Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality. Because of its highly complex underlying causality and limited observable nature, causal inference on EHR is quite challenging. Deep Learning (DL) achieved great success among the advanced machine learning methodologies. Nevertheless, it is still obstructed by the inappropriately assumed causal conditions. This work proposed a novel method to quantify clinically well-defined causal effects as a generalized estimation vector that is simply utilizable for causal models. We incorporated it into DL models to achieve better predictive performance and result interpretation. Furthermore, we also proved the existence of causal information blink spots that regular DL models cannot reach.
A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR data
Department of Biomedical Informatics Vanderbilt University Medical Center（生物医学信息学系范德堡大学医学中心）；Department of Biomedical Informatics University of Pittsburgh（生物医学信息学系匹兹堡大学）；Departments of Medicine & Biomedical Informatics Vanderbilt University Medical Center（医学与生物医学信息学系范德堡大学医学中心）；Departments of Biomedical Informatics & Computer Science Vanderbilt University Medical Center & Vanderbilt University（生物医学信息学与计算机科学系范德堡大学医学中心及范德堡大学）
Marquette University（马凯特大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩郡分校）；Oregon Health & Science University（俄勒冈健康与科学大学）
Delirium occurs in about 80% cases in the Intensive Care Unit (ICU) and is associated with a longer hospital stay, increased mortality and other related issues. Delirium does not have any biomarker-based diagnosis and is commonly treated with antipsychotic drugs (APD). However, multiple studies have shown controversy over the efficacy or safety of APD in treating delirium. Since randomized controlled trials (RCT) are costly and time-expensive, we aim to approach the research question of the efficacy of APD in the treatment of delirium using retrospective cohort analysis. We plan to use the Cau…
Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
The Pennsylvania State University（宾夕法尼亚州立大学）；Dalian University of Technology（大连理工大学）；Purdue University（普渡大学）；Iowa State University（爱荷华州立大学）；GE Healthcare（GE医疗）
Experiments remain the gold standard to establish an understanding of fire-related phenomena. A primary goal in designing tests is to uncover the data generating process (i.e., the how and why the observations we see come to be); or simply what causes such observations. Uncovering such a process not only advances our knowledge but also provides us with the capability to be able to predict phenomena accurately. This paper presents an approach that leverages causal discovery and causal inference to evaluate the fire resistance of structural members. In this approach, causal discovery algorithms …
Institute of Computer Science, FORTH, Greece（希腊FORTH研究所计算机科学研究所）；University of Crete, Greece（希腊克里特大学）
We consider the incorporation of causal knowledge about the presence or absence of (possibly indirect) causal relations into a causal model. Such causal relations correspond to directed paths in a causal model. This type of knowledge naturally arises from experimental data, among others. Specifically, we consider the formalisms of Causal Bayesian Networks and Maximal Ancestral Graphs and their Markov equivalence classes: Partially Directed Acyclic Graphs and Partially Oriented Ancestral Graphs. We introduce sound and complete procedures which are able to incorporate causal prior knowledge in s…
Institute for Health Informatics, University of Minnesota（明尼苏达大学健康信息学研究所）；Mayo Clinic（梅奥诊所）
Introduction: The discovery of causal mechanisms underlying diseases enables better diagnosis, prognosis and treatment selection. Clinical trials have been the gold standard for determining causality, but they are resource intensive, sometimes infeasible or unethical. Electronic Health Records (EHR) contain a wealth of real-world data that holds promise for the discovery of disease mechanisms, yet the existing causal structure discovery (CSD) methods fall short on leveraging them due to the special characteristics of the EHR data. We propose a new data transformation method and a novel CSD alg…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Sharing electronic health records (EHRs) on a large scale may lead to privacy intrusions. Recent research has shown that risks may be mitigated by simulating EHRs through generative adversarial network (GAN) frameworks. Yet the methods developed to date are limited because they 1) focus on generating data of a single type (e.g., diagnosis codes), neglecting other data types (e.g., demographics, procedures or vital signs) and 2) do not represent constraints between features. In this paper, we introduce a method to simulate EHRs composed of multiple data types by 1) refining the GAN model, 2) ac…
Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；Geisinger（盖辛格）
Embedding algorithms are increasingly used to represent clinical concepts in healthcare for improving machine learning tasks such as clinical phenotyping and disease prediction. Recent studies have adapted state-of-the-art bidirectional encoder representations from transformers (BERT) architecture to structured electronic health records (EHR) data for the generation of contextualized concept embeddings, yet do not fully incorporate temporal data across multiple clinical domains. Therefore we developed a new BERT adaptation, CEHR-BERT, to incorporate temporal information using a hybrid approach…
Incorporating causal knowledge and mechanisms is essential for refining causal models and improving downstream tasks such as designing new treatments. In this paper, we introduce a novel concept in causal discovery, termed interventional constraints, which differs fundamentally from interventional data. While interventional data require direct perturbations of variables, interventional constraints encode high-level causal knowledge in the form of inequality constraints on causal effects. For instance, in the Sachs dataset (Sachs et al.\ 2005), Akt has been shown to be activated by PIP3, meanin…
Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
School of Computer Science, Guangdong University of Technology（广东工业大学计算机科学学院）；Peng Cheng Laboratory（鹏城实验室）；Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed人工智能大学机器学习系）；College of Mathematics and Computer Science, Shantou University（汕头大学数学与计算机科学学院）
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Cambridge（剑桥大学）；Max Planck ETH Center for Learning Systems（马克斯·普朗克-苏黎世联邦理工学院学习系统中心）
The ability to learn and act in novel situations is still a prerogative of animate intelligence, as current machine learning methods mostly fail when moving beyond the standard i.i.d. setting. What is the reason for this discrepancy? Most machine learning tasks are anti-causal, i.e., we infer causes (labels) from effects (observations). Typically, in supervised learning we build systems that try to directly invert causal mechanisms. Instead, in this paper we argue that strong generalization capabilities crucially hinge on searching and validating meaningful hypotheses, requiring access to a ca…
UT-Battelle, LLC（UT-巴特勒有限责任公司）；U.S. Department of Energy（美国能源部）
Deep neural networks ("deep learning") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, and in just a few years has led to superhuman level performance and ushered in a new wave of "AI." Buoyed by these successes, researchers in the physical sciences have made steady progress in incorporating deep learning into their respective domains. However, such adoption brings substantial challenges that need to be recognized and confronted. Here, we discuss both opportunities and roadblocks to implementation of de…
In medicine, researchers often seek to infer the effects of a given treatment on patients' outcomes. However, the standard methods for causal survival analysis make simplistic assumptions about the data-generating process and cannot capture complex interactions among patient covariates. We introduce the Dynamic Survival Transformer (DynST), a deep survival model that trains on electronic health records (EHRs). Unlike previous transformers used in survival analysis, DynST can make use of time-varying information to predict evolving survival probabilities. We derive a semi-synthetic EHR dataset …
Massachusetts Institute of Technology（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior research has demonstrated the ability to construct such a graph from over 270,000 emergency department patient visits. In this work, we describe methods to evaluate a health knowledge graph for robustness. Moving beyond precision and recall, we analyze for which diseases and for…
Massachusetts Institute of Technology（麻省理工学院）；Enolink Inc.（Enolink公司）；Seoul National University Hospital Healthcare System Gangnam Center（首尔大学医院医疗系统江南中心）
With increasing interest in applying machine learning to develop healthcare solutions, there is a desire to create interpretable deep learning models for survival analysis. In this paper, we extend the Neural Additive Model (NAM) by incorporating pairwise feature interaction networks and equip these models with loss functions that fit both proportional and non-proportional extensions of the Cox model. We show that within this extended framework, we can construct non-proportional hazard models, which we call TimeNAM, that significantly improve performance over the standard NAM model architectur…
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Shanghai Jiao Tong University（上海交通大学）；Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University（中山大学机器智能与先进计算重点实验室）
Peng Cheng Laboratory（鹏城实验室）；Carnegie Mellon University（卡内基梅隆大学）；University of Toronto Mississauga（多伦多大学密西沙加分校）；Peking University（北京大学）
Background: Electronic Health Records (EHRs) contain rich information of patients' health history, which usually include both structured and unstructured data. There have been many studies focusing on distilling valuable information from structured data, such as disease codes, laboratory test results, and treatments. However, relying on structured data only might be insufficient in reflecting patients' comprehensive information and such data may occasionally contain erroneous records. Objective: With the recent advances of machine learning (ML) and deep learning (DL) techniques, an increasing …
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
Simula Metropolitan Center for Digital Engineering（西姆拉大都会数字工程中心）；Oslo Metropolitan University（奥斯陆城市大学）；Swammerdam Institute for Life Sciences, University of Amsterdam（阿姆斯特丹大学斯瓦默丹生命科学研究所）
Explaining AI systems is fundamental both to the development of high performing models and to the trust placed in them by their users. The Shapley framework for explainability has strength in its general applicability combined with its precise, rigorous foundation: it provides a common, model-agnostic language for AI explainability and uniquely satisfies a set of intuitive mathematical axioms. However, Shapley values are too restrictive in one significant regard: they ignore all causal structure in the data. We introduce a less restrictive framework, Asymmetric Shapley values (ASVs), which are…
Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions. When a CG is available, e.g., from the domain knowledge, we can infer the conditional independence (CI) relations that should hold in the data distribution. However, it is not straightforward how to incorporate this knowledge into predictive modeling. In this work, we propose a model-agnostic data augmentation method that allows us to exploit the prior knowledge of the CI encoded in a CG for supervised machine learning. We theoretically justify the proposed method by…
Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment
Department of Biostatistics and Bioinformatics, Duke University School of Medicine（生物统计学与生物信息学系，杜克大学医学中心）；Department of Pediatrics, Duke University School of Medicine（儿科学系，杜克大学医学中心）；Duke Center for Autism and Brain Development, Duke University School of Medicine（杜克大学自主与脑发展中心，杜克大学医学中心）；Department of Psychiatry and Behavioral Sciences, Durham, NC, USA（精神病学与行为科学系，新伯尔尼，NC，美国）
Electronic Health Record (EHR) data can be represented as discrete counts over a high dimensional set of possible procedures, diagnoses, and medications. Supervised topic models present an attractive option for incorporating EHR data as features into a prediction problem: given a patient's record, we estimate a set of latent factors that are predictive of the response variable. However, existing methods for supervised topic modeling struggle to balance prediction quality and coherence of the latent factors. We introduce a novel approach, the prediction-focused topic model, that uses the superv…
Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）
Background: The increasing adoption of electronic health records (EHR) across the US has created troves of computable data, to which machine learning methods have been applied to extract useful insights. EHR data, represented as a three-dimensional analogue of a matrix (tensor), is decomposed into two-dimensional factors that can be interpreted as computational phenotypes. Methods: We apply constrained tensor factorization to derive computational phenotypes and predict mortality in cohorts of patients with breast, prostate, colorectal, or lung cancer in the Northwestern Medicine Enterprise Dat…
Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data
Department of Biostatistics, Harvard T.H. Chan School of Public Health（哈佛T.H. 洪学校公共卫生学院生物统计学系）；Department of Mathematical Sciences, Bentley University（贝纳特大学数学科学系）；Department of Neurology, University of Pittsburgh（匹兹堡大学神经病学系）；Department of Biostatistics, Peking University Health Science Center（北京大学健康科学中心生物统计学系）；Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）
