Sequential Diagnosis Prediction with Transformer and Ontological Representation
Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；School of Information Technology and Electrical Engineering, The University of Queensland（昆士兰大学信息技术与电气工程学院）
Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang
备注：
10 pages, 5 figures, Accepted by IEEE ICDM 2021. arXiv admin note: text overlap with arXiv:2107.09288
Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a patient's interactions with healthcare systems, has numerous inherent characteristics of temporality, irregularity and data insufficiency. Some recent works train healthcare predictive models by making use of sequential information in EHR data, but they are vulnerable to irregular, temporal EHR data with the states of admission/discharge from hospital, and insufficient data. To mitigate this, we propose an end-to-end robust transformer-based model called SETOR, which exploits neural ordinary differential equation to handle both irregular intervals between a patient's visits with admitted timestamps and length of stay in each visit, to alleviate the limitation of insufficient data by integrating medical ontology, and to capture the dependencies between the patient's visits by employing multi-layer transformer blocks. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR not only achieves better predictive results than previous state-of-the-art approaches, irrespective of sufficient or insufficient training data, but also derives more interpretable embeddings of medical codes. The experimental codes are available at the GitHub repository (https://github.com/Xueping/SETOR).
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems
University of Science, VNUHCM（胡志明市国家大学科学大学）；Viet Nam National University, Ho Chi Minh City（越南国家大学胡志明市分校）；Ho Chi Minh City Hospital for Traumatology and Orthopedics（胡志明市创伤与骨科医院）
Debugging of ontologies is an important prerequisite for their wide-spread application, especially in areas that rely upon everyday users to create and maintain knowledge bases, as in the case of the Semantic Web. Recent approaches use diagnosis methods to identify causes of inconsistent or incoherent ontologies. However, in most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle …
The working conditions of large-scale industrial systems are very complex. Once a failure occurs, it will affect industrial production, cause property damage, and even endanger the workers' lives. Therefore, it is important to control the operation of the system to accurately grasp the operation status of the system and find out the failure in time. The occurrence of system failure is a gradual process, and the occurrence of the current system failure may depend on the previous state of the system, which is sequential. The fault diagnosis technology based on time series can monitor the operati…
Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University（郑州大学公共卫生学院流行病学与生物统计学系）；Victorian Melanoma Service, Alfred Health（阿尔弗雷德健康服务中心维多利亚黑色素瘤中心）；eResearch Centre, Monash University（莫纳什大学电子研究中心）
Dermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis are developed using single time-point images of lesions. Ignoring the temporal, morphological changes of lesions can lead to misdiagnosis in borderline cases. In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images. To this end, we construct our method in three steps. First, we align sequential dermoscopic images of skin lesions using estimated Euclidean transfo…
Medical automatic diagnosis aims to imitate human doctors in real-world diagnostic processes and to achieve accurate diagnoses by interacting with the patients. The task is formulated as a sequential decision-making problem with a series of symptom inquiring steps and the final diagnosis. Recent research has studied incorporating reinforcement learning for symptom inquiring and classification techniques for disease diagnosis, respectively. However, studies on efficiently and effectively combining the two procedures are still lacking. To address this issue, we devise an adaptive mechanism to al…
HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition
Technical University of Munich（慕尼黑工业大学）；Helmholtz Center Munich（亥姆霍兹慕尼黑中心）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；Aalto University（阿尔托大学）
MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Australian AI Institute, Faculty of Engineering and IT, University of Technology Sydney（澳大利亚人工智能研究所，工程与信息技术学院，悉尼技术大学）；School of Information Technology and Electrical Engineering, The University of Queensland（信息科技与电气工程学院，昆士兰大学）；Department of Data Science and AI, The Hong Kong Polytechnic University（数据科学与人工智能学院，香港理工大学）；Health Economics and Research Division, Department of Health and Aged Care（健康经济学与研究部，健康与老龄化部）
Development of CODO: A Comprehensive Tool for COVID-19 Data Representation, Analysis, and Visualization
Documentation Research and Training Centre (DRTC), Indian Statistical Institute（印度统计研究所文献研究与培训中心（DRTC））；Department of Library and Information Science, Calcutta University（加尔各答大学图书情报学系）
ActiveMedAgent: Cost-Aware Trajectory Learning for Multimodal Medical Diagnosis
Washington University in St. Louis（圣路易斯华盛顿大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Arizona State University（亚利桑那州立大学）；Yale University（耶鲁大学）；Amazon（亚马逊公司）
Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost. Clinical diagnostic data are often highly imbalanced; therefore, we aim to maximize the $F_1$ score instead of the error rate. However, optimizing the non-concave $F_1$ score is not a classic RL problem, thus invalidates standard RL methods. To remedy this issue, we develop a reward shaping approach, leveraging properties of the …
Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits
University of Southern California（南加州大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Texas Tech University（德克萨斯理工大学）；Southern Methodist University（南卫理公会大学）
The fault diagnosis of rolling bearings is a critical technique to realize predictive maintenance for mechanical condition monitoring. In real industrial systems, the main challenges for the fault diagnosis of rolling bearings pertain to the accuracy and real-time requirements. Most existing methods focus on ensuring the accuracy, and the real-time requirement is often neglected. In this paper, considering both requirements, we propose a novel fast fault diagnosis method for rolling bearings, based on extreme learning machine (ELM) and logistic mapping, named logistic-ELM. First, we identify 1…
Accurate and early prediction of a disease allows to plan and improve a patient's quality of future life. During pandemic situations, the medical decision becomes a speed challenge in which physicians have to act fast to diagnose and predict the risk of the severity of the disease, moreover this is also of high priority for neurodegenerative diseases like Parkinson's disease. Machine Learning (ML) models with Features Selection (FS) techniques can be applied to help physicians to quickly diagnose a disease. FS optimally subset features that improve a model performance and help reduce the numbe…
During applying data-oriented diagnosis systems to distinguishing the type of and evaluating the severity of nuclear power plant initial events, it is of vital importance to decide which parameters to be used as the system input. However, although several diagnosis systems have already achieved acceptable performance in diagnosis precision and speed, hardly have the researchers discussed the method of monitoring point choosing and its layout. For this reason, redundant measuring data are used to train the diagnostic model, leading to high uncertainty of the classification, extra training time …
In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention. EHR data consist of a sequence of medical visits, i.e. a multivariate time series of diagnosis, medications, physical examinations, lab tests, etc. This sequential nature makes EHR well matching the power of Recurrent Neural Network (RNN). In this paper, we propose "Deep Diabetologist" - using RNNs for EHR sequential data modelling, to provide the personalized hyperglycemia medication prediction for diabetic patients. Particularly, we develop a hierarchical RNN to capture the hete…
Emory University School of Medicine（埃默里大学医学院）；Georgia Institute of Technology（佐治亚理工学院）
Multistage sequential decision-making scenarios are commonly seen in the healthcare diagnosis process. In this paper, an active learning-based method is developed to actively collect only the necessary patient data in a sequential manner. There are two novelties in the proposed method. First, unlike the existing ordinal logistic regression model which only models a single stage, we estimate the parameters for all stages together. Second, it is assumed that the coefficients for common features in different stages are kept consistent. The effectiveness of the proposed method is validated in both…
Xerox（施乐公司）；SUNY Buffalo（纽约州立大学布法罗分校）；Conduent Labs US（Conduent美国实验室）；University of Rochester（罗切斯特大学）；United Technologies Research Center（联合技术研究中心）
Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data an…
Centre for Pattern Recognition and Data Analytics（模式识别与数据分析中心）；Deakin University（迪肯大学）
In this paper, we consider the patient similarity matching problem over a cancer cohort of more than 220,000 patients. Our approach first leverages on Word2Vec framework to embed ICD codes into vector-valued representation. We then propose a sequential algorithm for case-control matching on this representation space of diagnosis codes. The novel practice of applying the sequential matching on the vector representation lifted the matching accuracy measured through multiple clinical outcomes. We reported the results on a large-scale dataset to demonstrate the effectiveness of our method. For suc…
St. Michael’s Hospital（圣迈克尔医院）；Unity Health Toronto（多伦多统一健康中心）；University of Toronto（多伦多大学）；Stanford University（斯坦福大学）
Fractures of the cervical spine are a medical emergency and may lead to permanent paralysis and even death. Accurate diagnosis in patients with suspected fractures by computed tomography (CT) is critical to patient management. In this paper, we propose a deep convolutional neural network (DCNN) with a bidirectional long-short term memory (BLSTM) layer for the automated detection of cervical spine fractures in CT axial images. We used an annotated dataset of 3,666 CT scans (729 positive and 2,937 negative cases) to train and validate the model. The validation results show a classification accur…
UC3M（马德里卡洛斯三世大学）；Health Research Institute Gregorio Marañón（格雷戈里奥·马拉尼翁健康研究所）；Lapeyronie Hospital（拉佩罗尼医院）；University of Montpellier（蒙彼利埃大学）；Universidad Autónoma de Madrid（马德里自治大学）
This article presents a novel method for predicting suicidal ideation from Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly-sampled data sequences. In our method, we model each of them with a Recurrent Neural Network (RNN), and both sequences are aligned by concatenating the hidden state of each of them using temporal marks. Furthermore, we incorporate attention schemes to improve performance in long sequences and time-independent …
Georgia Institute of Technology（佐治亚理工学院）；Children Healthcare of Atlanta（亚特兰大儿童医疗保健机构）
Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification. Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits will have broad applications in healthcare analytics. However, in Electronic Health Records (EHR) the visit sequences of patients include multiple concepts (diagnosis, procedure, and medication codes) per visit. This structure provides two types of relational information, namely sequential order of visits and co-occurrence of the …
Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography
Concordia Institute for Information Systems Engineering (CIISE)（康科德信息系统工程研究所）；Concordia University（康科德大学）；Thomas Jefferson University Hospital（托马斯·杰斐逊大学医院）
The diagnosis of heart diseases is a difficult task generally addressed by an appropriate examination of patients clinical data. Recently, the use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, has proved to be a valuable support in the diagnosis process. However, till now, ischemic heart disease (IHD) has been diagnosed on the basis of Artificial Neural Networks (ANN) applied only to signs, symptoms and sequential ECG and coronary angiography, an invasive tool, while could be probably identified in a non-invasive way by using parameters extracted from…
Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound（国家级区域医疗超声关键技术工程实验室）；School of Biomedical Engineering（生物医学工程学院）；Medical School（医学院）；Shenzhen University（深圳大学）；Medical UltraSound Image Computing (MUSIC) Lab（医学超声图像计算（MUSIC）实验室）；School of Artificial Intelligence（人工智能学院）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Department of Electronic and Computer Engineering（电子与计算机工程系）；The University of Manchester（曼彻斯特大学）；The Third Affiliated Hospital of Sun Yat-sen University（中山大学第三附属医院）；Longgang District People’s Hospital of Shenzhen（深圳龙岗区人民医院）；The Second Affiliated Hospital of The Chinese University of Hong Kong（香港中文大学第二附属医院）；School of Biomedical Engineering and Informatics（生物医学工程与信息学学院）；NIHR Manchester Biomedical Research Centre（NIHR曼彻斯特生物医学研究中心）
Sequential data is everywhere, and it can serve as a basis for research that will lead to improved processes. For example, road infrastructure can be improved by identifying bottlenecks in GPS data, or early diagnosis can be improved by analyzing patterns of disease progression in medical data. The main obstacle is that access and use of such data is usually limited or not permitted at all due to concerns about violating user privacy, and rightly so. Anonymizing sequence data is not a simple task, since a user creates an almost unique signature over time. Existing anonymization methods reduce …
