StageNet: Stage-Aware Neural Networks for Health Risk Prediction
Peking University（北京大学）；IQVIA（艾昆纬）；Peking University Third Hospital（北京大学第三医院）；Temple University（天普大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Georgia Institute of Technology（佐治亚理工学院）
Junyi Gao, Cao Xiao, Yasha Wang, Wen Tang, Lucas M. Glass, Jimeng Sun
Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease Network (StageNet) model to extract disease stage information from patient data and integrate it into risk prediction. StageNet is enabled by (1) a stage-aware long short-term memory (LSTM) module that extracts health stage variations unsupervisedly; (2) a stage-adaptive convolutional module that incorporates stage-related progression patterns into risk prediction. We evaluate StageNet on two real-world datasets and show that StageNet outperforms state-of-the-art models in risk prediction task and patient subtyping task. Compared to the best baseline model, StageNet achieves up to 12% higher AUPRC for risk prediction task on two real-world patient datasets. StageNet also achieves over 58% higher Calinski-Harabasz score (a cluster quality metric) for a patient subtyping task.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data
Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers
Department of Information Sciences and Technology（信息科学与技术系）；George Mason University（乔治·马歇尔大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Jahangirnagar University（贾汗吉尔纳加尔大学）；AIUB；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Dhaka University of Engineering and Technology（达卡工程与技术大学）
Context-aware deep learning using individualized prior information reduces false positives in disease risk prediction and longitudinal health assessment
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Examples include finding lifestyle factors associated with chronic diseases and finding genetic mutations associated with diseases in precision health. We develop a combined semantic and machine learning system that uses a health risk ontology and knowledge graph (KG) to dynamically discover risk factors and their associated subpopulations. Semantics and the novel supervised cadre model make our system explainable. Future …
Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the information in claims data. Moreover, the development of such linear regression models requires substantial domain expert knowledge and computational effort for data preprocessing. In this paper, we propose a novel approach for risk adjustment that uses semantic embeddings to represent …
Cornell University（康奈尔大学）；Michigan State University（密歇根州立大学）；University of Michigan（密歇根大学）；Oregon Health & Science University（俄勒冈健康与科学大学）
In recent years, increasingly augmentation of health data, such as patient Electronic Health Records (EHR), are becoming readily available. This provides an unprecedented opportunity for knowledge discovery and data mining algorithms to dig insights from them, which can, later on, be helpful to the improvement of the quality of care delivery. Predictive modeling of clinical risk, including in-hospital mortality, hospital readmission, chronic disease onset, condition exacerbation, etc., from patient EHR, is one of the health data analytic problems that attract most of the interests. The reason …
A primary motivation for the development and implementation of structural health monitoring systems, is the prospect of gaining the ability to make informed decisions regarding the operation and maintenance of structures and infrastructure. Unfortunately, descriptive labels for measured data corresponding to health-state information for the structure of interest are seldom available prior to the implementation of a monitoring system. This issue limits the applicability of the traditional supervised and unsupervised approaches to machine learning in the development of statistical classifiers fo…
The quality of healthcare provided by hospitals is subject to considerable variability. Consequently, accurate measurements of hospital performance are essential for various decision-makers, including patients, hospital managers and health insurers. Hospital performance is assessed via the health outcomes of their patients. However, as the risk profiles of patients between hospitals vary, measuring hospital performance requires adjustment for patient risk. This task is formalized in the state-of-the-art procedure through a hierarchical generalized linear model, that isolates hospital fixed-eff…
University of Southern California（南加州大学）；AI Foundations, IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心AI基础部门）；Binghamton University, SUNY（纽约州立大学宾汉姆顿大学）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attentions. Recent progress in the design and applications of deep learning methods has shown promising results and is forcing massive changes in healthcare academia and industry, but most of these methods rely on massive labeled data. In this work, we propose a general deep learning framework which is able to boost risk prediction performance with limited EHR data. Our model takes a modified generative adversarial network …
Penn State（宾夕法尼亚州立大学）；IQVIA（艾昆纬）；Temple University（天普大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）
Successful health risk prediction demands accuracy and reliability of the model. Existing predictive models mainly depend on mining electronic health records (EHR) with advanced deep learning techniques to improve model accuracy. However, they all ignore the importance of publicly available online health data, especially socioeconomic status, environmental factors, and detailed demographic information for each location, which are all strong predictive signals and can definitely augment precision medicine. To achieve model reliability, the model needs to provide accurate prediction and uncertai…
The Pennsylvania State University（宾夕法尼亚州立大学）；Shanghai Jiao Tong University（上海交通大学）；Amplitude（Amplitude公司）
Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments. To evaluate their robustness, existing research conducts adversarial attacks in the white/gray-box setting where model parameters are accessible. However, a more realistic black-box adversarial attack is ignored even though most real-world models are trained with private data and released as black-box services on the cloud. To fill this gap, we propose the first black-box adversarial attack method against health risk prediction models named MedAttacker to investigate …
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
The Pennsylvania State University（宾夕法尼亚州立大学）；Purdue University（普渡大学）；Georgia State University（佐治亚州立大学）；Iowa State University（爱荷华州立大学）；Stony Brook University（石溪大学）
In 2019, The Centers for Medicare and Medicaid Services (CMS) launched an Artificial Intelligence (AI) Health Outcomes Challenge seeking solutions to predict risk in value-based care for incorporation into CMS Innovation Center payment and service delivery models. Recently, modern language models have played key roles in a number of health related tasks. This paper presents, to the best of our knowledge, the first application of these models to patient readmission prediction. To facilitate this, we create a dataset of 1.2 million medical history samples derived from the Limited Dataset (LDS) i…
In many public health settings, there is a perceived tension between allocating resources to known vulnerable areas and learning about the overall prevalence of the problem. Inspired by a door-to-door Covid-19 testing program we helped design, we combine multi-armed bandit strategies and insights from sampling theory to demonstrate how to recover accurate prevalence estimates while continuing to allocate resources to at-risk areas. We use the outbreak of an infectious disease as our running example. The public health setting has several characteristics distinguishing it from typical bandit set…
Predict and Interpret Health Risk using EHR through Typical Patients
Peking University（北京大学）；Key Laboratory of High Confidence Software Technologies, Ministry of Education（高可信软件技术教育部重点实验室）；National Engineering Research Center of Software Engineering, Peking University（北京大学软件工程国家工程研究中心）；Peking University Third Hospital（北京大学第三医院）；Coventry University（考文垂大学）
Explainable artificial intelligence is increasingly used in machine learning (ML) based decision-making systems in healthcare. However, little research has compared the utility of different explanation methods in guiding healthcare experts for patient care. Moreover, it is unclear how useful, understandable, actionable and trustworthy these methods are for healthcare experts, as they often require technical ML knowledge. This paper presents an explanation dashboard that predicts the risk of diabetes onset and explains those predictions with data-centric, feature-importance, and example-based e…
Predicting the health risks of patients using Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Health risk refers to the probability of the occurrence of a specific health outcome for a specific patient. The predicted risks can be used to support decision-making by healthcare professionals. EHRs are structured patient journey data. Each patient journey contains a chronological set of clinical events, and within each clinical event, there is a set of clinical/medical activities. Due to variations o…
Northeastern University（东北大学）；Partners Connected Health Innovation（帕特纳医疗健康创新中心）；Harvard Medical School（哈佛医学院）
Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients at risk for uncontrolled hypertension would allow targeted use of personalized, proactive treatments. We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within…
In this paper, we consider a risk-averse multi-armed bandit (MAB) problem where the goal is to learn a policy that minimizes the risk of low expected return, as opposed to maximizing the expected return itself, which is the objective in the usual approach to risk-neutral MAB. Specifically, we formulate this problem as a transfer learning problem between an expert and a learner agent in the presence of contexts that are only observable by the expert but not by the learner. Thus, such contexts are unobserved confounders (UCs) from the learner's perspective. Given a dataset generated by the exper…
Most children infected with COVID-19 have no or mild symptoms and can recover automatically by themselves, but some pediatric COVID-19 patients need to be hospitalized or even to receive intensive medical care (e.g., invasive mechanical ventilation or cardiovascular support) to recover from the illnesses. Therefore, it is critical to predict the severe health risk that COVID-19 infection poses to children to provide precise and timely medical care for vulnerable pediatric COVID-19 patients. However, predicting the severe health risk for COVID-19 patients including children remains a significan…
Machine Learning Models for Predicting Smoking-Related Health Decline and Disease Risk
School of Artificial Intelligence and Computer Science Nantong University（人工智能与计算机科学学院 南通大学）；School of Transportation and Civil Engineering Nantong University（交通与土木工程学院 南通大学）；School of Mechanical Engineering Nantong University（机械工程学院 南通大学）
K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations
University of Roehampton（罗汉普顿大学）；Kivira Health；University of Hertfordshire（赫特福德大学）；University of Surrey（萨里大学）；University of Bedfordshire（贝德福德大学）；Tavistock Relationships（塔维斯托克关系中心）；InsideOut
AI-based modular warning machine for risk identification in proximity healthcare
Department of Electrical, Computer and Biomedical Engineering, Università di Pavia, Pavia, Italy（电气、计算机与生物医学工程系，普瓦亚大学，普瓦亚，意大利）；Neuroscience and Human Genetics Department, Meyer Children's Hospital IRCCS, Firenze, Italy（神经科学与人类遗传学系，梅耶儿童医院IRCCS，佛罗伦萨，意大利）
An Investigation of Memorization Risk in Healthcare Foundation Models
MIT Broad Institute of MIT and Harvard（MIT Broad研究所（MIT和哈佛））；Vector Institute（向量研究所）；MIT；Chalmers University of Technology（查尔姆斯理工大学）；University of Gothenburg（哥德堡大学）；University of Toronto（多伦多大学）；University Health Network (UHN)（大学健康网络）；Worcester Polytechnic Institute（沃思维尔理工学院）；Computer Science Department（计算机科学系）
University of Wisconsin - Madison（威斯康星大学麦迪逊分校）；Marshfield Clinic Research Institute（马什菲尔德诊所研究所）；Stanford University（斯坦福大学）
The widespread digitization of patient data via electronic health records (EHRs) has created an unprecedented opportunity to use machine learning algorithms to better predict disease risk at the patient level. Although predictive models have previously been constructed for a few important diseases, such as breast cancer and myocardial infarction, we currently know very little about how accurately the risk for most diseases or events can be predicted, and how far in advance. Machine learning algorithms use training data rather than preprogrammed rules to make predictions and are well suited for…
Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care
Yale University（耶鲁大学）；Missouri S & T（密苏里科技大学）；Institute for Health Innovation and Technology (iHealthtech), NUS（新加坡国立大学健康创新与技术研究所（iHealthtech））；Singapore University of Technology and Design（新加坡科技设计大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Jivox（Jivox公司）；ORG Research（ORG研究公司）；Georgia Institute of Technology（佐治亚理工学院）
Unveiling Population Heterogeneity in Health Risks Posed by Environmental Hazards Using Regression-Guided Neural Network
University of Southern California（南加州大学）；Leonard Davis School of Gerontology, University of Southern California（南加州大学伦纳德·戴维斯老年医学院）；University of Minnesota（明尼苏达大学）
High quality risk adjustment in health insurance markets weakens insurer incentives to engage in inefficient behavior to attract lower-cost enrollees. We propose a novel methodology based on Markov Chain Monte Carlo methods to improve risk adjustment by clustering diagnostic codes into risk groups optimal for health expenditure prediction. We test the performance of our methodology against common alternatives using panel data from 500 thousand enrollees of the Colombian Healthcare System. Results show that our methodology outperforms common alternatives and suggest that it has potential to imp…
Social Determinants of Health and Fentanyl Overdose Mortality Across US Counties: An XGBoost and SHAP Analysis Identifying Silent Risk Counties and Treatment Deserts
Imperial College London（伦敦帝国理工学院）；University of Surrey（萨里大学）；Surrey and Borders NHS Foundation Trust（萨里和边境国民健康服务基金会信托）；The UK Dementia Research Institute (UK DRI)（英国痴呆症研究所）
Machine learning techniques combined with in-home monitoring technologies provide a unique opportunity to automate diagnosis and early detection of adverse health conditions in long-term conditions such as dementia. However, accessing sufficient labelled training samples and integrating high-quality, routinely collected data from heterogeneous in-home monitoring technologies are main obstacles hindered utilising these technologies in real-world medicine. This work presents a semi-supervised model that can continuously learn from routinely collected in-home observation and measurement data. We …
Given the widespread dissemination of inaccurate medical advice related to the 2019 coronavirus pandemic (COVID-19), such as fake remedies, treatments and prevention suggestions, misinformation detection has emerged as an open problem of high importance and interest for the research community. Several works study health misinformation detection, yet little attention has been given to the perceived severity of misinformation posts. In this work, we frame health misinformation as a risk assessment task. More specifically, we study the severity of each misinformation story and how readers perceiv…
Augmented Risk Prediction for the Onset of Alzheimer's Disease from Electronic Health Records with Large Language Models
Michigan State University（密歇根州立大学）；Weill Cornell Medicine, Cornell University（康奈尔大学威尔医学院）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）
Contrastive learning has demonstrated promising performance in image and text domains either in a self-supervised or a supervised manner. In this work, we extend the supervised contrastive learning framework to clinical risk prediction problems based on longitudinal electronic health records (EHR). We propose a general supervised contrastive loss $\mathcal{L}_{\text{Contrastive Cross Entropy} } + λ\mathcal{L}_{\text{Supervised Contrastive Regularizer}}$ for learning both binary classification (e.g. in-hospital mortality prediction) and multi-label classification (e.g. phenotyping) in a unified…
Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
Georgia Institute of Technology（佐治亚理工学院）；McGill University（麦吉尔大学）；Carnegie Mellon University（卡内基梅隆大学）；Oregon State University（俄勒冈州立大学）；University of Pennsylvania（宾夕法尼亚大学）；Northeastern University（东北大学）；University of California, San Diego（加州大学圣地亚哥分校）
Mutual Information Assisted Ensemble Recommender System for Identifying Critical Risk Factors in Healthcare Prognosis
Bethune College, University of Calcutta（加尔各答大学贝休恩学院）；Jadavpur University（jadavpur大学）；Mahindra University（马恒达大学）；Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；King Mongkut’s University of Technology Thonburi（泰国国王科技大学吞武里校区）
