We intend to create a new risk assessment methodology that combines the best characteristics of both risk score and machine learning models. More specifically, we aim to develop a method that, besides having a good performance, offers a personalized model and outcome for each patient, presents high interpretability, and incorporates an estimation of the prediction reliability which is not usually available. By combining these features in the same approach we expect that it can boost the confidence of physicians to use such a tool in their daily activity. In order to achieve the mentioned goals, a three-step methodology was developed: several rules were created by dichotomizing risk factors; such rules were trained with a machine learning classifier to predict the acceptance degree of each rule (the probability that the rule is correct) for each patient; that information was combined and used to compute the risk of mortality and the reliability of such prediction. The methodology was applied to a dataset of patients admitted with any type of acute coronary syndromes (ACS), to assess the 30-days all-cause mortality risk. The performance was compared with state-of-the-art approaches: logistic regression (LR), artificial neural network (ANN), and clinical risk score model (Global Registry of Acute Coronary Events - GRACE). The proposed approach achieved testing results identical to the standard LR, but offers superior interpretability and personalization; it also significantly outperforms the GRACE risk model and the standard ANN model. The calibration curve also suggests a very good generalization ability of the obtained model as it approaches the ideal curve. Finally, the reliability estimation of individual predictions presented a great correlation with the misclassifications rate. Those properties may have a beneficial application in other clinical scenarios as well. [abridged]
IBM Research – China, IBM（IBM中国研究院（IBM））；Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院）；AstraZeneca China（阿斯利康中国）；The First Affiliated Hospital, Zhengzhou University（郑州大学第一附属医院）
Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in data-driven disease classification and subtyping. Acute coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery blood flow, where disease classification would help to inform therapeutic strategies and provide prognostic insights. Here we conducted outcome-driven cluster analysis of ACS patients, which jointly considers treatment and patient outcome as indicators for patient state. Multi-task neur…
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…
New York University（纽约大学）；IBM Watson Health（IBM沃森健康）
Acute aortic syndrome (AAS) is a group of life threatening conditions of the aorta. We have developed an end-to-end automatic approach to detect AAS in computed tomography (CT) images. Our approach consists of two steps. At first, we extract N cross sections along the segmented aorta centerline for each CT scan. These cross sections are stacked together to form a new volume which is then classified using two different classifiers, a 3D convolutional neural network (3D CNN) and a multiple instance learning (MIL). We trained, validated, and compared two models on 2291 contrast CT volumes. We tes…
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…
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…
TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration
Department of Computing, and Mathematics, Manchester Metropolitan University（计算机与数学系，曼彻斯特 Metropolitan 大学）；Blackpool Teaching Hospitals NHS Foundation Trust（布莱克浦教学医院 NHS 基础信托）；Northern Care Alliance, NHS Foundation Trust（北方护理联盟，NHS 基础信托）；Department of Engineering, Manchester Metropolitan University（工程系，曼彻斯特 Metropolitan 大学）；Faculty of Business and Law, Manchester Metropolitan University（商业与法律学院，曼彻斯特 Metropolitan 大学）；Faculty of Medical Sciences, Newcastle University（医学科学学院，纽卡斯尔大学）
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…
The Ohio State University College of Medicine（俄亥俄州立大学医学院）
We propose a fully automated algorithm based on a deep learning framework enabling screening of a coronary computed tomography angiography (CCTA) examination for confident detection of the presence or absence of coronary artery atherosclerosis. The system starts with extracting the coronary arteries and their branches from CCTA datasets and representing them with multi-planar reformatted volumes; pre-processing and augmentation techniques are then applied to increase the robustness and generalization ability of the system. A 3-dimensional convolutional neural network (3D-CNN) is utilized to mo…
Patients who suffer an acute coronary syndrome are at elevated risk for adverse cardiovascular events such as myocardial infarction and cardiovascular death. Accurate assessment of this risk is crucial to their course of care. We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndrome based on a patient's raw electrocardiogram (ECG) signal. Learning from this signal is challenging for two reasons: 1) positive examples signifying a downstream cardiovascular event are scarce, causing drastic class imbalance, and 2) each patient's ECG signal consists of thous…
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…
In this work, we utilize Machine Learning for early recognition of patients at high risk of acute respiratory distress syndrome (ARDS), which is critical for successful prevention strategies for this devastating syndrome. The difficulty in early ARDS recognition stems from its complex and heterogenous nature. In this study, we integrate knowledge of the heterogeneity of ARDS patients into predictive model building. Using MIMIC-III data, we first apply latent class analysis (LCA) to identify homogeneous sub-groups in the ARDS population, and then build predictive models on the partitioned data.…
With the huge spike in the COVID-19 cases across the globe and reverse transcriptase-polymerase chain reaction (RT-PCR) test remains a key component for rapid and accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In recent months there has been an acute shortage of medical supplies in developing countries, especially a lack of RT-PCR testing resulting in delayed patient care and high infection rates. We present a gradient-boosting machine model that predicts the diagnostics result of SARS-CoV- 2 in an RT-PCR test by utilizing eight binary features. We used the…
TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care
Emory University（埃默里大学）；Massachusetts Institute of Technology（麻省理工学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Rochester（罗切斯特大学）
ITMO University（圣彼得堡国立信息技术机械与光学大学）；Almazov National Medical Research Center（阿尔马佐夫国家医学研究中心）
The paper proposes and investigates an approach for surrogate-assisted performance prediction of data-driven knowledge discovery algorithms. The approach is based on the identification of surrogate models for prediction of the target algorithm's quality and performance. The proposed approach was implemented and investigated as applied to an evolutionary algorithm for discovering clusters of interpretable clinical pathways in electronic health records of patients with acute coronary syndrome. Several clustering metrics and execution time were used as the target quality and performance metrics r…
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data
École de technologie supérieure, Université du Québec（魁北克大学高等技术学院）；K. N. Toosi University of Technology（K. N. 图什理工大学）；Centre de recherche de l’Institut universitaire de gériatrie de Montréal（蒙特利尔大学老年学研究所）
COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for se…
