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 model is validated on the eICU collaborate database. We achieved a high AUROC performance (80-83%) and significantly improved AUCPR 4% on Day 5 since ICU admission, compared to the state-of-art prediction models. In addition, we illustrated that the survival curve includes the time-varying information for the early ICU admission survival analysis.
Electronic Health Record (EHR) systems provide critical, rich and valuable information at high frequency. One of the most exciting applications of EHR data is in developing a real-time mortality warning system with tools from survival analysis. However, most of the survival analysis methods used recently are based on (semi)parametric models using static covariates. These models do not take advantage of the information conveyed by the time-varying EHR data. In this work, we present an application of a highly scalable survival analysis method, BoXHED 2.0 to develop a real-time in-ICU mortality w…
Think as a Doctor: An Interpretable AI Approach for ICU Mortality Prediction
School of Information Management & Engineering, Shanghai University of Finance and Economics（上海金融学院信息管理与工程学院）；Key Laboratory of Data Intelligence and Management (Beihang University), Ministry of Industry and Information Technology, School of Economics and Management, Beihang University（北京航空航天大学数据智能与管理重点实验室）
Mortality prediction in intensive care units is considered one of the critical steps for efficiently treating patients in serious condition. As a result, various prediction models have been developed to address this problem based on modern electronic healthcare records. However, it becomes increasingly challenging to model such tasks as time series variables because some laboratory test results such as heart rate and blood pressure are sampled with inconsistent time frequencies. In this paper, we propose several deep learning models using the same features as the SAPS II score. To derive insig…
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…
Good predictors of ICU Mortality have the potential to identify high-risk patients earlier, improve ICU resource allocation, or create more accurate population-level risk models. Machine learning practitioners typically make choices about how to represent features in a particular model, but these choices are seldom evaluated quantitatively. This study compares the performance of different representations of clinical event data from MIMIC II in a logistic regression model to predict 36-hour ICU mortality. The most common representations are linear (normalized counts) and binary (yes/no). These,…
To date, developing a good model for early intensive care unit (ICU) mortality prediction is still challenging. This paper presents a patient based predictive modeling framework (PPMF) to improve the performance of ICU mortality prediction using data collected during the first 48 hours of ICU admission. PPMF consists of three main components verifying three related research hypotheses. The first component captures dynamic changes of patients status in the ICU using their time series data (e.g., vital signs and laboratory tests). The second component is a local approximation algorithm that clas…
Early Mortality Prediction in ICU Patients with Hypertensive Kidney Disease Using Interpretable Machine Learning
Department of Industrial and Systems Engineering, University of Southern California（美国南加州大学工业与系统工程系）；Colorectal Research Center, Iran University of Medical Sciences（伊朗医学科学大学结直肠研究中心）；Department of Health Science, California State University, Long Beach (CSULB)（加州大学长滩分校健康科学系）；Department of Industrial and Manufacturing Engineering, California State Polytechnic University, Pomona（加州州立大学波莫纳分校工业与制造工程系）
Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach
organization= Daniel J. Epstein Department of Industrial \& Systems Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= United States；organization= Colorectal Research Center, Iran University of Medical Sciences , city= Tehran , postcode= 14535 , country= Iran；organization= Department of Health Science, California State University , city= Long Beach , state= CA , postcode= 90840 , country= United States；organization= Department of Industrial；Manufacturing Engineering, California State Polytechnic University , city= Pomona , state= CA , postcode= 91768 , country= United States
Paralytic Ileus (PI) patients are at high risk of death when admitted to the Intensive care unit (ICU), with mortality as high as 40\%. There is minimal research concerning PI patient mortality prediction. There is a need for more accurate prediction modeling for ICU patients diagnosed with PI. This paper demonstrates performance improvements in predicting the mortality of ICU patients diagnosed with PI after 24 hours of being admitted. The proposed framework, PMPI(Process Mining Model to predict mortality of PI patients), is a modification of the work used for prediction of in-hospital mortal…
Feinberg School of Medicine Northwestern University Chicago, U.S；Weill Cornell Medicine Cornell University New York, U.S
ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpret them easily; on the other hand, good prediction can help clinicians take timely actions to prevent the mortality. These correspond to the interpretability and accuracy problems. Most existing methods lack of the interpretability, but recently Subgraph Augmented Nonnegative Matrix Factorization (SANMF) has been successfully applied to time series data to provide a path to interpret the features well. Therefore, we …
Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review
Affiliated Hospital of Guangdong Medical University（广东医科大学附属医院）；Maastricht University Medical Center（马斯特里赫特大学医学中心）；GROW School for Oncology and Reproduction, Maastricht University Medical Center（马斯特里赫特大学医学中心肿瘤与生殖GROW学院）；Institute of Nephrology, Affiliated Hospital of Guangdong Medical University（广东医科大学附属医院肾脏病研究所）；Guangdong Provincial Key Laboratory of Autophagy and Major Chronic Non-communicable Diseases（广东省自噬与重大慢性非传染性疾病重点实验室）；Beijing University of Technology（北京工业大学）；China Agricultural University（中国农业大学）；JTB Technology Corp.（JTB科技股份有限公司）；Guangdong Medical University（广东医科大学）；Chinese PLA General Hospital（中国人民解放军总医院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）
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…
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…
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…
Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting
School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China（天津理工大学计算机科学与工程学院）；Institute for Artificial Intelligence, Peking University, Beijing, China（北京大学人工智能研究院）；DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China（清华大学信息产业研究院）；National Institute of Health Data Science, Peking University, Beijing, China（北京大学健康数据科学国家研究院）
IMEDACS；Vivace Health Solutions Inc（Vivace健康解决方案公司）；Johns Hopkins University（约翰斯·霍普金斯大学）；Scripps Memorial Hospital - Encinitas（斯克里普斯恩西尼塔斯纪念医院）
Although timely sepsis diagnosis and prompt interventions in Intensive Care Unit (ICU) patients are associated with reduced mortality, early clinical recognition is frequently impeded by non-specific signs of infection and failure to detect signs of sepsis-induced organ dysfunction in a constellation of dynamically changing physiological data. The goal of this work is to identify patient at risk of life-threatening sepsis utilizing a data-centered and machine learning-driven approach. We derive a mortality risk predictive dynamic Bayesian network (DBN) guided by a customized sepsis knowledgeba…
XAI for In-hospital Mortality Prediction via Multimodal ICU Data
Wuhan University（武汉大学）；University of Oxford（牛津大学）；The University of Sydney（悉尼大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Edinburgh（爱丁堡大学）
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA（计算机科学与工程系，华盛顿大学圣路易斯分校）；Institute for Informatics, Washington University School of Medicine, St. Louis, MO, USA（信息学院，华盛顿大学医学学院）；Department of Anaesthesiology, Washington University School of Medicine, St. Louis, MO, USA（麻醉学系，华盛顿大学医学学院）；Department of Pulmonary Critical Care and Medicine, Washington University School of Medicine, St. Louis, MO, USA（呼吸科重症医学系，华盛顿大学医学学院）
Stockholm University（斯德哥尔摩大学）；Getinge（洁定）；QRIOS IT
Although Machine Learning (ML) can be seen as a promising tool to improve clinical decision-making for supporting the improvement of medication plans, clinical procedures, diagnoses, or medication prescriptions, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silos, making traditional machine learning challenging. Federated learning can counteract those limitations by training machine learning models over data silos while keeping the sensitive data localized. This study proposes a federated learning…
Machine Learning-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database
University of Southern California（南加州大学）；Iran University of Medical Sciences（伊朗医学科学大学）；California State University（加利福尼亚州立大学）；California State Polytechnic University（加利福尼亚州_polytechnic大学）
ICU Mortality Prediction Using Long Short-Term Memory Networks
Faculty of Medicine, University of Monastir（莫纳斯提尔大学医学院）；Higher Institute of Computer Sciences and Mathematics, University of Monastir（莫纳斯提尔大学高等计算机科学与数学学院）
Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19 Patients
Imam Abdulrahman Bin Faisal University（伊玛目阿卜杜勒拉赫曼·本·费萨尔大学）；Ahsanullah University of Science and Technology（阿赫萨努拉科技大学）；Health Cluster（卫生集群）；Ajman University（阿治曼大学）
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Northeast Georgia Health System（东北乔治亚医疗系统）；Palos Health（帕洛斯医疗中心）；Penn State University（宾夕法尼亚州立大学）；Purdue University（普渡大学）
Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identification of causal effects from observational data in the cases where the causal graph is identifiable, i.e., the data generating mechanism can be recovered from the joint distribution. However, no such studies have been done to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effect from observational data by augmenting expert knowledge in the model development phase and with a practical clinical a…
New Mexico Institute of Mining and Technology（新墨西哥矿业技术学院）；Oak Ridge National Laboratory（橡树岭国家实验室）；The University of Tennessee Health Science Center（田纳西大学健康科学中心）；Illinois State University（伊利诺伊州立大学）
Patient monitoring is vital in all stages of care. We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction …
Auckland University of Technology（奥克兰理工大学）；Universidad Tecnológica de Bolívar（玻利瓦尔理工大学）
To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance. Alternatively, Deep Learning offers state of the art capabilities in certain prediction tasks and research suggests deep neural networks are able to outperform traditional techniques. Nevertheless, a main impediment…
Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion
Affiliated Hospital of Guangdong Medical University（广东医科大学附属医院）；Guangdong Medical University（广东医科大学）；Sun Yat-sen University（中山大学）；Beijing University of Technology（北京工业大学）；China Agricultural University（中国农业大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；JTB Technology Corp.（JTB科技公司）；Peking University（北京大学）；China Medical University（中国医科大学）
Florida State University（佛罗里达州立大学）；University of Florida（佛罗里达大学）
Heart disease remains the leading cause of death in the United States. Compared with risk assessment guidelines that require manual calculation of scores, machine learning-based prediction for disease outcomes such as mortality can be utilized to save time and improve prediction accuracy. This study built and evaluated various machine learning models to predict one-year mortality in patients diagnosed with acute myocardial infarction or post myocardial infarction syndrome in the MIMIC-III database. The results of the best performing shallow prediction models were compared to a deep feedforward…
Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia
University of Southern California（南加州大学）；Iran University of Medical Sciences（伊朗医科大学）；California State University, Long Beach（加州州立大学长滩分校）；California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）
Deep learning has demonstrated success in many applications; however, their use in healthcare has been limited due to the lack of transparency into how they generate predictions. Algorithms such as Recurrent Neural Networks (RNNs) when applied to Electronic Medical Records (EMR) introduce additional barriers to transparency because of the sequential processing of the RNN and the multi-modal nature of EMR data. This work seeks to improve transparency by: 1) introducing Learned Binary Masks (LBM) as a method for identifying which EMR variables contributed to an RNN model's risk of mortality (ROM…
Sepsis is an important cause of mortality, especially in intensive care unit (ICU) patients. Developing novel methods to identify early mortality is critical for improving survival outcomes in sepsis patients. Using the MIMIC-III database, we integrated demographic data, physiological measurements and clinical notes. We built and applied several machine learning models to predict the risk of hospital mortality and 30-day mortality in sepsis patients. From the clinical notes, we generated clinically meaningful word representations and embeddings. Supervised learning classifiers and a deep learn…
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…
University of Virginia（弗吉尼亚大学）；Frank Batten School of Leadership and Public Policy（弗兰克·巴顿领导力与公共政策学院）
ICU mortality scoring systems attempt to predict patient mortality using predictive models with various clinical predictors. Examples of such systems are APACHE, SAPS and MPM. However, most such scoring systems do not actively look for and include interaction terms, despite physicians intuitively taking such interactions into account when making a diagnosis. One barrier to including such terms in predictive models is the difficulty of using most variable selection methods in high-dimensional datasets. A genetic algorithm framework for variable selection with logistic regression models is used …
A Novel Multi-Task Teacher-Student Architecture with Self-Supervised Pretraining for 48-Hour Vasoactive-Inotropic Trend Analysis in Sepsis Mortality Prediction
University of Southern California（南加州大学）；California State University, Long Beach（加州州立大学长滩分校）；Iran University of Medical Sciences（伊朗医科大学）；California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）
The acute respiratory distress syndrome (ARDS) is a severe form of hypoxemic respiratory failure with in-hospital mortality of 35-46%. High mortality is thought to be related in part to challenges in making a prompt diagnosis, which may in turn delay implementation of evidence-based therapies. A deep neural network (DNN) algorithm utilizing unbiased ventilator waveform data (VWD) may help to improve screening for ARDS. We first show that a convolutional neural network-based ARDS detection model can outperform prior work with random forest models in AUC (0.95+/-0.019 vs. 0.88+/-0.064), accuracy…
High Flow Nasal Cannula (HFNC) provides non-invasive respiratory support for critically ill children who may tolerate it more readily than other Non-Invasive (NIV) techniques. Timely prediction of HFNC failure can provide an indication for increasing respiratory support. This work developed and compared machine learning models to predict HFNC failure. A retrospective study was conducted using EMR of patients admitted to a tertiary pediatric ICU from January 2010 to February 2020. A Long Short-Term Memory (LSTM) model was trained to generate a continuous prediction of HFNC failure. Performance …
CT-HEG: A Bidirectional, Timestamp-Attributed Event Graph for ICU In-Hospital Mortality Prediction - An Architectural Ablation Study
Westcliff University（韦斯特克利夫大学）；Northern University Bangladesh（孟加拉国北方大学）；Stanton University（斯坦顿大学）；Washington University of Science and Technology（华盛顿科技大学）
The intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of Electronic Health Record (EHR). This data allows for the development of a prediction tool with perfect knowledge backing. We aimed to build a mortality prediction model on 2012 Physionet Challenge mortality prediction database of 4000 patients admitted in ICU. The challenges in the dataset, such as high dimensionality, imbalanced distribution, and missing values were tackled with analytical methods and tools via feature engineering and new variable construction. The objective of the research i…
Computer Science and Artificial Intelligence Lab, MIT（麻省理工学院计算机科学与人工智能实验室）
Sepsis is a leading cause of mortality in intensive care units (ICUs) and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. Understanding more about a patient's physiological state at a given time could hold the key to effective treatment policies. In this work, we propose a new approach to deduce optimal treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Learning…
Wright State University（莱特州立大学）；Kno.e.sis Research Center（Kno.e.sis研究中心）
Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based…
Florida State University（佛罗里达州立大学）；University of Florida（佛罗里达大学）
Predicting the risk of mortality for patients with acute myocardial infarction (AMI) using electronic health records (EHRs) data can help identify risky patients who might need more tailored care. In our previous work, we built computational models to predict one-year mortality of patients admitted to an intensive care unit (ICU) with AMI or post myocardial infarction syndrome. Our prior work only used the structured clinical data from MIMIC-III, a publicly available ICU clinical database. In this study, we enhanced our work by adding the word embedding features from free-text discharge summar…
Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data
University of California, San Diego Health（加州大学圣地亚哥分校健康中心）；University of California, San Diego（加州大学圣地亚哥分校）
King’s College London（伦敦国王学院）；University College London（伦敦大学学院）
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked platform comprises two components: a) an unsupervised LSTM Autoencoder that learns …
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 be…
Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020. Many cases of COVID-19 escalate from Severe Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acute respiratory distress syndrome (ARDS) to death. Extracorporeal membranous oxygenation (ECMO) is a life-sustaining oxygenation and ventilation therapy that may be used for patients with severe ARDS when mechanical ventilation is insufficient to sustain life. While early planning and surgical cannulation for ECMO can increase survival, clinicians report the lack of a risk score…
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…
