Proceedings of the ACM Conference on Health, Inference, and Learning最新文献_第5页

Extracting medical entities from social media 从社交媒体中提取医疗实体

Proceedings of the ACM Conference on Health, Inference, and Learning Pub Date : 2020-04-02 DOI: 10.1145/3368555.3384467

S. Šćepanović, E. Martin-Lopez, D. Quercia, Khan Baykaner

{"title":"Extracting medical entities from social media","authors":"S. Šćepanović, E. Martin-Lopez, D. Quercia, Khan Baykaner","doi":"10.1145/3368555.3384467","DOIUrl":"https://doi.org/10.1145/3368555.3384467","url":null,"abstract":"Accurately extracting medical entities from social media is challenging because people use informal language with different expressions for the same concept, and they also make spelling mistakes. Previous work either focused on specific diseases (e.g., depression) or drugs (e.g., opioids) or, if working with a wide-set of medical entities, only tackled individual and small-scale benchmark datasets (e.g., AskaPatient). In this work, we first demonstrated how to accurately extract a wide variety of medical entities such as symptoms, diseases, and drug names on three benchmark datasets from varied social media sources, and then also validated this approach on a large-scale Reddit dataset. We first implemented a deep-learning method using contextual embeddings that upon two existing benchmark datasets, one containing annotated AskaPatient posts (CADEC) and the other containing annotated tweets (Micromed), outperformed existing state-of-the-art methods. Second, we created an additional benchmark dataset by annotating medical entities in 2K Reddit posts (made publicly available under the name of MedRed) and showed that our method also performs well on this new dataset. Finally, to demonstrate that our method accurately extracts a wide variety of medical entities on a large scale, we applied the model pre-trained on MedRed to half a million Reddit posts. The posts came from disease-specific subreddits so we could categorise them into 18 diseases based on the subreddit. We then trained a machine-learning classifier to predict the post's category solely from the extracted medical entities. The average F1 score across categories was .87. These results open up new cost-effective opportunities for modeling, tracking and even predicting health behavior at scale.","PeriodicalId":87342,"journal":{"name":"Proceedings of the ACM Conference on Health, Inference, and Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78562336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment 结合机器和人类智能进行个性化康复评估的交互式混合方法

Proceedings of the ACM Conference on Health, Inference, and Learning Pub Date : 2020-04-02 DOI: 10.1145/3368555.3384452

Min Hun Lee, D. Siewiorek, A. Smailagic, A. Bernardino, S. Badia

{"title":"Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment","authors":"Min Hun Lee, D. Siewiorek, A. Smailagic, A. Bernardino, S. Badia","doi":"10.1145/3368555.3384452","DOIUrl":"https://doi.org/10.1145/3368555.3384452","url":null,"abstract":"Automated assessment of rehabilitation exercises using machine learning has a potential to improve current rehabilitation practices. However, it is challenging to completely replicate therapist's decision making on the assessment of patients with various physical conditions. This paper describes an interactive machine learning approach that iteratively integrates a data-driven model with expert's knowledge to assess the quality of rehabilitation exercises. Among a large set of kinematic features of the exercise motions, our approach identifies the most salient features for assessment using reinforcement learning and generates a user-specific analysis to elicit feature relevance from a therapist for personalized rehabilitation assessment. While accommodating therapist's feedback on feature relevance, our approach can tune a generic assessment model into a personalized model. Specifically, our approach improves performance to predict assessment from 0.8279 to 0.9116 average F1-scores of three upper-limb rehabilitation exercises (p < 0.01). Our work demonstrates that machine learning models with feature selection can generate kinematic feature-based analysis as explanations on predictions of a model to elicit expert's knowledge of assessment, and how machine learning models can augment with expert's knowledge for personalized rehabilitation assessment.","PeriodicalId":87342,"journal":{"name":"Proceedings of the ACM Conference on Health, Inference, and Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82401429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records. TASTE：用于电子健康记录表型的时态和静态张量因子化。

Proceedings of the ACM Conference on Health, Inference, and Learning Pub Date : 2020-04-01 DOI: 10.1145/3368555.3384464

Ardavan Afshar, Ioakeim Perros, Haesun Park, Christopher deFilippi, Xiaowei Yan, Walter Stewart, Joyce Ho, Jimeng Sun

{"title":"TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records.","authors":"Ardavan Afshar, Ioakeim Perros, Haesun Park, Christopher deFilippi, Xiaowei Yan, Walter Stewart, Joyce Ho, Jimeng Sun","doi":"10.1145/3368555.3384464","DOIUrl":"10.1145/3368555.3384464","url":null,"abstract":"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 Temporal And Static TEnsor factorization (TASTE) that jointly models both static and temporal information to extract phenotypes. TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor. To fit the proposed model, we transform the original problem into simpler ones which are optimally solved in an alternating fashion. For each of the sub-problems, our proposed mathematical re-formulations lead to efficient sub-problem solvers. Comprehensive experiments on large EHR data from a heart failure (HF) study confirmed that TASTE is up to 14× faster than several baselines and the resulting phenotypes were confirmed to be clinically meaningful by a cardiologist. Using 60 phenotypes extracted by TASTE, a simple logistic regression can achieve the same level of area under the curve (AUC) for HF prediction compared to a deep learning model using recurrent neural networks (RNN) with 345 features.","PeriodicalId":87342,"journal":{"name":"Proceedings of the ACM Conference on Health, Inference, and Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924914/pdf/nihms-1587674.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25428959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CaliForest: Calibrated Random Forest for Health Data. califforest:健康数据校准随机森林。

Proceedings of the ACM Conference on Health, Inference, and Learning Pub Date : 2020-04-01 Epub Date: 2020-04-02 DOI: 10.1145/3368555.3384461

Yubin Park, Joyce C Ho

{"title":"CaliForest: Calibrated Random Forest for Health Data.","authors":"Yubin Park, Joyce C Ho","doi":"10.1145/3368555.3384461","DOIUrl":"https://doi.org/10.1145/3368555.3384461","url":null,"abstract":"Real-world predictive models in healthcare should be evaluated in terms of discrimination, the ability to differentiate between high and low risk events, and calibration, or the accuracy of the risk estimates. Unfortunately, calibration is often neglected and only discrimination is analyzed. Calibration is crucial for personalized medicine as they play an increasing role in the decision making process. Since random forest is a popular model for many healthcare applications, we propose CaliForest, a new calibrated random forest. Unlike existing calibration methodologies, CaliForest utilizes the out-of-bag samples to avoid the explicit construction of a calibration set. We evaluated CaliForest on two risk prediction tasks obtained from the publicly-available MIMIC-III database. Evaluation on these binary prediction tasks demonstrates that CaliForest can achieve the same discriminative power as random forest while obtaining a better-calibrated model evaluated across six different metrics. CaliForest is published on the standard Python software repository and the code is openly available on Github.","PeriodicalId":87342,"journal":{"name":"Proceedings of the ACM Conference on Health, Inference, and Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3368555.3384461","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39226442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Multiple Instance Learning for Predicting Necrotizing Enterocolitis in Premature Infants Using Microbiome Data. 利用微生物组数据预测早产儿坏死性小肠结肠炎的多实例学习。

Proceedings of the ACM Conference on Health, Inference, and Learning Pub Date : 2020-04-01 Epub Date: 2020-04-02 DOI: 10.1145/3368555.3384466

Thomas A Hooven, Adam Yun Chao Lin, Ansaf Salleb-Aouissi

{"title":"Multiple Instance Learning for Predicting Necrotizing Enterocolitis in Premature Infants Using Microbiome Data.","authors":"Thomas A Hooven, Adam Yun Chao Lin, Ansaf Salleb-Aouissi","doi":"10.1145/3368555.3384466","DOIUrl":"10.1145/3368555.3384466","url":null,"abstract":"Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease that primarily affects preterm infants during their first weeks after birth. Mortality rates associated with NEC are 15-30%, and surviving infants are susceptible to multiple serious, long-term complications. The disease is sporadic and, with currently available tools, unpredictable. We are creating an early warning system that uses stool microbiome features, combined with clinical and demographic information, to identify infants at high risk of developing NEC. Our approach uses a multiple instance learning, neural network-based system that could be used to generate daily or weekly NEC predictions for premature infants. The approach was selected to effectively utilize sparse and weakly annotated datasets characteristic of stool microbiome analysis. Here we describe initial validation of our system, using clinical and microbiome data from a nested case-control study of 161 preterm infants. We show receiver-operator curve areas above 0.9, with 75% of dominant predictive samples for NEC-affected infants identified at least 24 hours prior to disease onset. Our results pave the way for development of a real-time early warning system for NEC using a limited set of basic clinical and demographic details combined with stool microbiome data.","PeriodicalId":87342,"journal":{"name":"Proceedings of the ACM Conference on Health, Inference, and Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3368555.3384466","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39229696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks. 基于深度神经网络的电子健康记录药物不良反应发现。

Proceedings of the ACM Conference on Health, Inference, and Learning Pub Date : 2020-04-01 DOI: 10.1145/3368555.3384459

Wei Zhang, Peggy Peissig, Zhaobin Kuang, David Page

引用次数: 7

MMiDaS-AE: Multi-modal Missing Data aware Stacked Autoencoder for Biomedical Abstract Screening. MMiDaS-AE:用于生物医学摘要筛选的多模态缺失数据感知堆叠自编码器。

Proceedings of the ACM Conference on Health, Inference, and Learning Pub Date : 2020-04-01 Epub Date: 2020-04-02 DOI: 10.1145/3368555.3384463

Eric W Lee, Byron C Wallace, Karla I Galaviz, Joyce C Ho

{"title":"MMiDaS-AE: Multi-modal Missing Data aware Stacked Autoencoder for Biomedical Abstract Screening.","authors":"Eric W Lee, Byron C Wallace, Karla I Galaviz, Joyce C Ho","doi":"10.1145/3368555.3384463","DOIUrl":"10.1145/3368555.3384463","url":null,"abstract":"Systematic review (SR) is an essential process to identify, evaluate, and summarize the findings of all relevant individual studies concerning health-related questions. However, conducting a SR is labor-intensive, as identifying relevant studies is a daunting process that entails multiple researchers screening thousands of articles for relevance. In this paper, we propose MMiDaS-AE, a Multi-modal Missing Data aware Stacked Autoencoder, for semi-automating screening for SRs. We use a multi-modal view that exploits three representations, of: 1) documents, 2) topics, and 3) citation networks. Documents that contain similar words will be nearby in the document embedding space. Models can also exploit the relationship between documents and the associated SR MeSH terms to capture article relevancy. Finally, related works will likely share the same citations, and thus closely related articles would, intuitively, be trained to be close to each other in the embedding space. However, using all three learned representations as features directly result in an unwieldy number of parameters. Thus, motivated by recent work on multi-modal auto-encoders, we adopt a multi-modal stacked autoencoder that can learn a shared representation encoding all three representations in a compressed space. However, in practice one or more of these modalities may be missing for an article (e.g., if we cannot recover citation information). Therefore, we propose to learn to impute the shared representation even when specific inputs are missing. We find this new model significantly improves performance on a dataset consisting of 15 SRs compared to existing approaches.","PeriodicalId":87342,"journal":{"name":"Proceedings of the ACM Conference on Health, Inference, and Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3368555.3384463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39226443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Variational Learning of Individual Survival Distributions. 个体生存分布的变分学习。

Proceedings of the ACM Conference on Health, Inference, and Learning Pub Date : 2020-04-01 Epub Date: 2020-04-02 DOI: 10.1145/3368555.3384454

Zidi Xiu, Chenyang Tao, Ricardo Henao

引用次数: 10

Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging. 隐藏分层导致医学成像机器学习中有临床意义的失败。

Proceedings of the ACM Conference on Health, Inference, and Learning Pub Date : 2020-04-01 DOI: 10.1145/3368555.3384468

Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, Christopher Ré

{"title":"Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging.","authors":"Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, Christopher Ré","doi":"10.1145/3368555.3384468","DOIUrl":"https://doi.org/10.1145/3368555.3384468","url":null,"abstract":"Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing. For example, overall performance of a cancer detection model may be high, but the model may still consistently miss a rare but aggressive cancer subtype. We refer to this problem as hidden stratification, and observe that it results from incompletely describing the meaningful variation in a dataset. While hidden stratification can substantially reduce the clinical efficacy of machine learning models, its effects remain difficult to measure. In this work, we assess the utility of several possible techniques for measuring hidden stratification effects, and characterize these effects both via synthetic experiments on the CIFAR-100 benchmark dataset and on multiple real-world medical imaging datasets. Using these measurement techniques, we find evidence that hidden stratification can occur in unidentified imaging subsets with low prevalence, low label quality, subtle distinguishing features, or spurious correlates, and that it can result in relative performance differences of over 20% on clinically important subsets. Finally, we discuss the clinical implications of our findings, and suggest that evaluation of hidden stratification should be a critical component of any machine learning deployment in medical imaging.","PeriodicalId":87342,"journal":{"name":"Proceedings of the ACM Conference on Health, Inference, and Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3368555.3384468","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38608685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 268

Deidentification of free-text medical records using pre-trained bidirectional transformers. 使用预训练的双向变换器消除自由文本医疗记录的身份识别。

Proceedings of the ACM Conference on Health, Inference, and Learning Pub Date : 2020-04-01 Epub Date: 2020-04-02 DOI: 10.1145/3368555.3384455

Alistair E W Johnson, Lucas Bulgarelli, Tom J Pollard

{"title":"Deidentification of free-text medical records using pre-trained bidirectional transformers.","authors":"Alistair E W Johnson, Lucas Bulgarelli, Tom J Pollard","doi":"10.1145/3368555.3384455","DOIUrl":"10.1145/3368555.3384455","url":null,"abstract":"The ability of caregivers and investigators to share patient data is fundamental to many areas of clinical practice and biomedical research. Prior to sharing, it is often necessary to remove identifiers such as names, contact details, and dates in order to protect patient privacy. Deidentification, the process of removing identifiers, is challenging, however. High-quality annotated data for developing models is scarce; many target identifiers are highly heterogenous (for example, there are uncountable variations of patient names); and in practice anything less than perfect sensitivity may be considered a failure. As a result, patient data is often withheld when sharing would be beneficial, and identifiable patient data is often divulged when a deidentified version would suffice. In recent years, advances in machine learning methods have led to rapid performance improvements in natural language processing tasks, in particular with the advent of large-scale pretrained language models. In this paper we develop and evaluate an approach for deidentification of clinical notes based on a bidirectional transformer model. We propose human interpretable evaluation measures and demonstrate state of the art performance against modern baseline models. Finally, we highlight current challenges in deidentification, including the absence of clear annotation guidelines, lack of portability of models, and paucity of training data. Code to develop our model is open source, allowing for broad reuse.","PeriodicalId":87342,"journal":{"name":"Proceedings of the ACM Conference on Health, Inference, and Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e7/a6/nihms-1679580.PMC8330601.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39277422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0