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1.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Artigo em Inglês | MEDLINE | ID: mdl-34903654

RESUMO

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.


Assuntos
COVID-19/epidemiologia , Bases de Dados Factuais , Indicadores Básicos de Saúde , Assistência Ambulatorial/tendências , Métodos Epidemiológicos , Humanos , Internet/estatística & dados numéricos , Distanciamento Físico , Inquéritos e Questionários , Viagem , Estados Unidos/epidemiologia
2.
Proc Conf ; 2021: 106-115, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-34151319

RESUMO

Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence can be found at https://textessence.github.io.

3.
IEEE Trans Vis Comput Graph ; 26(1): 863-873, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31502978

RESUMO

The performance of deep learning models is dependent on the precise configuration of many layers and parameters. However, there are currently few systematic guidelines for how to configure a successful model. This means model builders often have to experiment with different configurations by manually programming different architectures (which is tedious and time consuming) or rely on purely automated approaches to generate and train the architectures (which is expensive). In this paper, we present Rapid Exploration of Model Architectures and Parameters, or REMAP, a visual analytics tool that allows a model builder to discover a deep learning model quickly via exploration and rapid experimentation of neural network architectures. In REMAP, the user explores the large and complex parameter space for neural network architectures using a combination of global inspection and local experimentation. Through a visual overview of a set of models, the user identifies interesting clusters of architectures. Based on their findings, the user can run ablation and variation experiments to identify the effects of adding, removing, or replacing layers in a given architecture and generate new models accordingly. They can also handcraft new models using a simple graphical interface. As a result, a model builder can build deep learning models quickly, efficiently, and without manual programming. We inform the design of REMAP through a design study with four deep learning model builders. Through a use case, we demonstrate that REMAP allows users to discover performant neural network architectures efficiently using visual exploration and user-defined semi-automated searches through the model space.

4.
IEEE Comput Graph Appl ; 39(5): 18-19, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31442962
5.
Epidemics ; 27: 59-65, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-30902616

RESUMO

The recent Zika virus (ZIKV) epidemic in the Americas ranks among the largest outbreaks in modern times. Like other mosquito-borne flaviviruses, ZIKV circulates in sylvatic cycles among primates that can serve as reservoirs of spillover infection to humans. Identifying sylvatic reservoirs is critical to mitigating spillover risk, but relevant surveillance and biological data remain limited for this and most other zoonoses. We confronted this data sparsity by combining a machine learning method, Bayesian multi-label learning, with a multiple imputation method on primate traits. The resulting models distinguished flavivirus-positive primates with 82% accuracy and suggest that species posing the greatest spillover risk are also among the best adapted to human habitations. Given pervasive data sparsity describing animal hosts, and the virtual guarantee of data sparsity in scenarios involving novel or emerging zoonoses, we show that computational methods can be useful in extracting actionable inference from available data to support improved epidemiological response and prevention.


Assuntos
Primatas/virologia , Infecção por Zika virus/epidemiologia , Zika virus/patogenicidade , Zoonoses/epidemiologia , Zoonoses/virologia , Animais , Teorema de Bayes , Humanos , Risco , Infecção por Zika virus/patologia , Zoonoses/patologia
6.
Artigo em Inglês | MEDLINE | ID: mdl-30334796

RESUMO

Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and "what if"-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.

7.
IEEE Trans Vis Comput Graph ; 24(1): 142-151, 2018 01.
Artigo em Inglês | MEDLINE | ID: mdl-28866567

RESUMO

Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

8.
IEEE Trans Vis Comput Graph ; 23(6): 1636-1649, 2017 06.
Artigo em Inglês | MEDLINE | ID: mdl-28113471

RESUMO

The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.

9.
IEEE Trans Vis Comput Graph ; 22(1): 91-100, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26529690

RESUMO

Many researchers across diverse disciplines aim to analyze the behavior of cohorts whose behaviors are recorded in large event databases. However, extracting cohorts from databases is a difficult yet important step, often overlooked in many analytical solutions. This is especially true when researchers wish to restrict their cohorts to exhibit a particular temporal pattern of interest. In order to fill this gap, we designed COQUITO, a visual interface that assists users defining cohorts with temporal constraints. COQUITO was designed to be comprehensible to domain experts with no preknowledge of database queries and also to encourage exploration. We then demonstrate the utility of COQUITO via two case studies, involving medical and social media researchers.

10.
J Biomed Inform ; 56: 369-78, 2015 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-26146159

RESUMO

OBJECTIVE: In order to derive data-driven insights, we develop Care Pathway Explorer, a system that mines and visualizes a set of frequent event sequences from patient EMR data. The goal is to utilize historical EMR data to extract common sequences of medical events such as diagnoses and treatments, and investigate how these sequences correlate with patient outcome. MATERIALS AND METHODS: The Care Pathway Explorer uses a frequent sequence mining algorithm adapted to handle the real-world properties of EMR data, including techniques for handling event concurrency, multiple levels-of-detail, temporal context, and outcome. The mined patterns are then visualized in an interactive user interface consisting of novel overview and flow visualizations. RESULTS: We use the proposed system to analyze the diagnoses and treatments of a cohort of hyperlipidemic patients with hypertension and diabetes pre-conditions, and demonstrate the clinical relevance of patterns mined from EMR data. The patterns that were identified corresponded to clinical and published knowledge, some of it unknown to the physician at the time of discovery. CONCLUSION: Care Pathway Explorer, which combines frequent sequence mining techniques with advanced visualizations supports the integration of data-driven insights into care pathway discovery.


Assuntos
Mineração de Dados/métodos , Registros Eletrônicos de Saúde , Hiperlipidemias/diagnóstico , Algoritmos , Estudos de Coortes , Gráficos por Computador , Coleta de Dados , Humanos , Hiperlipidemias/complicações , Hiperlipidemias/tratamento farmacológico , Hipertensão/diagnóstico , Hipertensão/tratamento farmacológico , Lipoproteínas LDL/análise , Avaliação de Resultados da Assistência ao Paciente , Estado Pré-Diabético/diagnóstico , Estado Pré-Diabético/tratamento farmacológico , Software
11.
Stud Health Technol Inform ; 210: 70-4, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-25991104

RESUMO

Care pathways play significant roles in delivering evidence-based and coordinated care to patients with specific conditions. In order to put care pathways into practice, clinical institutions always need to adapt them based on local care settings so that the best local practices can be incorporated and used to develop refined pathways. However, it is knowledge-intensive and error-prone to incorporate various analytic insights from local data sets. In order to assist care pathway developers in working effectively and efficiently, we propose to automatically synthesize the analytical evidences derived from multiple analysis methods, and recommend modelling operations accordingly to derive a refined care pathway for a specific patient cohort. We validated our method by adapting a Congestive Heart Failure (CHF) Ambulatory Care Pathway for patients with additional condition of COPD through synthesizing the results of variation analysis and frequent pattern mining against patient records.


Assuntos
Mineração de Dados/métodos , Sistemas de Apoio a Decisões Clínicas/organização & administração , Registros Eletrônicos de Saúde/organização & administração , Medicina Baseada em Evidências , Aprendizado de Máquina , Procedimentos Clínicos
12.
Stud Health Technol Inform ; 205: 23-7, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25160138

RESUMO

Care pathways (CPs) as a means of healthcare quality control are getting increasing attention due to widespread recognition in the healthcare industry of the need for well coordinated, evidence based and personalized care. To keep the promise, CPs require continuous refinement in order to stay up to date with regard to both clinical guidelines and data-driven insights from real world practices. There is therefore a strong demand for a unified platform that allows harmonization of evidence coming from multiple sources. In this paper we describe Care Pathway Workbench, a web-based platform that enables users to build and continuously improve Case Management Model and Notation based CPs by harmonizing evidences from guidelines and patient data. To illustrate the functionalities, we describe how a CHF (Congestive Heart Failure) Ambulatory Care Pathway can be developed using this workbench by first extracting key elements from widely accepted guidelines for CHF management, then incorporating evidence mined from clinical practice data, and finally transforming and exporting the resulting CP model to a care management product.


Assuntos
Assistência Ambulatorial/normas , Administração de Caso/normas , Procedimentos Clínicos/normas , Sistemas de Apoio a Decisões Clínicas/normas , Insuficiência Cardíaca/diagnóstico , Insuficiência Cardíaca/terapia , Software , China , Técnicas de Apoio para a Decisão , Medicina Baseada em Evidências , Humanos
13.
J Biomed Inform ; 48: 148-59, 2014 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-24486355

RESUMO

Patients' medical conditions often evolve in complex and seemingly unpredictable ways. Even within a relatively narrow and well-defined episode of care, variations between patients in both their progression and eventual outcome can be dramatic. Understanding the patterns of events observed within a population that most correlate with differences in outcome is therefore an important task in many types of studies using retrospective electronic health data. In this paper, we present a method for interactive pattern mining and analysis that supports ad hoc visual exploration of patterns mined from retrospective clinical patient data. Our approach combines (1) visual query capabilities to interactively specify episode definitions, (2) pattern mining techniques to help discover important intermediate events within an episode, and (3) interactive visualization techniques that help uncover event patterns that most impact outcome and how those associations change over time. In addition to presenting our methodology, we describe a prototype implementation and present use cases highlighting the types of insights or hypotheses that our approach can help uncover.


Assuntos
Mineração de Dados/métodos , Informática Médica/métodos , Idoso , Algoritmos , Sistemas Computacionais , Progressão da Doença , Registros Eletrônicos de Saúde , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Estudos Retrospectivos , Software , Fatores de Tempo , Resultado do Tratamento
14.
J Am Med Inform Assoc ; 21(2): 337-44, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-24045907

RESUMO

OBJECTIVE: Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control. METHOD: In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier. RESULTS: The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780). CONCLUSIONS: This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans.


Assuntos
Gerenciamento Clínico , Registros Eletrônicos de Saúde , Hipertensão/terapia , Anti-Hipertensivos/uso terapêutico , Doença Crônica , Humanos , Modelos Teóricos , Prognóstico
15.
IEEE Trans Vis Comput Graph ; 20(12): 1614-23, 2014 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-26356875

RESUMO

Predictive modeling techniques are increasingly being used by data scientists to understand the probability of predicted outcomes. However, for data that is high-dimensional, a critical step in predictive modeling is determining which features should be included in the models. Feature selection algorithms are often used to remove non-informative features from models. However, there are many different classes of feature selection algorithms. Deciding which one to use is problematic as the algorithmic output is often not amenable to user interpretation. This limits the ability for users to utilize their domain expertise during the modeling process. To improve on this limitation, we developed INFUSE, a novel visual analytics system designed to help analysts understand how predictive features are being ranked across feature selection algorithms, cross-validation folds, and classifiers. We demonstrate how our system can lead to important insights in a case study involving clinical researchers predicting patient outcomes from electronic medical records.


Assuntos
Gráficos por Computador , Modelos Teóricos , Software , Algoritmos , Humanos , Interface Usuário-Computador
16.
IEEE Trans Vis Comput Graph ; 20(12): 1653-62, 2014 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-26356879

RESUMO

As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.


Assuntos
Gráficos por Computador , Modelos Teóricos , Interface Usuário-Computador , Algoritmos , Registros Eletrônicos de Saúde , Humanos , Aprendizado de Máquina
17.
IEEE Trans Vis Comput Graph ; 19(7): 1095-108, 2013 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-23661007

RESUMO

As people continue to author and share increasing amounts of information in social media, the opportunity to leverage such information for relationship discovery tasks increases. In this paper, we describe a set of systems that mine, aggregate, and infer a social graph from social media inside an enterprise, resulting in over 73 million relationships between 450,000 people. We then describe SaNDVis, a novel visual analytics tool that supports people-centric tasks like expertise location, team building, and team coordination in the enterprise. We provide details of a 22-month-long, large-scale deployment to over 2,300 users from which we analyze longitudinal usage patterns, classify types of visual analytics queries and users, and extract dominant use cases from log and interview data. By integrating social position, evidence, and facets into SaNDVis, we demonstrate how users can use a visual analytics tool to reflect on existing relationships as well as build new relationships in an enterprise setting.

18.
AMIA Annu Symp Proc ; 2012: 716-25, 2012.
Artigo em Inglês | MEDLINE | ID: mdl-23304345

RESUMO

OBJECTIVE: To develop a visual analytic system to help medical professionals improve disease diagnosis by providing insights for understanding disease progression. METHODS: We develop MatrixFlow, a visual analytic system that takes clinical event sequences of patients as input, constructs time-evolving networks and visualizes them as a temporal flow of matrices. MatrixFlow provides several interactive features for analysis: 1) one can sort the events based on the similarity in order to accentuate underlying cluster patterns among those events; 2) one can compare co-occurrence events over time and across cohorts through additional line graph visualization. RESULTS: MatrixFlow is applied to visualize heart failure (HF) symptom events extracted from a large cohort of HF cases and controls (n=50,625), which allows medical experts to reach insights involving temporal patterns and clusters of interest, and compare cohorts in novel ways that may lead to improved disease diagnoses. CONCLUSIONS: MatrixFlow is an interactive visual analytic system that allows users to quickly discover patterns in clinical event sequences. By unearthing the patterns hidden within and displaying them to medical experts, users become empowered to make decisions influenced by historical patterns.


Assuntos
Gráficos por Computador , Tomada de Decisões Assistida por Computador , Progressão da Doença , Insuficiência Cardíaca/diagnóstico , Estudos de Casos e Controles , Feminino , Humanos , Masculino
19.
IEEE Trans Vis Comput Graph ; 15(6): 953-60, 2009.
Artigo em Inglês | MEDLINE | ID: mdl-19834159

RESUMO

A common goal in graph visualization research is the design of novel techniques for displaying an overview of an entire graph. However, there are many situations where such an overview is not relevant or practical for users, as analyzing the global structure may not be related to the main task of the users that have semi-specific information needs. Furthermore, users accessing large graph databases through an online connection or users running on less powerful (mobile) hardware simply do not have the resources needed to compute these overviews. In this paper, we advocate an interaction model that allows users to remotely browse the immediate context graph around a specific node of interest. We show how Furnas' original degree of interest function can be adapted from trees to graphs and how we can use this metric to extract useful contextual subgraphs, control the complexity of the generated visualization and direct users to interesting datapoints in the context. We demonstrate the effectiveness of our approach with an exploration of a dense online database containing over 3 million legal citations.

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