Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments. In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management. While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together. We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model. Our implementation can be found at \url{https://github.com/kaggarwal/ClinicalNotesICU}.
College of Information and Computer Sciences（信息与计算机科学学院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
Intensive Care Unit Electronic Health Records (ICU EHRs) store multimodal data about patients including clinical notes, sparse and irregularly sampled physiological time series, lab results, and more. To date, most methods designed to learn predictive models from ICU EHR data have focused on a single modality. In this paper, we leverage the recently proposed interpolation-prediction deep learning architecture(Shukla and Marlin 2019) as a basis for exploring how physiological time series data and clinical notes can be integrated into a unified mortality prediction model. We study both early and…
Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse patient populations. Patients within different ICU populations (referred here as domains) vary by age, conditions and interventions. Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the fea…
MIT（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Determining whether hypotensive patients in intensive care units (ICUs) should receive fluid bolus therapy (FBT) has been an extremely challenging task for intensive care physicians as the corresponding increase in blood pressure has been hard to predict. Our study utilized regression models and attention-based recurrent neural network (RNN) algorithms and a multi-clinical information system large-scale database to build models that can predict the successful response to FBT among hypotensive patients in ICUs. We investigated both time-aggregated modeling using logistic regression algorithms w…
Mining the contribution of intensive care clinical course to outcome after traumatic brain injury
University of Cambridge（剑桥大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Humanitas University（Humanitas大学）；Karolinska Institutet（卡罗林斯卡学院）；University of Stirling（斯特灵大学）；Leiden University Medical Center（莱顿大学医学中心）；Cambridge Centre for Artificial Intelligence in Medicine（剑桥医学人工智能中心）
University of Michigan（密歇根大学）；Michigan Medicine（密歇根医学中心）
In healthcare, patient risk stratification models are often learned using time-series data extracted from electronic health records. When extracting data for a clinical prediction task, several formulations exist, depending on how one chooses the time of prediction and the prediction horizon. In this paper, we show how the formulation can greatly impact both model performance and clinical utility. Leveraging a publicly available ICU dataset, we consider two clinical prediction tasks: in-hospital mortality, and hypokalemia. Through these case studies, we demonstrate the necessity of evaluating …
ETH Zurich（苏黎世联邦理工学院）；University Hospital Zurich（苏黎世大学医院）
Patients in the intensive care unit (ICU) require constant and close supervision. To assist clinical staff in this task, hospitals use monitoring systems that trigger audiovisual alarms if their algorithms indicate that a patient's condition may be worsening. However, current monitoring systems are extremely sensitive to movement artefacts and technical errors. As a result, they typically trigger hundreds to thousands of false alarms per patient per day - drowning the important alarms in noise and adding to the exhaustion of clinical staff. In this setting, data is abundantly available, but ob…
Imperial College London（帝国理工学院）；Pangaea Data Limited；Queen Mary University of London（伦敦玛丽女王大学）；Hong Kong Baptist University（香港浸会大学）
Objective: Clinical notes contain information not present elsewhere, including drug response and symptoms, all of which are highly important when predicting key outcomes in acute care patients. We propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information, which is complementary to typically used vital signs and laboratory test results, to predict outcomes in the Intensive Care Unit (ICU). Methods: We develop a novel phenotype annotation model to annotate phenotypic features of patients which are then used as input features of predictive mod…
Auckland University of Technology（奥克兰理工大学）；Universidad Tecnológica de Bolívar（玻利瓦尔理工大学）
Accurate mortality prediction allows Intensive Care Units (ICUs) to adequately benchmark clinical practice and identify patients with unexpected outcomes. Traditionally, simple statistical models have been used to assess patient death risk, many times with sub-optimal performance. On the other hand deep learning holds promise to positively impact clinical practice by leveraging medical data to assist diagnosis and prediction, including mortality prediction. However, as the question of whether powerful Deep Learning models attend correlations backed by sound medical knowledge when generating pr…
Northeast Yucai Foreign Language School（东北育才外国语学校）；Northeast Yucai School（东北育才学校）；Shenyang Pharmaceutical University（沈阳药科大学）
ICU readmission is associated with longer hospitalization, mortality and adverse outcomes. An early recognition of ICU re-admission can help prevent patients from worse situation and lower treatment cost. As the abundance of Electronics Health Records (EHR), it is popular to design clinical decision tools with machine learning technique manipulating on healthcare large scale data. We designed data-driven predictive models to estimate the risk of ICU readmission. The discharge summary of each hospital admission was carefully represented by natural language processing techniques. Unified Medical…
RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation
Technical University of Munich（慕尼黑技术大学）；TUM University Hospital（TUM大学医院）；LMU Munich（慕尼黑大学）；University of Sheffield（谢菲尔德大学）；University of Oxford（牛津大学）；Zhongshan Hospital Fudan University（复旦大学中山医院）；Sun Yat-sen University Cancer Center（中山大学肿瘤中心）；Imperial College London（伦敦帝国学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；relAI – Konrad Zuse School of Excellence in Reliable AI（relAI – 卡诺夫茨卓越可靠人工智能学校）
École de Technologie Supérieure, University of Québec（魁北克大学高等技术学院）；CHU Sainte-Justine Hospital, University of Montreal（蒙特利尔大学圣茹斯特医院）
The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are the ideal clinical research environment for such development because they collect many clinical data and are highly computerized environments. We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation…
Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Review
National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Advanced Institute of Information Technology, Peking University（北京大学信息技术高等研究院）；Peking University Third Hospital（北京大学第三医院）；University of Liverpool（利物浦大学）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）；University of California, Los Angeles（加州大学洛杉矶分校）；Peking University People’s Hospital（北京大学人民医院）
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations …
TOBB University of Economics and Technology（TOBB经济与技术大学）
Early prediction of mortality and length of stay(LOS) of a patient is vital for saving a patient's life and management of hospital resources. Availability of electronic health records(EHR) makes a huge impact on the healthcare domain and there has seen several works on predicting clinical problems. However, many studies did not benefit from the clinical notes because of the sparse, and high dimensional nature. In this work, we extract medical entities from clinical notes and use them as additional features besides time-series features to improve our predictions. We propose a convolution based …
Berliner Hochschule für Technik (BHT)（柏林应用科学技术大学）；AHEPA University Hospital（阿赫帕大学医院）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide. However, current state-of-the-art models are mostly applicable to clinical notes written in English. We therefore investigate cross-lingual knowledge transfer strategies to execute this task for clinics that do not use the English language and have a small amount of in-domain data available. We evaluate these strategies for a Greek and a Spanish clinic leveraging clinical notes from different clinical domains such as cardiology, oncology a…
King’s College London（伦敦国王学院）；King’s College Hospital NHS Foundation Trust（国王学院医院NHS基金会信托）；University College London（伦敦大学学院）
The current mode of use of Electronic Health Record (EHR) elicits text redundancy. Clinicians often populate new documents by duplicating existing notes, then updating accordingly. Data duplication can lead to a propagation of errors, inconsistencies and misreporting of care. Therefore, quantifying information redundancy can play an essential role in evaluating innovations that operate on clinical narratives. This work is a quantitative examination of information redundancy in EHR notes. We present and evaluate two strategies to measure redundancy: an information-theoretic approach and a lexic…
