We present a machine learning pipeline and model that uses the entire uncurated EHR for prediction of in-hospital mortality at arbitrary time intervals, using all available chart, lab and output events, without the need for pre-processing or feature engineering. Data for more than 45,000 American ICU patients from the MIMIC-III database were used to develop an ICU mortality prediction model. All chart, lab and output events were treated by the model in the same manner inspired by Natural Language Processing (NLP). Patient events were discretized by percentile and mapped to learnt embeddings before being passed to a Recurrent Neural Network (RNN) to provide early prediction of in-patient mortality risk. We compared mortality predictions with the Simplified Acute Physiology Score II (SAPS II) and the Oxford Acute Severity of Illness Score (OASIS). Data were split into an independent test set (10%) and a ten-fold cross-validation was carried out during training to avoid overfitting. 13,233 distinct variables with heterogeneous data types were included without manual selection or pre-processing. Recordings in the first few hours of a patient's stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores within just 2 hours and achieving a state of the art Area Under the Receiver Operating Characteristic (AUROC) value of 0.80 (95% CI 0.79-0.80) at 12 hours vs 0.70 and 0.66 for SAPS II and OASIS at 24 hours respectively. Our model achieves a very strong performance of AUROC 0.86 (95% CI 0.85-0.86) for in-patient mortality prediction after 48 hours on the MIMIC-III dataset. Predictive performance increases over the first 48 hours of the ICU stay, but suffers from diminishing returns, providing rationale for time-limited trials of critical care and suggesting that the timing of decision making can be optimised and individualised.
With the ever-growing complexity of primary health care system, proactive patient failure management is an effective way to enhancing the availability of health care resource. One key enabler is the dynamic prediction of time-to-event outcomes. Conventional explanatory statistical approach lacks the capability of making precise individual level prediction, while the data adaptive binary predictors does not provide nominal survival curves for biologically plausible survival analysis. The purpose of this article is to elucidate that the knowledge of explanatory survival analysis can significantl…
Respiratory failure is the one of major causes of death in critical care unit. During the outbreak of COVID-19, critical care units experienced an extreme shortage of mechanical ventilation because of respiratory failure related syndromes. To help this, the early mortality risk prediction in patients who suffer respiratory failure can provide timely support for clinical treatment and resource management. In the study, we propose a dynamic modeling approach for early mortality risk prediction of the respiratory failure patients based on the first 24 hours ICU physiological data. Our proposed mo…
More than 144 000 Australians were diagnosed with cancer in 2019. The majority will first present to their GP symptomatically, even for cancer for which screening programs exist. Diagnosing cancer in primary care is challenging due to the non-specific nature of cancer symptoms and its low prevalence. Understanding the epidemiology of cancer symptoms and patterns of presentation in patient's medical history from primary care data could be important to improve earlier detection and cancer outcomes. As past medical data about a patient can be incomplete, irregular or missing, this creates additio…
Acute Care Solutions (ACS)（急性护理解决方案（ACS））；Philips Research North America（飞利浦北美研究院）；Cardon Children’s Medical Center（卡顿儿童医疗中心）；Banner Health System（班纳健康系统）
Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit. Using data collected over 5.5 years from the electronic health records of two medical facilities, we develop classifiers based on adaptive boosting and gradient tree boosting. We further combine these learned classifiers into an ensemble model and compare its performance to a modified pediatric early warning score (PEWS) baseline that relies on expert defined guidelines. To gauge model generalizability, we perform an inter-facility evaluatio…
University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；Feinberg School of Medicine, Northwestern University（西北大学范伯格医学院）
Survival analysis is a technique to predict the times of specific outcomes, and is widely used in predicting the outcomes for intensive care unit (ICU) trauma patients. Recently, deep learning models have drawn increasing attention in healthcare. However, there is a lack of deep learning methods that can model the relationship between measurements, clinical notes and mortality outcomes. In this paper we introduce BERTSurv, a deep learning survival framework which applies Bidirectional Encoder Representations from Transformers (BERT) as a language representation model on unstructured clinical n…
Research has shown that false alarms constitute more than 80% of the alarms triggered in the intensive care unit (ICU). The high false arrhythmia alarm rate has severe implications such as disruption of patient care, caregiver alarm fatigue, and desensitization from clinical staff to real life-threatening alarms. A method to reduce the false alarm rate would therefore greatly benefit patients as well as nurses in their ability to provide care. We here develop and describe a robust false arrhythmia alarm reduction system for use in the ICU. Building off of work previously described in the liter…
ETH Zürich（苏黎世联邦理工学院）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Weill Cornell Medicine（威尔康奈尔医学院）；Zürich University Hospital（苏黎世大学医院）；University of Bern（伯尔尼大学）；Swiss Institute for Bioinformatics（瑞士生物信息学研究所）；Auckland City Hospital（奥克兰市医院）
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulti…
Recent deep learning research based on Transformer model architectures has demonstrated state-of-the-art performance across a variety of domains and tasks, mostly within the computer vision and natural language processing domains. While some recent studies have implemented Transformers for clinical tasks using electronic health records data, they are limited in scope, flexibility, and comprehensiveness. In this study, we propose a flexible Transformer-based EHR embedding pipeline and predictive model framework that introduces several novel modifications of existing workflows that capitalize on…
Our aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical testing of the learned policies in a clinical deployment. We focus on infections in intensive care units which are one of the major causes of death and difficult to treat because of the complex and opaque patient dynamics, and the clinically debated, highly-divergent set of intervention policies required by each individual patient, yet intensive care units are naturally data rich. In our work, we build on RL approache…
Academy of Arts and Design, Tsinghua University（清华大学美术学院）；The Future Laboratory, Tsinghua University（清华大学未来实验室）；University of Huddersfield（哈德斯菲尔德大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Northeastern University（东北大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. Sharing data among different sources is a big challenge due to regulatory, operational and security reasons. One potential solution is federated machine learning, which is a method that sends machine learning algorithms simultaneously to all data sources, trains models in each source and aggregates the learned models. This strategy allows utilization of valuable data without mov…
Rey Juan Carlos University（胡安·卡洛斯国王大学）；University Hospital of Fuenlabrada（富恩拉夫拉达大学医院）；University Hospital of North-Norway（北挪威大学医院）；UiT The Arctic University of Norway（挪威北极大学）
The acquisition of Antimicrobial Multidrug Resistance (AMR) in patients admitted to the Intensive Care Units (ICU) is a major global concern. This study analyses data in the form of multivariate time series (MTS) from 3476 patients recorded at the ICU of University Hospital of Fuenlabrada (Madrid) from 2004 to 2020. 18\% of the patients acquired AMR during their stay in the ICU. The goal of this paper is an early prediction of the development of AMR. Towards that end, we leverage the time-series cluster kernel (TCK) to learn similarities between MTS. To evaluate the effectiveness of TCK as a k…
Dascena, Inc.（Dascena 公司）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Clinical decision support tools rooted in machine learning and optimization can provide significant value to healthcare providers, including through better management of intensive care units. In particular, it is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay (and associated hospitalization costs) and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions given a patient's elec…
We study multiple rule-based and machine learning (ML) models for sepsis detection. We report the first neural network detection and prediction results on three categories of sepsis. We have used the retrospective Medical Information Mart for Intensive Care (MIMIC)-III dataset, restricted to intensive care unit (ICU) patients. Features for prediction were created from only common vital sign measurements. We show significant improvement of AUC score using neural network based ensemble model compared to single ML and rule-based models. For the detection of sepsis, severe sepsis, and septic shock…
Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of models - known as individual survival distributions (ISDs) - produces patient-specific survival functions that offer greater descriptive power of patient outcomes than was previously possible. Unfortunately, at the time of writing, ISD models almost universally lack uncertainty quantification. In this paper, we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can …
Microsoft Research Cambridge（微软研究院剑桥）；University of Cambridge（剑桥大学）；Alan Turing Institute（艾伦·图灵研究所）
Clinical decision making is challenging because of pathological complexity, as well as large amounts of heterogeneous data generated as part of routine clinical care. In recent years, machine learning tools have been developed to aid this process. Intensive care unit (ICU) admissions represent the most data dense and time-critical patient care episodes. In this context, prediction models may help clinicians determine which patients are most at risk and prioritize care. However, flexible tools such as artificial neural networks (ANNs) suffer from a lack of interpretability limiting their accept…
ETH Zürich（苏黎世联邦理工学院）；University of Bern（伯尔尼大学）；Microsoft Research（微软研究院）；Auckland City Hospital（奥克兰市医院）
The development of respiratory failure is common among patients in intensive care units (ICU). Large data quantities from ICU patient monitoring systems make timely and comprehensive analysis by clinicians difficult but are ideal for automatic processing by machine learning algorithms. Early prediction of respiratory system failure could alert clinicians to patients at risk of respiratory failure and allow for early patient reassessment and treatment adjustment. We propose an early warning system that predicts moderate/severe respiratory failure up to 8 hours in advance. Our system was trained…
Northeastern University（东北大学）；New York University School of Medicine（纽约大学医学院）
Patients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on a deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critical ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice. Basically, we modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual …
With the development of the Internet of Things(IoT) and Artificial Intelligence(AI) technologies, human activity recognition has enabled various applications, such as smart homes and assisted living. In this paper, we target a new healthcare application of human activity recognition, early mobility recognition for Intensive Care Unit(ICU) patients. Early mobility is essential for ICU patients who suffer from long-time immobilization. Our system includes accelerometer-based data collection from ICU patients and an AI model to recognize patients' early mobility. To improve the model accuracy and…
Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University School of Medicine（杜克大学医学院）；Duke AI Health（杜克人工智能健康中心）；Mass General Brigham（麻省总医院布里格姆）；Harvard Medical School（哈佛医学院）；National University of Singapore（新加坡国立大学）
Domain Generalization and Adaptation in Intensive Care with Anchor Regression
Seminar for Statistics, ETH Zürich（统计研究所，苏黎世联邦理工学院）；AI Center, ETH Zürich（人工智能中心，苏黎世联邦理工学院）；Department of Computer Science, ETH Zürich（计算机科学系，苏黎世联邦理工学院）；Swiss Institute for Bioinformatics, Zürich（瑞士生物信息学研究所）
Ortho Biomed Inc.（奥索生物医学公司）；University of Toronto（多伦多大学）；Worcester Polytechnic Institute (WPI)（伍斯特理工学院（WPI））；University Health Network（大学健康网络）
The aim of survival analysis in healthcare is to estimate the probability of occurrence of an event, such as a patient's death in an intensive care unit (ICU). Recent developments in deep neural networks (DNNs) for survival analysis show the superiority of these models in comparison with other well-known models in survival analysis applications. Ensuring the reliability and explainability of deep survival models deployed in healthcare is a necessity. Since DNN models often behave like a black box, their predictions might not be easily trusted by clinicians, especially when predictions are cont…
University of Cambridge（剑桥大学）；Microsoft Research（微软研究院）
The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff. Most critical is the efficient allocation of resource-heavy Intensive Care Unit (ICU) beds to the patients who need life support. Central to solving this problem is knowing for how long the current set of ICU patients are likely to stay in the unit. In this work, we propose a new deep learning model based on the combination of temporal convolution and pointwise (1x1) convolution, to solve the length of stay prediction task on the eICU critical care dataset. …
University of Cambridge（剑桥大学）；Cantab Capital Institute for Mathematics of Information（Cantab Capital 数学信息研究所）；Department of Clinical Neurosciences（临床神经科学系）；Department of Medicine（医学系）；Division of Anaesthesia（麻醉科）；Department of Applied Mathematics and Theoretical Physics（应用数学与理论物理系）
Waveform physiological data is important in the treatment of critically ill patients in the intensive care unit. Such recordings are susceptible to artefacts, which must be removed before the data can be re-used for alerting or reprocessed for other clinical or research purposes. Accurate removal of artefacts reduces bias and uncertainty in clinical assessment, as well as the false positive rate of intensive care unit alarms, and is therefore a key component in providing optimal clinical care. In this work, we present DeepClean; a prototype self-supervised artefact detection system using a con…
Institute of Medical Engineering & Science（医学工程与科学研究所）；Massachussetts Institute of Technology（麻省理工学院）；Microsoft Research（微软研究院）
A large volume of research has considered the creation of predictive models for clinical data; however, much existing literature reports results using only a single source of data. In this work, we evaluate the performance of models trained on the publicly-available eICU Collaborative Research Database. We show that cross-validation using many distinct centers provides a reasonable estimate of model performance in new centers. We further show that a single model trained across centers transfers well to distinct hospitals, even compared to a model retrained using hospital-specific data. Our res…
University Paris Descartes（巴黎笛卡尔大学）；Institut Hypercube（超立方体研究所）；Montreal Institute for Learning Algorithms（蒙特利尔学习算法研究所）；École Polytechnique de Montréal（蒙特利尔理工学院）
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for graft patients based on the principle of multi-task learning. By learning to jointly predict the time of the event, and its rank in the cox partial log likelihood framework, our deep learning approach outperforms, in terms of survival time prediction quality and concordance index…
A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care
University of Edinburgh（爱丁堡大学）；Health Data Research UK（英国健康数据研究中心）；Peking University（北京大学）；Peking University Third Hospital（北京大学第三医院）；Kaihong Digital（凯宏数字）
Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort
University of London（伦敦大学）；Goldsmiths College, University of London（伦敦大学金史密斯学院）；University of Manchester（曼彻斯特大学）；King’s College London（伦敦国王学院）；University College London（伦敦大学学院）
ETH Zürich（苏黎世联邦理工学院）；FAU Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
Clinical practice in intensive care units (ICUs) requires early warnings when a patient's condition is about to deteriorate so that preventive measures can be undertaken. To this end, prediction algorithms have been developed that estimate the risk of mortality in ICUs. In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs. Specifically, we develop an attentive deep Markov model called AttDMM. To the best of our knowledge, AttDMM is the first ICU prediction model that jointly learns both long-term disease dynamics (via attention) and different …
University of Cambridge（剑桥大学）；Microsoft Research（微软研究院）
The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff. Most critical is the efficient allocation of resource-heavy Intensive Care Unit (ICU) beds to the patients who need life support. Central to solving this problem is knowing for how long the current set of ICU patients are likely to stay in the unit. In this work, we propose a new deep learning model based on the combination of temporal convolution and pointwise (1x1) convolution, to solve the length of stay prediction task on the eICU and MIMIC-IV critical c…
Georgia Institute of Technology（佐治亚理工学院）；Childrens Healthcare of Atlanta（亚特兰大儿童保健中心）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Deep learning models have achieved expert-level performance in healthcare with an exclusive focus on training accurate models. However, in many clinical environments such as intensive care unit (ICU), real-time model serving is equally if not more important than accuracy, because in ICU patient care is simultaneously more urgent and more expensive. Clinical decisions and their timeliness, therefore, directly affect both the patient outcome and the cost of care. To make timely decisions, we argue the underlying serving system must be latency-aware. To compound the challenge, health analytic app…
An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes
Columbia University（哥伦比亚大学）；Flinders University（弗林德斯大学）；University of Melbourne（墨尔本大学）；Technical University of Munich（慕尼黑工业大学）；Prince Charles Hospital Brisbane（布里斯班查尔斯王子医院）；Edith Cowan University（伊迪丝·考恩大学）；Monash University（莫纳什大学）；Massachusetts Institute of Technology（麻省理工学院）
ETH Zurich（苏黎世联邦理工学院）；SIB Swiss Institute of Bioinformatics（SIB瑞士生物信息学研究所）
Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathogen is identified, organ damage may have already progressed to irreversible stages. Effective sepsis management is therefore highly time-sensitive. By systematically analysing trends in the plethora of clinical data available in the intensive care unit (ICU), an early prediction of sepsis could lead to earlier pathogen identification, resistance testing, and effective antibiotic and supportive treatment, and thereby be…
Rediminds；Translational Analytics and Statistics（转化分析与统计）；University of Liverpool（利物浦大学）；Vattikuti Urology Institute（瓦提库蒂泌尿外科研究所）
Intensive care clinicians need reliable clinical practice tools to preempt unexpected critical events that might harm their patients in intensive care units (ICU), to pre-plan timely interventions, and to keep the patient's family well informed. The conventional statistical models are built by curating only a limited number of key variables, which means a vast unknown amount of potentially precious data remains unused. Deep learning models (DLMs) can be leveraged to learn from large complex datasets and construct predictive clinical tools. This retrospective study was performed using 42,818 ho…
Politecnico di Milano（米兰理工大学）；Technische Universität Dresden（德累斯顿工业大学）
Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, the data needed to train survival models are often distributed, incomplete, censored, and confidential. In this context, federated learning can be exploited to tremendously improve the quality of the models trained on distributed data while preserving user privacy. However, federated survival analysis is still in its early development, and there is no common benchmarking dataset to test …
Accurate and transparent prediction of cancer survival times on the level of individual patients can inform and improve patient care and treatment practices. In this paper, we design a model that concurrently learns to accurately predict patient-specific survival distributions and to explain its predictions in terms of patient attributes such as clinical tests or assessments. Our model is flexible and based on a recurrent network, can handle various modalities of data including temporal measurements, and yet constructs and uses simple explanations in the form of patient- and time-specific line…
University of California, Santa Barbara（加州大学圣巴巴拉分校）
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) algor…
University Hospital Bern（伯尔尼大学医院）；Auckland City Hospital（奥克兰市医院）；Swiss Institute of Bioinformatics（瑞士生物信息研究所）
Dynamic assessment of mortality risk in the intensive care unit (ICU) can be used to stratify patients, inform about treatment effectiveness or serve as part of an early-warning system. Static risk scoring systems, such as APACHE or SAPS, have recently been supplemented with data-driven approaches that track the dynamic mortality risk over time. Recent works have focused on enhancing the information delivered to clinicians even further by producing full survival distributions instead of point predictions or fixed horizon risks. In this work, we propose a non-parametric ensemble model, Weighted…
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）
Explainable Artificial Intelligence Techniques for Irregular Temporal Classification of Multidrug Resistance Acquisition in Intensive Care Unit Patients
Rey Juan Carlos University（雷伊·胡安·卡洛斯大学）；University Hospital of Fuenlabrada（芬拉布拉达大学医院）
LUME-DBN: Full Bayesian Learning of DBNs from Incomplete data in Intensive Care
Department of Informatics, Systems and Communication, University of Milano-Bicocca（米兰-比科卡大学信息学、系统与通信系）；Department of Science and Engineering, University of Groningen（格罗宁根大学科学与工程系）；BReCHS – Bicocca Research Centre in Health Services（比科卡健康服务研究中心）；Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milano-Bicocca, Vedano al Lambro (MB), Italy（比科卡生物信息学、生物统计学与生物成像中心-B4，米兰-比科卡大学，维达诺阿尔兰布罗（MB），意大利）
