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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.00200">arXiv:2411.00200</a> <span> [<a href="https://arxiv.org/pdf/2411.00200">pdf</a>, <a href="https://arxiv.org/format/2411.00200">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Oufattole%2C+N">Nassim Oufattole</a>, <a href="/search/cs?searchtype=author&query=Bergamaschi%2C+T">Teya Bergamaschi</a>, <a href="/search/cs?searchtype=author&query=Kolo%2C+A">Aleksia Kolo</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+H">Hyewon Jeong</a>, <a href="/search/cs?searchtype=author&query=Gaggin%2C+H">Hanna Gaggin</a>, <a href="/search/cs?searchtype=author&query=Stultz%2C+C+M">Collin M. Stultz</a>, <a href="/search/cs?searchtype=author&query=McDermott%2C+M+B+A">Matthew B. A. McDermott</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.00200v1-abstract-short" style="display: inline;"> Effective, reliable, and scalable development of machine learning (ML) solutions for structured electronic health record (EHR) data requires the ability to reliably generate high-quality baseline models for diverse supervised learning tasks in an efficient and performant manner. Historically, producing such baseline models has been a largely manual effort--individual researchers would need to deci… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00200v1-abstract-full').style.display = 'inline'; document.getElementById('2411.00200v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.00200v1-abstract-full" style="display: none;"> Effective, reliable, and scalable development of machine learning (ML) solutions for structured electronic health record (EHR) data requires the ability to reliably generate high-quality baseline models for diverse supervised learning tasks in an efficient and performant manner. Historically, producing such baseline models has been a largely manual effort--individual researchers would need to decide on the particular featurization and tabularization processes to apply to their individual raw, longitudinal data; and then train a supervised model over those data to produce a baseline result to compare novel methods against, all for just one task and one dataset. In this work, powered by complementary advances in core data standardization through the MEDS framework, we dramatically simplify and accelerate this process of tabularizing irregularly sampled time-series data, providing researchers the ability to automatically and scalably featurize and tabularize their longitudinal EHR data across tens of thousands of individual features, hundreds of millions of clinical events, and diverse windowing horizons and aggregation strategies, all before ultimately leveraging these tabular data to automatically produce high-caliber XGBoost baselines in a highly computationally efficient manner. This system scales to dramatically larger datasets than tabularization tools currently available to the community and enables researchers with any MEDS format dataset to immediately begin producing reliable and performant baseline prediction results on various tasks, with minimal human effort required. This system will greatly enhance the reliability, reproducibility, and ease of development of powerful ML solutions for health problems across diverse datasets and clinical settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00200v1-abstract-full').style.display = 'none'; document.getElementById('2411.00200v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.18941">arXiv:2407.18941</a> <span> [<a href="https://arxiv.org/pdf/2407.18941">pdf</a>, <a href="https://arxiv.org/format/2407.18941">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> LEMoN: Label Error Detection using Multimodal Neighbors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+H">Haoran Zhang</a>, <a href="/search/cs?searchtype=author&query=Balagopalan%2C+A">Aparna Balagopalan</a>, <a href="/search/cs?searchtype=author&query=Oufattole%2C+N">Nassim Oufattole</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+H">Hyewon Jeong</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Yan Wu</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+J">Jiacheng Zhu</a>, <a href="/search/cs?searchtype=author&query=Ghassemi%2C+M">Marzyeh Ghassemi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.18941v1-abstract-short" style="display: inline;"> Large repositories of image-caption pairs are essential for the development of vision-language models. However, these datasets are often extracted from noisy data scraped from the web, and contain many mislabeled examples. In order to improve the reliability of downstream models, it is important to identify and filter images with incorrect captions. However, beyond filtering based on image-caption… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18941v1-abstract-full').style.display = 'inline'; document.getElementById('2407.18941v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.18941v1-abstract-full" style="display: none;"> Large repositories of image-caption pairs are essential for the development of vision-language models. However, these datasets are often extracted from noisy data scraped from the web, and contain many mislabeled examples. In order to improve the reliability of downstream models, it is important to identify and filter images with incorrect captions. However, beyond filtering based on image-caption embedding similarity, no prior works have proposed other methods to filter noisy multimodal data, or concretely assessed the impact of noisy captioning data on downstream training. In this work, we propose LEMoN, a method to automatically identify label errors in multimodal datasets. Our method leverages the multimodal neighborhood of image-caption pairs in the latent space of contrastively pretrained multimodal models. We find that our method outperforms the baselines in label error identification, and that training on datasets filtered using our method improves downstream classification and captioning performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18941v1-abstract-full').style.display = 'none'; document.getElementById('2407.18941v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.10308">arXiv:2312.10308</a> <span> [<a href="https://arxiv.org/pdf/2312.10308">pdf</a>, <a href="https://arxiv.org/format/2312.10308">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Event-Based Contrastive Learning for Medical Time Series </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jeong%2C+H">Hyewon Jeong</a>, <a href="/search/cs?searchtype=author&query=Oufattole%2C+N">Nassim Oufattole</a>, <a href="/search/cs?searchtype=author&query=Mcdermott%2C+M">Matthew Mcdermott</a>, <a href="/search/cs?searchtype=author&query=Balagopalan%2C+A">Aparna Balagopalan</a>, <a href="/search/cs?searchtype=author&query=Jangeesingh%2C+B">Bryan Jangeesingh</a>, <a href="/search/cs?searchtype=author&query=Ghassemi%2C+M">Marzyeh Ghassemi</a>, <a href="/search/cs?searchtype=author&query=Stultz%2C+C">Collin Stultz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.10308v4-abstract-short" style="display: inline;"> In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event. For example, quantifying the risk of adverse outcomes after an acute cardiovascular event helps healthcare providers identify those patients at the highest risk of poor outcomes; i.e., patients who benefit from invasive therapies that can lower their risk. Assessing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.10308v4-abstract-full').style.display = 'inline'; document.getElementById('2312.10308v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.10308v4-abstract-full" style="display: none;"> In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event. For example, quantifying the risk of adverse outcomes after an acute cardiovascular event helps healthcare providers identify those patients at the highest risk of poor outcomes; i.e., patients who benefit from invasive therapies that can lower their risk. Assessing the risk of adverse outcomes, however, is challenging due to the complexity, variability, and heterogeneity of longitudinal medical data, especially for individuals suffering from chronic diseases like heart failure. In this paper, we introduce Event-Based Contrastive Learning (EBCL) - a method for learning embeddings of heterogeneous patient data that preserves temporal information before and after key index events. We demonstrate that EBCL can be used to construct models that yield improved performance on important downstream tasks relative to other pretraining methods. We develop and test the method using a cohort of heart failure patients obtained from a large hospital network and the publicly available MIMIC-IV dataset consisting of patients in an intensive care unit at a large tertiary care center. On both cohorts, EBCL pretraining yields models that are performant with respect to a number of downstream tasks, including mortality, hospital readmission, and length of stay. In addition, unsupervised EBCL embeddings effectively cluster heart failure patients into subgroups with distinct outcomes, thereby providing information that helps identify new heart failure phenotypes. The contrastive framework around the index event can be adapted to a wide array of time-series datasets and provides information that can be used to guide personalized care. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.10308v4-abstract-full').style.display = 'none'; document.getElementById('2312.10308v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at Unifying Representations in Neural Models Workshop in NeurIPS 2023, MLHC 2024</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> MLHC 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.09566">arXiv:2311.09566</a> <span> [<a href="https://arxiv.org/pdf/2311.09566">pdf</a>, <a href="https://arxiv.org/format/2311.09566">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wong%2C+A">Anna Wong</a>, <a href="/search/cs?searchtype=author&query=Ge%2C+S">Shu Ge</a>, <a href="/search/cs?searchtype=author&query=Oufattole%2C+N">Nassim Oufattole</a>, <a href="/search/cs?searchtype=author&query=Dejl%2C+A">Adam Dejl</a>, <a href="/search/cs?searchtype=author&query=Su%2C+M">Megan Su</a>, <a href="/search/cs?searchtype=author&query=Saeedi%2C+A">Ardavan Saeedi</a>, <a href="/search/cs?searchtype=author&query=Lehman%2C+L+H">Li-wei H. Lehman</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.09566v1-abstract-short" style="display: inline;"> Sepsis is a life-threatening condition triggered by an extreme infection response. Our objective is to forecast sepsis patient outcomes using their medical history and treatments, while learning interpretable state representations to assess patients' risks in developing various adverse outcomes. While neural networks excel in outcome prediction, their limited interpretability remains a key issue.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09566v1-abstract-full').style.display = 'inline'; document.getElementById('2311.09566v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.09566v1-abstract-full" style="display: none;"> Sepsis is a life-threatening condition triggered by an extreme infection response. Our objective is to forecast sepsis patient outcomes using their medical history and treatments, while learning interpretable state representations to assess patients' risks in developing various adverse outcomes. While neural networks excel in outcome prediction, their limited interpretability remains a key issue. In this work, we use knowledge distillation via constrained variational inference to distill the knowledge of a powerful "teacher" neural network model with high predictive power to train a "student" latent variable model to learn interpretable hidden state representations to achieve high predictive performance for sepsis outcome prediction. Using real-world data from the MIMIC-IV database, we trained an LSTM as the "teacher" model to predict mortality for sepsis patients, given information about their recent history of vital signs, lab values and treatments. For our student model, we use an autoregressive hidden Markov model (AR-HMM) to learn interpretable hidden states from patients' clinical time series, and use the posterior distribution of the learned state representations to predict various downstream outcomes, including hospital mortality, pulmonary edema, need for diuretics, dialysis, and mechanical ventilation. Our results show that our approach successfully incorporates the constraint to achieve high predictive power similar to the teacher model, while maintaining the generative performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09566v1-abstract-full').style.display = 'none'; document.getElementById('2311.09566v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 12 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.13081">arXiv:2009.13081</a> <span> [<a href="https://arxiv.org/pdf/2009.13081">pdf</a>, <a href="https://arxiv.org/ps/2009.13081">ps</a>, <a href="https://arxiv.org/format/2009.13081">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jin%2C+D">Di Jin</a>, <a href="/search/cs?searchtype=author&query=Pan%2C+E">Eileen Pan</a>, <a href="/search/cs?searchtype=author&query=Oufattole%2C+N">Nassim Oufattole</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+W">Wei-Hung Weng</a>, <a href="/search/cs?searchtype=author&query=Fang%2C+H">Hanyi Fang</a>, <a href="/search/cs?searchtype=author&query=Szolovits%2C+P">Peter Szolovits</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.13081v1-abstract-short" style="display: inline;"> Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.13081v1-abstract-full').style.display = 'inline'; document.getElementById('2009.13081v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.13081v1-abstract-full" style="display: none;"> Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7\%, 42.0\%, and 70.1\% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.13081v1-abstract-full').style.display = 'none'; document.getElementById('2009.13081v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to AAAI 2021</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search 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