Unlike in a clinical trial, where researchers get to determine the least number of positive and negative samples required, or in a machine learning study where the size and the class distribution of the validation set is static and known, in a real-world scenario, there is little control over the size and distribution of incoming patients. As a result, when measured during different time periods, evaluation metrics like Area under the Receiver Operating Curve (AUCROC) and Area Under the Precision-Recall Curve(AUCPR) may not be directly comparable. Therefore, in this study, for binary classifiers running in a long time period, we proposed to adjust these performance metrics for sample size and class distribution, so that a fair comparison can be made between two time periods. Note that the number of samples and the class distribution, namely the ratio of positive samples, are two robustness factors which affect the variance of AUCROC. To better estimate the mean of performance metrics and understand the change of performance over time, we propose a Kalman filter based framework with extrapolated variance adjusted for the total number of samples and the number of positive samples during different time periods. The efficacy of this method is demonstrated first on a synthetic dataset and then retrospectively applied to a 2-days ahead in-hospital mortality prediction model for COVID-19 patients during 2021 and 2022. Further, we conclude that our prediction model is not significantly affected by the evolution of the disease, improved treatments and changes in hospital operational plans.
Cardiovascular Research Center, Health Research Institute, Imam Ali Hospital, Kermanshah University of Medical Sciences（克尔曼沙赫医科大学伊玛目阿里医院心血管研究中心、健康研究所）
Investigation and analysis of patient outcomes, including in-hospital mortality and length of stay, are crucial for assisting clinicians in determining a patient's result at the outset of their hospitalization and for assisting hospitals in allocating their resources. This paper proposes an approach based on combining the well-known gray wolf algorithm with frequent items extracted by association rule mining algorithms. First, original features are combined with the discriminative extracted frequent items. The best subset of these features is then chosen, and the parameters of the used classif…
Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality
University of Southern California（南加州大学）；California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）；California State University, Long Beach（加州州立大学长滩分校）
Population stratification for prediction of mortality in post-AKI patients
Institute of Applied Health Sciences, University of Aberdeen（阿伯丁大学应用卫生科学学院）；Institute of Mathematics and Statistics, University of Sao Paulo（圣保罗大学数学与统计学院）
Machine learning models can be used for pattern recognition in medical data in order to improve patient outcomes, such as the prediction of in-hospital mortality. Deep learning models, in particular, require large amounts of data for model training. However, the data is often collected at different hospitals and sharing is restricted due to patient privacy concerns. In this paper, we aimed to demonstrate the potential of distributed training in achieving state-of-the-art performance while maintaining data privacy. Our results show that training the model in the federated learning framework lea…
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 …
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）
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）
Febrile neutropenia (FN) has been associated with high mortality, especially among adults with cancer. Understanding the patient and provider level heterogeneity in FN hospital admissions has potential to inform personalized interventions focused on increasing survival of individuals with FN. We leverage machine learning techniques to disentangling the complex interactions among multi domain risk factors in a population with FN. Data from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample and Nationwide Inpatient Sample (NIS) were used to build machine learning based …
Academy of Arts and Design, Tsinghua University（清华大学美术学院）；Massachusetts Institute of Technology（麻省理工学院）
Electronic medical records (EMRs) supports the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But insofar most algorithms have been centralized, taking little account of the decentralized, non-identically independently distributed (non-IID), and privacy-sensitive characteristics of EMRs that can complicate data collection, sharing and learning. To address this challenge, we introduced a community-based federated machine learning (CBFL) algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustere…
Hasso Plattner Institute for Digital Health at Mount Sinai（西奈山哈索·普拉特纳数字健康研究所）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；School of Informatics, Computing, and Engineering, Indiana University（印第安纳大学信息学、计算与工程学院）；Dell Medical School, University of Texas at Austin（德克萨斯大学奥斯汀分校戴尔医学院）；School of Informatics, University of Texas at Austin（德克萨斯大学奥斯汀分校信息学院）
Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modeling strategies is required to identify architectures that can best model outcomes. In this work, we train a Heterogeneous Graph Model (HGM) on Electronic Health Record data and use the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospita…
Protocol Choices Matter More Than the Aggregation Algorithm: Federated Learning for In-Hospital Mortality Prediction in Care-Unit and Multi-Hospital Federations
Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte (IFRN)（北大河州教育、科学和技术联邦学院（IFRN））
Vital signs and laboratory values are routinely used to guide clinical decision-making for polytrauma patients, such as the decision to use damage control techniques versus early definitive fracture fixation. Prior multivariate models have tried to predict mortality risk, but due to several limitations like one-time prediction at the time of admission, they have not proven clinically useful. There is a need for a dynamic model that captures evolving physiologic changes during patient's hospital course to trauma and resuscitation for mortality prediction. The Parkland Trauma Index of Mortality …
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…
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas（应用数学与系统研究所）；Instituto Mexicano del Seguro Social（墨西哥社会保险研究所）；Hospital Clínico Quirúrgico Hermanos Ameijeiras（阿梅赫拉斯兄弟临床外科医院）
Background: Several studies have highlighted the importance of considering sex differences in the diagnosis and treatment of Acute Coronary Syndrome (ACS). However, the identification of sex-specific risk markers in ACS sub-populations has been scarcely studied. The present study aims to explore machine learning (ML) models to identify in-hospital mortality markers for women and men in ACS sub-populations collected from a public database of electronic health records (EHR). Methods: We extracted 1,299 patients with ST-elevation myocardial infarction (STEMI) and 2,820 patients with non-ST-elevat…
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）
Universitat Politècnica de València（瓦伦西亚理工大学）；Instituto de Investigación Sanitaria La Fe（拉费健康研究所）；Universitat de València（瓦伦西亚大学）；Consejo Superior de Investigaciones Científicas（西班牙国家研究委员会）
Background: Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to guarantee a minimum level of quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of risk of one-year mortality. Objectives: The main objective of this work is to develop and validate machine-learning based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Methods: Five machine learning techniques were applied in our study to develop machine-learning predictive mo…
Acute infection, if not rapidly and accurately detected, can lead to sepsis, organ failure and even death. Current detection of acute infection as well as assessment of a patient's severity of illness are imperfect. Characterization of a patient's immune response by quantifying expression levels of specific genes from blood represents a potentially more timely and precise means of accomplishing both tasks. Machine learning methods provide a platform to leverage this 'host response' for development of deployment-ready classification models. Prioritization of promising classifiers is dependent, …
Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）
Healthcare data continues to flourish yet a relatively small portion, mostly structured, is being utilized effectively for predicting clinical outcomes. The rich subjective information available in unstructured clinical notes can possibly facilitate higher discrimination but tends to be under-utilized in mortality prediction. This work attempts to assess the gain in performance when multiple notes that have been minimally preprocessed are used as an input for prediction. A hierarchical architecture consisting of both convolutional and recurrent layers is used to concurrently model the differen…
University of Vienna（维也纳大学）；Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）
Intensive Care in-hospital mortality prediction has various clinical applications. Neural prediction models, especially when capitalising on clinical notes, have been put forward as improvement on currently existing models. However, to be acceptable these models should be performant and transparent. This work studies different attention mechanisms for clinical neural prediction models in terms of their discrimination and calibration. Specifically, we investigate sparse attention as an alternative to dense attention weights in the task of in-hospital mortality prediction from clinical notes. We…
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…
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 …
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…
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Sorbonne University（索邦大学）；Stanford University（斯坦福大学）；Singapore General Hospital（新加坡中央医院）；National University of Singapore（新加坡国立大学）
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…
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）
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…
Background Sepsis is one of the most life-threatening circumstances for critically ill patients in the US, while a standardized criteria for sepsis identification is still under development. Disparities in social determinants of sepsis patients can interfere with the risk prediction performances using machine learning. Methods Disparities in social determinants, including race, gender, marital status, insurance types and languages, among patients identified by six available sepsis criteria were revealed by forest plots. Sixteen machine learning classifiers were trained to predict in-hospital m…
Robô Laura；University of São Paulo（圣保罗大学）；Pontifical Catholic University of Parana（巴拉那天主教大学）；Federal University of Parana（巴拉那联邦大学）
Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural ne…
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；University of Maryland Baltimore（马里兰大学巴尔的摩分校）
The use of artificial intelligence in clinical care to improve decision support systems is increasing. This is not surprising since, by its very nature, the practice of medicine consists of making decisions based on observations from different systems both inside and outside the human body. In this paper, we combine three general systems (ICU, diabetes, and comorbidities) and use them to make patient clinical predictions. We use an artificial intelligence approach to show that we can improve mortality prediction of hospitalized diabetic patients. We do this by utilizing a machine learning appr…
Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所）；Complexo Hospitalario Universitario de A Coruña (CHUAC)（拉科鲁尼亚大学医院综合体）；Health Research Institute of Santiago de Compostela (IDIS)（圣地亚哥-德孔波斯特拉健康研究所）
Early prediction of patients at risk of clinical deterioration can help physicians intervene and alter their clinical course towards better outcomes. In addition to the accuracy requirement, early warning systems must make the predictions early enough to give physicians enough time to intervene. Interpretability is also one of the challenges when building such systems since being able to justify the reasoning behind model decisions is desirable in clinical practice. In this work, we built an interpretable model for the early prediction of various adverse clinical events indicative of clinical …
Background: Elderly patients with MODS have high risk of death and poor prognosis. The performance of current scoring systems assessing the severity of MODS and its mortality remains unsatisfactory. This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS. Methods: The MIMIC-III, eICU-CRD and PLAGH-S databases were employed for model generation and evaluation. We used the eXtreme Gradient Boosting model with the SHapley Additive exPlanations method to conduct early and interpretable predictions of patients' hospital outcom…
This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID patient mortality. An Extreme Gradient Boosting (XGBoost) prediction model is built based on a de-identified Austin area hospital dataset to predict the mortality of COVID-19 patients. We apply two XAI methods, Shapley Additive exPlanations (SHAP) and Locally Interpretable Model Agnostic Explanations (LIME), to compare the global and local interpretation of feature importance. This paper demonstrates the advantages of using XAI which shows the feature importance and decisi…
Huma Therapeutics Limited（Huma Therapeutics有限公司）；Imperial College London（帝国理工学院）；University of Belgrade（贝尔格莱德大学）；University of Exeter（埃克塞特大学）；Johns Hopkins Bloomberg School Public Health（约翰斯·霍普金斯大学彭博公共卫生学院）
The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterio…
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）
Caltech（加州理工学院）；Walter Burke Institute for Theoretical Physics（沃尔特·伯克理论物理研究所）
Predictive models with a focus on different spatial-temporal scales benefit governments and healthcare systems to combat the COVID-19 pandemic. Here we present the conditional Long Short-Term Memory networks with Quantile output (condLSTM-Q), a well-performing model for making quantile predictions on COVID-19 death tolls at the county level with a two-week forecast window. This fine geographical scale is a rare but useful feature in publicly available predictive models, which would especially benefit state-level officials to coordinate resources within the state. The quantile predictions from …
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）
Indian Institute of Technology, Delhi（印度理工学院德里分校）；AFMC, Pune（武装部队医学院，浦那）；CMC Ludhiana（卢迪亚纳基督教医学院）
As the second wave in India mitigates, COVID-19 has now infected about 29 million patients countrywide, leading to more than 350 thousand people dead. As the infections surged, the strain on the medical infrastructure in the country became apparent. While the country vaccinates its population, opening up the economy may lead to an increase in infection rates. In this scenario, it is essential to effectively utilize the limited hospital resources by an informed patient triaging system based on clinical parameters. Here, we present two interpretable machine learning models predicting the clinica…
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）
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 …
Indian Institute of Technology, Delhi（印度理工学院德里分校）；AFMC（武装部队医学院）；All India Institute of Medical Sciences（全印度医学科学研究所）；Indian Institute of Science Education and Research, Bhopal（印度科学教育与研究院博帕尔分校）；Armed Forces Medical College（武装部队医学院）
Many countries are now experiencing the third wave of the COVID-19 pandemic straining the healthcare resources with an acute shortage of hospital beds and ventilators for the critically ill patients. This situation is especially worse in India with the second largest load of COVID-19 cases and a relatively resource-scarce medical infrastructure. Therefore, it becomes essential to triage the patients based on the severity of their disease and devote resources towards critically ill patients. Yan et al. 1 have published a very pertinent research that uses Machine learning (ML) methods to predict…
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）
From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU
Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林夏里特医学院柏林健康研究所）；Universitätsmedizin Greifswald（格赖夫斯瓦尔德大学医学院）；Birmingham City University（伯明翰城市大学）
