0.0001). In patients with pre-existing CVD, the MACE risk was 33% lower (aHR 0.67; 95% CI, 0.46–0.96; p = 0.0299) and composite CV events risk was 84% lower (aHR 0.16; 95% CI, 0.05–0.50; p = 0.0017) in GnRH antagonist-treated than the GnRHa-treated *** In patients with PCa and pre-existing CVD, GnRH antagonist use was associated with lower risks for composite CV events and MACE compared with GnRHa.

关键词： Cancer therapy Prostate cancer

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HIGH NOISE SCHEDULING IS A MUST

arXiv

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arXiv 2024年

作者： Gokmen, Mahmut S. Bumgardner, Cody Zhang, Jie Wang, Ge Chen, Jin Department of Computer Science University of Kentucky United States Department of Internal Medicine Institute for Biomedical Informatics University of Kentucky United States Department of Radiology University of Kentucky United States Department of Biomedical Engineering Rensselaer Polytechnic Institute United States Department of Medicine Department of Biomedical Informatics and Data Science University of Alabama Birmingham United States

Consistency models possess high capabilities for image generation, advancing sampling steps to a single step through their advanced techniques. Current advancements move one step forward consistency training techniques and eliminates the limitation of distillation training. Even though the proposed curriculum and noise scheduling in improved training techniques yield better results than basic consistency models, it lacks well balanced noise distribution and its consistency between curriculum. In this study, it is investigated the balance between high and low noise levels in noise distribution and offered polynomial noise distribution to maintain the stability. This proposed polynomial noise distribution is also supported with a predefined Karras noises to prevent unique noise levels arises with Karras noise generation algorithm. Furthermore, by elimination of learned noisy steps with a curriculum based on sinusoidal function increase the performance of the model in denoising. To make a fair comparison with the latest released consistency model training techniques, experiments are conducted with same hyper-parameters except curriculum and noise distribution. The models utilized during experiments are determined with low depth to prove the robustness of our proposed technique. The results show that the polynomial noise distribution outperforms the model trained with log-normal noise distribution, yielding a 33.54 FID score after 100,000 training steps with constant discretization steps. Additionally, the implementation of a sinusoidal-based curriculum enhances denoising performance, resulting in a FID score of 30.48. Copyright © 2024, The Authors. All rights reserved.

关键词： Polynomials

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ENHANCING LOW DOSE COMPUTED TOMOGRAPHY IMAGES USING CONSISTENCY TRAINING TECHNIQUES

arXiv

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arXiv 2024年

Diffusion models have significant impact on wide range of generative tasks, especially on image inpainting and restoration. Although the improvements on aiming for decreasing number of function evaluations (NFE), the iterative results are still computationally expensive. Consistency models are as a new family of generative models, enable single-step sampling of high quality data without the need for adversarial training. In this paper, we introduce the beta noise distribution, which provides flexibility in adjusting noise levels. This is combined with a sinusoidal curriculum that enhances the learning of the trajectory between the noise distribution and the posterior distribution of interest, allowing High Noise Improved Consistency Training (HN-iCT) to be trained in a supervised fashion. Additionally, High Noise Improved Consistency Training with Image Condition (HN-iCT-CN) architecture is introduced, enables to take Low Dose images as a condition for extracting significant features by Weighted Attention Gates (WAG). Our results indicate that unconditional image generation using HN-iCT significantly outperforms basic CT and iCT training techniques with NFE=1 on the CIFAR10 and CelebA datasets. Moreover, our image-conditioned model demonstrates exceptional performance in enhancing low-dose (LD) CT scans. Copyright © 2024, The Authors. All rights reserved.

关键词： Computerized tomography

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HAM: Hybrid Associations Models for Sequential Recommendation (Extended abstract)

HAM: Hybrid Associations Models for Sequential Recommendatio...

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International Conference on data Engineering

作者： Bo Peng Zhiyun Ren Srinivasan Parthasarathy Xia Ning Department of Computer Science and Engineering The Ohio State University Department of Biomedical Informatics The Ohio State University Department of Computer Science and Engineering Translational Data Analytics Institute The Ohio State University Department of Biomedical Informatics Department of Computer Science and Engineering Translational Data Analytics Institute The Ohio State University

Sequential recommendation aims to identify and recommend the next few items of users’ interest. It becomes an effective tool to help users select their favorite items from a variety of options. A key challenge in sequential recommendation is to learn the patterns and dynamics, which are most pertinent to inform future interactions of users. With the prosperity of deep learning, many deep models, particularly based on recurrent neural networks [1] and with attention mechanisms [2] , [3] , have been developed for sequential recommendation purposes. However, our analysis demonstrates that, these deep models, particularly those with attention mechanisms, may not always learn meaningful attention weights from the extremely sparse recommendation data, and thus, could degrade the recommendation performance. Therefore, in this study, instead of deep models, we develop novel, effective and efficient hybrid associations models (HAM) to better learn from the sparse and limited recommendation data. This study has been published in IEEE Transactions on Knowledge and data Engineering. Please refer to the full manuscript [4] for more details.

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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eCeLLM: generalizing large language models for E-commerce from large-scale, high-quality instruction data 24

eCeLLM: generalizing large language models for E-commerce fr...

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Proceedings of the 41st International Conference on Machine Learning

作者： Bo Peng Xinyi Ling Ziru Chen Huan Sun Xia Ning Department of Computer Science and Engineering The Ohio State University Department of Computer Science and Engineering and Translational Data Analytics Institute The Ohio State University Department of Computer Science and Engineering and Translational Data Analytics Institute and Department of Biomedical Informatics The Ohio State University

With tremendous efforts in developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products - a typical out-of-domain generalization challenge. Meanwhile, large language models (LLMs) demonstrate outstanding performance in generalist modeling and out-of-domain generalizability in many fields. Toward fully unleashing their power for e-commerce, in this paper, we construct ECInstruct, the first open-sourced, large-scale, and high-quality benchmark instruction dataset for e-commerce. Leveraging ECInstruct, we develop eCeLLM, a series of e-commerce LLMs, by instruction-tuning general-purpose LLMs. Our comprehensive experiments and evaluation demonstrate that eCeLLM models substantially outperform baseline models, including the most advanced GPT-4, and the state-of-the-art task-specific models in in-domain evaluation. Moreover, eCeLLM exhibits excellent generalizability to out-of-domain settings, including unseen products and unseen instructions, highlighting its superiority as a generalist e-commerce model. Both the ECInstruct dataset and the eCeLLM models show great potential in empowering versatile and effective LLMs for e-commerce. ECInstruct and eCeLLM models are publicly accessible through https://***/eCeLLM/.

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Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19:A Large-Scale Social Media Analysis with Natural Language Processing

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Health data science 2024年第1期4卷 152-161页

作者： Zhiyun Zhang Yining Hua Peilin Zhou Shixu Lin Minghui Li Yujie Zhang Li Zhou Yanhui Liao Jie Yang Department of Big Data in Health Science School of Public Health Zhejiang University School of MedicineHangzhouChina Department of Epidemiology Harvard T.H.Chan School of Public HealthBostonMAUSA Department of Biomedical Informatics Harvard Medical SchoolBostonMAUSA Division of General Internal Medicine and Primary Care Department of MedicineBrigham and Women’s HospitalBostonMAUSA Thrust of Data Science and Analytics The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina Department of Psychiatry Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina

Background:The COVID-19 pandemic has caused a disproportionate impact on the sexual and genderdiverse(SGD)*** with non-SGD populations,their social relations and health status are more vulnerable,whereas public health data regarding SGD are ***:To analyze the concerns and health status of SGD individuals,this cohort study leveraged 471,371,477 tweets from 251,455 SGD and 22,644,411 non-SGD users,spanning from 2020 February 1 to 2022 April *** outcome measures comprised the distribution and dynamics of COVID-related topics,attitudes toward vaccines,and the prevalence of ***:Topic analysis revealed that SGD users engaged more frequently in discussions related to“friends and family”(20.5%vs.13.1%,P<0.001)and“wear masks”(10.1%vs.8.3%,P<0.001)compared to non-SGD ***,SGD users exhibited a marked higher proportion of positive sentiment in tweets about vaccines,including Moderna,Pffzer,AstraZeneca,and Johnson&*** 102,464 users who self-reported COVID-19 diagnoses,SGD users disclosed signiffcantly higher frequencies of mentioning 61 out of 69 COVID-related symptoms than non-SGD users,encompassing both physical and mental health ***:The results provide insights into an understanding of the unique needs and experiences of the SGD community during the pandemic,emphasizing the value of social media data in epidemiological and public health research.

关键词： COV discussions users

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Modeling Path Importance for E_ective Alzheimer's Disease Drug Repurposing

Modeling Path Importance for E_ective Alzheimer's Disease Dr...

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29th Pacific Symposium on Biocomputing, PSB 2024

作者： Xiang, Shunian Lawrence, Patrick J. Peng, Bo Chiang, Chienwei Kim, Dokyoon Shen, Li Ning, Xia Biomedical Informatics Department The Ohio State University ColumbusOH43210 United States Computer Science and Engineering Department The Ohio State University ColumbusOH43210 United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA19104 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

ISBN: (纸本)9789811286414

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a networks rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies. © 2023 The Authors.

关键词： Embeddings

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Generating Synthetic Electronic Health Record (EHR) data: A Review with Benchmarking

arXiv

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arXiv 2024年

作者： Chen, Xingran Wu, Zhenke Shi, Xu Cho, Hyunghoon Mukherjee, Bhramar Department of Biostatistics University of Michigan United States Department of Biomedical Informatics and Data Science Yale University United States Department of Biostatistics Yale University United States

Objectives: To conduct a scoping review of existing approaches for synthetic Electronic Health Records (EHR) data generation, to benchmark major methods with proposed open-source software, and to offer recommendations for practitioners. Materials and Methods: We search three academic databases for our scoping review. Methods are benchmarked on open-source EHR datasets, Medical Information Mart for Intensive Care III and IV (MIMIC-III/IV). Seven existing methods covering major categories and two baseline methods are implemented and compared. Evaluation metrics concern data fidelity, downstream utility, privacy protection, and computational cost. Results: 42 studies are identified and classified into five categories. Seven open-source methods covering all categories are selected, trained on MIMIC-III, and evaluated on MIMIC-III or MIMIC-IV for transportability considerations. Among them, Generative Adversarial Network (GAN)-based methods demonstrate competitive performance in fidelity and utility on MIMIC-III;rule-based methods excel in privacy protection. Similar findings are observed on MIMIC-IV, except that GAN-based methods further outperform the baseline methods in preserving fidelity. A Python package, "SynthEHRella", is provided to integrate various choices of approaches and evaluation metrics, enabling more streamlined exploration and evaluation of multiple methods, which can be accessed at https://***/chenxran/synthEHRella. Discussion: Method choice is governed by the relative importance of the evaluation metrics in downstream use cases. We provide a decision tree to guide the choice among the benchmarked methods. Conclusion: GAN-based methods excel when distributional shifts exist between the training and testing populations. Otherwise, CorGAN and MedGAN are most suitable for association modeling and predictive modeling, respectively. Future research should prioritize enhancing fidelity of the synthetic data while controlling privacy exposure, and com

关键词： Electronic health record

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