The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
Stanford Center for Biomedical Informatics Research, Stanford University（斯坦福大学生物医学信息学研究中心）；USC Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）
Daisy Yi Ding, Chloé Simpson, Stephen Pfohl, Dave C. Kale, Kenneth Jung, Nigam H. Shah
Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is foundational in clinical informatics. Increasingly, electronic phenotyping is performed via supervised learning. We investigate the effectiveness of multitask learning for phenotyping using electronic health records (EHR) data. Multitask learning aims to improve model performance on a target task by jointly learning additional auxiliary tasks and has been used in disparate areas of machine learning. However, its utility when applied to EHR data has not been established, and prior work suggests that its benefits are inconsistent. We present experiments that elucidate when multitask learning with neural nets improves performance for phenotyping using EHR data relative to neural nets trained for a single phenotype and to well-tuned logistic regression baselines. We find that multitask neural nets consistently outperform single-task neural nets for rare phenotypes but underperform for relatively more common phenotypes. The effect size increases as more auxiliary tasks are added. Moreover, multitask learning reduces the sensitivity of neural nets to hyperparameter settings for rare phenotypes. Last, we quantify phenotype complexity and find that neural nets trained with or without multitask learning do not improve on simple baselines unless the phenotypes are sufficiently complex.
Georgia Institute of Technology（佐治亚理工学院）；Health at Scale；INOVA；Sutter Health（萨特医疗）；Emory University（埃默里大学）
Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective tool for phenotyping. Most of the existing works assume either a static patient representation with aggregate data or only model temporal data. However, real EHR data contain both temporal (e.g., longitudinal clinical visits) and static information (e.g., patient demographics), which are difficult to model simultaneously. In this paper, we propose Te…
Using binary silver labels in electronic health records-based computable phenotyping algorithms
Department of Biostatistics, University of Washington（华盛顿大学生物统计学系）；Kaiser Permanente Center for Health Research（凯撒医疗研究中心）；Biostatistics Division, Kaiser Permanente Washington Health Research Institute（凯撒医疗华盛顿健康研究机构生物统计学部）；Kaiser Permanente Washington Health Research Institute（凯撒医疗华盛顿健康研究机构）；Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center（弗雷德 Hutchinson 癌症中心疫苗与传染病部）
With large volumes of health care data comes the research area of computational phenotyping, making use of techniques such as machine learning to describe illnesses and other clinical concepts from the data itself. The "traditional" approach of using supervised learning relies on a domain expert, and has two main limitations: requiring skilled humans to supply correct labels limits its scalability and accuracy, and relying on existing clinical descriptions limits the sorts of patterns that can be found. For instance, it may fail to acknowledge that a disease treated as a single condition may r…
Pohang University of Science and Technology（浦项科技大学）；Georgia Institute of Technology（佐治亚理工学院）；University of California, San Diego（加利福尼亚大学圣迭戈分校）
Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data…
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）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Cambridge（剑桥大学）；Alan Turing Institute（艾伦·图灵研究所）
Due to the wider availability of modern electronic health records, patient care data is often being stored in the form of time-series. Clustering such time-series data is crucial for patient phenotyping, anticipating patients' prognoses by identifying "similar" patients, and designing treatment guidelines that are tailored to homogeneous patient subgroups. In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e.g., adverse events, the onset of comorbidities). To encourage each clu…
SWoTTeD: An Extension of Tensor Decomposition to Temporal Phenotyping
AIstroSight；Inria（法国国家信息与自动化研究所）；Hospices Civils de Lyon（里昂公立医院）；Université Claude Bernard Lyon 1（里昂第一大学）；CEPIA Team（CEPIA团队）；Paris University Hospital（巴黎大学医院）
USC Information Sciences Institute（南加州大学信息科学研究所）；YerevaNN（耶烈万神经网络实验室）；Yerevan State University（埃里温国立大学）
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems in…
University of Texas, Austin（得克萨斯大学奥斯汀分校）；Vanderbilt University（范德堡大学）；Emory University（埃默里大学）
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational phenotyping. However, such approaches typically do not leverage available domain knowledge while extracting the phenotypes; hence, some of the suggested phenotypes may not map well to clinical concepts or may be very similar to other suggested phenotypes. To address these issues, we present a novel, automatic approach called PIVETed-Granite that mines existing biomedical literature (PubMed) to obtain cannot-link constrain…
Columbia University（哥伦比亚大学）；Columbia University Medical Center（哥伦比亚大学医学中心）
Identifying a patient's key problems over time is a common task for providers at the point care, yet a complex and time-consuming activity given current electric health records. To enable a problem-oriented summarizer to identify a patient's comprehensive list of problems and their salience, we propose an unsupervised phenotyping approach that jointly learns a large number of phenotypes/problems across structured and unstructured data. To identify the appropriate granularity of the learned phenotypes, the model is trained on a target patient population of the same clinic. To enable the content…
The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets. Special attention is given to preprocessing of in-put data and the different modeling approaches. The literature review confirms natural language processing to be a promising approach for electronic phenotyping. However, accessibility and lack of natural language process standards for medical texts remain a challenge. Future research should develop such standar…
Texas A&M University（德克萨斯农工大学）；Yale School of Medicine（耶鲁医学院）
Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The triage process is critical in the appropriate allocation of resources and in anticipating eventual patient disposition, typically admission to the hospital or discharge home. EHR data are high-dimensional and complex, but offer the opportunity to discover and characterize underlying data-driven patient phenotypes. These phenotypes will enable improved, personalized therapeutic…
A Weakly Supervised Transformer for Rare Disease Diagnosis and Subphenotyping from EHRs with Pulmonary Case Studies
Harvard T.H. Chan School of Public Health（哈佛T.H. 洪学校公共卫生学院）；Harvard Medical School（哈佛医学院）；Bentley University（伯顿大学）；Columbia University（哥伦比亚大学）；Boston Children’s Hospital（波士顿儿童医院）；Computational Health Informatics Program（计算健康信息学计划）；Department of Pediatrics（儿科部门）；Brigham and Women’s Hospital（布里奇沃特医院）
University of Oxford（牛津大学）；The George Institute for Global Health (UK)（（英国）乔治全球健康研究院）；Oxford University Hospitals NHS Foundation Trust（牛津大学医院国民保健服务基金会信托）；University of New South Wales（新南威尔士大学）
Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population, both in absolute and relative terms. However, multimorbidity remains poorly understood, and the evidence from existing research to describe its burden, determinants and consequences has been limited. Previous studies attempting to understand multimorbidity patterns are often cross-sectional and do not explicitly account for multimorbidity patterns' evolution over time; some of them are based on small datasets and/or use arbitrary and narrow age ranges; and those that emp…
Objective: To evaluate unsupervised clustering methods for identifying individual-level behavioral-clinical phenotypes that relate personal biomarkers and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials and Methods: We used hierarchical clustering (HC) to identify groups of meals with similar nutrition and glycemic impact for 6 individuals with T2DM who collected self-monitoring data. We evaluated clusters on: 1) correspondence to gold standards generated by certified diabetes educators (CDEs) for 3 participants; 2) face validity, rated by CDEs, and 3) impact on CDE…
Emory University（埃默里大学）；UT Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meaningful clinical concepts. While distributing the tensor factorization tasks to local sites can avoid direct data sharing, it still requires the exchange of intermediary results which could reveal sensitive patient information. Therefore, the challenge is how to jointly decompose the tensor under rigorous and principled privacy constraints, while still support the model's interpretability. We propose DPFact, a privacy-…
University of Oxford（牛津大学）；Oxford Martin School（牛津马丁学院）
Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning (more specifically, deep learning) provides a great opportunity to address this unmet need. In this study, we introduce BEHRT: A deep neural sequence transduction …
IBM Research（IBM研究院）；Ain Shams University（艾因夏姆斯大学）
Despite the large number of patients in Electronic Health Records (EHRs), the subset of usable data for modeling outcomes of specific phenotypes are often imbalanced and of modest size. This can be attributed to the uneven coverage of medical concepts in EHRs. In this paper, we propose OMTL, an Ontology-driven Multi-Task Learning framework, that is designed to overcome such data limitations. The key contribution of our work is the effective use of knowledge from a predefined well-established medical relationship graph (ontology) to construct a novel deep learning network architecture that mirr…
