In this paper we present a Recurrent neural networks (RNN) based architecture that achieves an AUCROC of 0.9147 for predicting the onset of Congestive Heart Failure (CHF) 15 months in advance using a 12-month observation window on a large cohort of 216,394 patients. We believe this to be the largest study in CHF onset prediction with respect to the number of CHF case patients in the cohort and the test set (3,332 CHF patients) on which the AUC metrics are reported. We explore the extent to which LSTM (Long Short Term Memory) based model, a variant of RNNs, can accurately predict the onset of CHF when compared to known linear baselines like Logistic Regression, Random Forests and deep learning based models such as Multi-Layer Perceptron and Convolutional Neural Networks. We utilize demographics, medical diagnosis and procedure data from 21,405 CHF and 194,989 control patients to as our features. We describe our feature embedding strategy for medical diagnosis codes that accommodates the sparse, irregular, longitudinal, and high-dimensional characteristics of EHR data. We empirically show that LSTMs can capture the longitudinal aspects of EHR data better than the proposed baselines. As an attempt to interpret the model, we present a temporal data analysis-based technique on false positives to attribute feature importance. A model capable of predicting the onset of congestive heart failure months in the future with this level of accuracy and precision can support efforts of practitioners to implement risk factor reduction strategies and researchers to begin to systematically evaluate interventions to potentially delay or avert development of the disease with high mortality, morbidity and significant costs.
A. Yay k ( ) Military Academy \ Defence University, Ankara, Turkey email: Y. Kutlu Department of Computer Engineering, \. I skenderun Technical University, Hatay, Turkey G. Altan Department of Computer Engineering, \. I skenderun Technical University, Hatay, Turkey
Our study concerns with automated predicting of congestive heart failure (CHF) through the analysis of electrocardiography (ECG) signals. A novel machine learning approach, regularized hessenberg decomposition based extreme learning machine (R-HessELM), and feature models; squared, circled, inclined and grid entropy measurement were introduced and used for prediction of CHF. This study proved that inclined entropy measurements features well represent characteristics of ECG signals and together with R-HessELM approach overall accuracy of 98.49% was achieved.
University of Washington Tacoma（塔科马华盛顿大学）；Ghent University（根特大学）；Multicare Health System（多凯尔医疗系统）
Mitigating risk-of-readmission of Congestive Heart Failure (CHF) patients within 30 days of discharge is important because such readmissions are not only expensive but also critical indicator of provider care and quality of treatment. Accurately predicting the risk-of-readmission may allow hospitals to identify high-risk patients and eventually improve quality of care by identifying factors that contribute to such readmissions in many scenarios. In this paper, we investigate the problem of predicting risk-of-readmission as a supervised learning problem, using a multi-layer classification appro…
Generating datasets that "look like" given real ones is an interesting tasks for healthcare applications of ML and many other fields of science and engineering. In this paper we propose a new method of general application to binary datasets based on a method for learning the parameters of a latent variable moment that we have previously used for clustering patient datasets. We compare our method with a recent proposal (MedGan) based on generative adversarial methods and find that the synthetic datasets we generate are globally more realistic in at least two senses: real and synthetic instances…
Electronic health records (EHR) are increasingly being used for constructing disease risk prediction models. Feature engineering in EHR data however is challenging due to their highly dimensional and heterogeneous nature. Low-dimensional representations of EHR data can potentially mitigate these challenges. In this paper, we use global vectors (GloVe) to learn word embeddings for diagnoses and procedures recorded using 13 million ontology terms across 2.7 million hospitalisations in national UK EHR. We demonstrate the utility of these embeddings by evaluating their performance in identifying p…
Charles Sturt University（查尔斯特大学）；Feni University（费尼大学）；Z.H. Sikder University of Science and Technology（Z.H. 锡克德科技大学）；Macquarie University（麦考瑞大学）
The combination of big data and deep learning is a world-shattering technology that can greatly impact any objective if used properly. With the availability of a large volume of health care datasets and progressions in deep learning techniques, systems are now well equipped to predict the future trend of any health problems. From the literature survey, we found the SVM was used to predict the heart failure rate without relating objective factors. Utilizing the intensity of important historical information in electronic health records (EHR), we have built a smart and predictive model utilizing …
H-Infinity Filter Enhanced CNN-LSTM for Arrhythmia Detection from Heart Sound Recordings
Department of Computer Science and Engineering（计算机科学与工程系）；National Institute of Technology Karnataka, Surathkal, Karnataka, India 575025（卡纳塔克邦苏拉塔卡尔国家理工学院）
本文提出CNN-H-Infinity-LSTM架构，通过引入受控制理论H-Infinity滤波器启发的可训练参数，增强模型鲁棒性与泛化能力，在PhysioNet CinC Challenge 2016数据集上实现99.42%准确率，有效解决心音记录心律失常检测问题。
Queensland University of Technology（昆士兰科技大学）；M3DICINE Pty Ltd（M3DICINE私人有限公司）
This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection of the heart state. We propose the use of recurrent neural networks and exploit recent advancements in attention based learning to segment the PCG signal. This allows the network to identify the most salient aspects of the signal and disregard uninformative information. The proposed method attains state-of-the-art performance on multiple benchmarks including b…
Inventec Corporation（英业达公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）；Chang Gung University of Science and Technology（长庚科技大学）
Survival modeling in healthcare relies on explainable statistical models; yet, their underlying assumptions are often simplistic and, thus, unrealistic. Machine learning models can estimate more complex relationships and lead to more accurate predictions, but are non-interpretable. This study shows it is possible to estimate hospitalization for congestive heart failure by a 30 seconds single-lead electrocardiogram signal. Using a machine learning approach not only results in greater predictive power but also provides clinically meaningful interpretations. We train an eXtreme Gradient Boosting …
Multi-Feature Fusion and Compressed Bi-LSTM for Memory-Efficient Heartbeat Classification on Wearable Devices
School of Electrical and Electronic Engineering, University College Dublin（都柏林大学学院电气与电子工程学院）；School of Electrical and Electronic Engineering, California Institute of Technology（加州理工学院电气与电子工程学院）
Early Prediction of Sepsis using Heart Rate Signals and Genetic Optimized LSTM Algorithm
Department of Mechatronics, School of Intelligent Systems College of Interdisciplinary Science and Technology University of Tehran（机械工程系、智能系统学院 interdisciplinary 科学与技术学院 波斯语大学）；School of Science and Technology Faculty of Science, Agriculture, Business and Law University of New England（科学与技术学院 科学、农业、商业与法律学院 新英格兰大学）；School of Project Management Faculty of Engineering The University of Sydney（项目管理学院 工程学院 悉尼大学）
University of Michigan（密歇根大学）；Institute for Healthcare Policy & Innovation（医疗政策与创新研究所）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Radiology（放射科）；Department of Internal Medicine（内科）
While deep learning has shown promise in improving the automated diagnosis of disease based on chest X-rays, deep networks may exhibit undesirable behavior related to shortcuts. This paper studies the case of spurious class skew in which patients with a particular attribute are spuriously more likely to have the outcome of interest. For instance, clinical protocols might lead to a dataset in which patients with pacemakers are disproportionately likely to have congestive heart failure. This skew can lead to models that take shortcuts by heavily relying on the biased attribute. We explore this p…
University of Isfahan（伊斯法罕大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；New Jersey Institute of Technology（新泽西理工学院）；Rutgers University（罗格斯大学）
Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to heart stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods: This paper in…
Recurrent neural networks have become ubiquitous in computing representations of sequential data, especially textual data in natural language processing. In particular, Bidirectional LSTMs are at the heart of several neural models achieving state-of-the-art performance in a wide variety of tasks in NLP. However, BiLSTMs are known to suffer from sequential bias - the contextual representation of a token is heavily influenced by tokens close to it in a sentence. We propose a general and effective improvement to the BiLSTM model which encodes each suffix and prefix of a sequence of tokens in both…
IBM Research – Zurich（IBM苏黎世研究院）；IBM Denmark（IBM丹麦）
In this work we perform a study of various unsupervised methods to identify mental stress in firefighter trainees based on unlabeled heart rate variability data. We collect RR interval time series data from nearly 100 firefighter trainees that participated in a drill. We explore and compare three methods in order to perform unsupervised stress detection: 1) traditional K-Means clustering with engineered time and frequency domain features 2) convolutional autoencoders and 3) long short-term memory (LSTM) autoencoders, both trained on the raw RRI measurements combined with DBSCAN clustering and …
Air pollution is a worldwide issue that affects the lives of many people in urban areas. It is considered that the air pollution may lead to heart and lung diseases. A careful and timely forecast of the air quality could help to reduce the exposure risk for affected people. In this paper, we use a data-driven approach to predict air quality based on historical data. We compare three popular methods for time series prediction: Exponential Smoothing (ES), Auto-Regressive Integrated Moving Average (ARIMA) and Long short-term memory (LSTM). Considering prediction accuracy and time complexity, our …
University of Southern California（南加州大学）；IBM T. J. Watson Research Center（IBM T. J. 沃森研究中心）
The widespread availability of electronic health records (EHRs) promises to usher in the era of personalized medicine. However, the problem of extracting useful clinical representations from longitudinal EHR data remains challenging. In this paper, we explore deep neural network models with learned medical feature embedding to deal with the problems of high dimensionality and temporality. Specifically, we use a multi-layer convolutional neural network (CNN) to parameterize the model and is thus able to capture complex non-linear longitudinal evolution of EHRs. Our model can effectively capture…
School of Computer & Information Sciences, University of Hyderabad（海得拉巴大学计算机与信息科学学院）
Language models, being at the heart of many NLP problems, are always of great interest to researchers. Neural language models come with the advantage of distributed representations and long range contexts. With its particular dynamics that allow the cycling of information within the network, `Recurrent neural network' (RNN) becomes an ideal paradigm for neural language modeling. Long Short-Term Memory (LSTM) architecture solves the inadequacies of the standard RNN in modeling long-range contexts. In spite of a plethora of RNN variants, possibility to add multiple memory cells in LSTM nodes was…
Medical diagnoses can shape and change the life of a person drastically. Therefore, it is always best advised to collect as much evidence as possible to be certain about the diagnosis. Unfortunately, in the case of the Brugada Syndrome (BrS), a rare and inherited heart disease, only one diagnostic criterion exists, namely, a typical pattern in the Electrocardiogram (ECG). In the following treatise, we question whether the investigation of ECG strips by the means of machine learning methods improves the detection of BrS positive cases and hence, the diagnostic process. We propose a pipeline tha…
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（健康结局与生物医学信息学系，佛罗里达大学医学院）；Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida（心血管医学部，医学院，佛罗里达大学）；Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida（血液学与肿瘤学部，医学院，佛罗里达大学）
Machine Learning Derived Blood Input for Dynamic PET Images of Rat Heart
Computer Science, University of Virginia（计算机科学，弗吉尼亚大学）；Radiology, University of Virginia（放射学，弗吉尼亚大学）；Biomedical Engineering, Charlottesville, VA, United States（生物医学工程，弗吉尼亚州夏洛特维尔）
Chalmers University of Technology（查尔姆斯理工大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions. In cycling, it is standard to equip bicycles with small computers recording data from sensors such as power-meters, in addition to heart-rate, speed, altitude etc. Recently, machine learning techniques have provided huge success in a wide variety of areas where large amounts of data (big data) is available. In this paper, we perform a pilot experiment on machine learning to model physical response in elite…
A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Houston（休斯顿大学）；Shanghai Jiao Tong University（上海交通大学）；New York University（纽约大学）；Tulane University（杜兰大学）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）
Facultad de Ingeniería, Universidad de Buenos Aires（布宜诺斯艾利斯大学工程学院）；Instituto Argentino de Matemática ”Alberto P. Calderón”, CONICET（国家科学技术研究委员会“阿尔贝托·P·卡尔德隆”阿根廷数学研究所）
In this work, a novel stack of well-known technologies is presented to determine an automatic method to segment the heart sounds in a phonocardiogram (PCG). We will show a deep recurrent neural network (DRNN) capable of segmenting a PCG into its main components and a very specific way of extracting instantaneous frequency that will play an important role in the training and testing of the proposed model. More specifically, it involves a Long Short-Term Memory (LSTM) neural network accompanied by the Fourier Synchrosqueezed Transform (FSST) used to extract instantaneous time-frequency features …
Patient Similarity Computation for Clinical Decision Support: An Efficient Use of Data Transformation, Combining Static and Time Series Data
Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh（计算机科学与工程系，孟加拉国工程与技术大学，达卡，孟加拉国）；College of Information Technology, United Arab Emirates University（信息技术学院，阿拉伯联合酋长国大学）
AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals
Engineering and Technology institute Groningen (ENTEG), University of Groningen（格罗宁根大学工程与技术学院（ENTEG））；Department of Human Movement Sciences, University Medical Center Groningen（格罗宁根大学医学中心人类运动科学系）
Thapar Institute of Engineering and Technology（塔帕尔工程技术学院）；University of St Andrews（圣安德鲁斯大学）
Heart Failure is a major component of healthcare expenditure and a leading cause of mortality worldwide. Despite higher inter-rater variability, endomyocardial biopsy (EMB) is still regarded as the standard technique, used to identify the cause (e.g. ischemic or non-ischemic cardiomyopathy, coronary artery disease, myocardial infarction etc.) of unexplained heart failure. In this paper, we focus on identifying cardiomyopathy as ischemic or non-ischemic. For this, we propose and implement a new unified architecture comprising CNN (inception-V3 model) and bidirectional LSTM (BiLSTM) with self-at…
Inertial Measurement Unit (IMU) sensors are present in everyday devices such as smartphones and fitness watches. As a result, the array of health-related research and applications that tap onto this data has been growing, but little attention has been devoted to the prediction of an individual's heart rate (HR) from IMU data, when undergoing a physical activity. Would that be even possible? If so, this could be used to design personalized sets of aerobic exercises, for instance. In this work, we show that it is viable to obtain accurate HR predictions from IMU data using Recurrent Neural Netwo…
University of Delaware（特拉华大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Taipei Veterans General Hospital（台北荣民总医院）；National Yang-Ming University（阳明大学）；University of California San Francisco（加利福尼亚大学旧金山分校）
Hypertrophic cardiomyopathy (HCM) patients have a high incidence of atrial fibrillation (AF) and increased stroke risk, even with low risk of congestive heart failure, hypertension, age, diabetes, previous stroke/transient ischemic attack scores. Hence, there is a need to understand the pathophysiology of AF and stroke in HCM. In this retrospective study, we develop and apply a data-driven, machine learning based method to identify AF cases, and clinical and imaging features associated with AF, using electronic health record data. HCM patients with documented paroxysmal/persistent/permanent AF…
Institute for Informatics, LMU Munich（慕尼黑大学信息学研究所）
Common to all different kinds of recurrent neural networks (RNNs) is the intention to model relations between data points through time. When there is no immediate relationship between subsequent data points (like when the data points are generated at random, e.g.), we show that RNNs are still able to remember a few data points back into the sequence by memorizing them by heart using standard backpropagation. However, we also show that for classical RNNs, LSTM and GRU networks the distance of data points between recurrent calls that can be reproduced this way is highly limited (compared to even…
Reliable generalization lies at the heart of safe ML and AI. However, understanding when and how neural networks generalize remains one of the most important unsolved problems in the field. In this work, we conduct an extensive empirical study (20'910 models, 15 tasks) to investigate whether insights from the theory of computation can predict the limits of neural network generalization in practice. We demonstrate that grouping tasks according to the Chomsky hierarchy allows us to forecast whether certain architectures will be able to generalize to out-of-distribution inputs. This includes nega…
Optimizing Disease Prediction with Artificial Intelligence Driven Feature Selection and Attention Networks
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology（Vel Tech Rangarajan Dr. Sagunthala 研发科学与技术研究所）；R.M.K. Engineering College（R.M.K.工程学院）；P.S.R. Engineering College（P.S.R.工程学院）
School of Biomedical Informatics, University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心生物医学信息学院）；School of Public Health, University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心公共卫生学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Computer Science Department, School of Engineering, Rice University（莱斯大学工程学院计算机科学系）
Recently, there is great interest to investigate the application of deep learning models for the prediction of clinical events using electronic health records (EHR) data. In EHR data, a patient's history is often represented as a sequence of visits, and each visit contains multiple events. As a result, deep learning models developed for sequence modeling, like recurrent neural networks (RNNs) are common architecture for EHR-based clinical events predictive models. While a large variety of RNN models were proposed in the literature, it is unclear if complex architecture innovations will offer s…
