Objective
To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30‐day ...readmissions.
Study Setting
A multihospital academic health system in southeastern Massachusetts.
Study Design
An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics.
Data Collection/Extraction Methods
All‐payer claims, EHR data, and physician notes extracted from a centralized clinical registry.
Principal Findings
All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD‐9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk‐adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01).
Conclusions
The seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs.
AbstractInformation technologies, such as building information modeling (BIM), significantly improve the clash detection process, but the clash resolution process is still time consuming. Although ...some studies have devoted attention to this field, they rarely discuss the dependency relations between clashes, which impact clash resolution in practice. This paper proposes to adopt graph theory to capture clash dependency and harnesses the information embedded in BIM models. This provides decision support for clash resolution, specifically focusing on automatically generating a component change list to minimize potential change impacts. This paper maps this situation as a minimum-weight vertex cover problem and discusses how to use component attributes to calculate vertex weights for approximating component change impact. Then, a branch and bound algorithm is designed to search for an optimal component change list. The paper tests the framework in two construction projects, and the results show the efficiency of the designed algorithm. The validity of the proposed method is further supported by comparing the list generated by the proposed method with actual project solutions.
Building information modeling has demonstrated its advantage to support design coordination, specifically for automatic clash detection. Detecting clashes helps us identify problems, but the process ...for solving these problems is still manual and time-consuming. This paper proposes using network theory to improve clash resolution by optimizing the clash correction sequence. Building systems are often interdependent of each other, and the dependency relations between building components propagate the impacts of clashes. Ignoring the dependency may cause new clashes when solving a clash or cause iterative adjustments for a single building component. However, a well-organized clash correction sequence can help reduce these issues. Therefore, it is necessary to holistically discuss the clash correction sequence by considering the dependence between clashes. This paper analyzes clash dependencies based on building component dependency relations. We design an optimization algorithm for determining the optimal sequence based on the clash dependency network to minimize feedback dependency, which may cause design rework on a project in project practice. The proposed method is validated on a real building project. After comparing with the natural sequence detected by commercial software, we find that the optimized sequence significantly reduces feedback and automatically groups dependent clashes, which facilitates design coordination.
•Clash network is constructed based on the dependency relations between building components•The network is used to optimize clash correction sequence by minimizing feedbacks in it.•An algorithm is designed to identify the optimal correction sequence•This study impacts the practice by helping project teams to reduce potential iterative adjustments when solving clashes.
A considerable amount of research has addressed Internet of Things and connected communities. It is possible to exploit the sensing capabilities of connected communities, by leveraging the ...continuously growing use of cloud computing solutions and mobile devices. The pervasiveness of mobile sensors also enables the Mobile Crowd Sensing (MCS) paradigm, which aims at using mobile-embedded sensors to extend monitoring of multiple (environmental) phenomena in expansive urban areas. In this article, we discuss our approach with a cloud-based platform to pave the way for applying crowd sensing in urban scenarios. We have implemented a complete solution for environmental monitoring of several pollutants, like noise, air, electromagnetic fields, and so on in an urban area based on this paradigm. Through extensive experimentation, specifically on noise pollution, we show how the proposed infrastructure exhibits the ability to collect data from connected communities, and enables a seamless support of services needed for improving citizens’ quality of life and eventually helps city decision makers in urban planning.
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•A novel constraint-based event sequence mining algorithm to extract temporal treatment patterns.•An integrated predictive model that involves clinic, genomic and temporal treatment ...patterns on Glioblastoma patients.•Accurate prediction (c-statistic 0.85) obtained and clinically meaningful predictive features identified.
A significant challenge in treating rare forms of cancer such as Glioblastoma (GBM) is to find optimal personalized treatment plans for patients. The goals of our study is to predict which patients survive longer than the median survival time for GBM based on clinical and genomic factors, and to assess the predictive power of treatment patterns.
We developed a predictive model based on the clinical and genomic data from approximately 300 newly diagnosed GBM patients for a period of 2years. We proposed sequential mining algorithms with novel clinical constraints, namely, ‘exact-order’ and ‘temporal overlap’ constraints, to extract treatment patterns as features used in predictive modeling. With diverse features from clinical, genomic information and treatment patterns, we applied both logistic regression model and Cox regression to model patient survival outcome.
The most predictive features influencing the survival period of GBM patients included mRNA expression levels of certain genes, some clinical characteristics such as age, Karnofsky performance score, and therapeutic agents prescribed in treatment patterns. Our models achieved c-statistic of 0.85 for logistic regression and 0.84 for Cox regression.
We demonstrated the importance of diverse sources of features in predicting GBM patient survival outcome. The predictive model presented in this study is a preliminary step in a long-term plan of developing personalized treatment plans for GBM patients that can later be extended to other types of cancers.
In database-as-a-service platforms, automated ver-ification of query equivalence helps eliminate redundant computation in the form of overlapping sub-queries. Researchers have proposed two pragmatic ...techniques to tackle this problem. The first approach consists of reducing the queries to algebraic expressions and proving their equivalence using an algebraic theory. The limitations of this technique are threefold. It cannot prove the equivalence of queries with significant differences in the attributes of their relational operators (e.g., predicates in the filter operator). It does not support certain widely-used SQL features (e.g., NULL values). Its verification procedure is computationally intensive. The second approach transforms this problem to a constraint satisfaction problem and leverages a general-purpose solver to determine query equivalence. This technique consists of deriving the symbolic representation of the queries and proving their equivalence by determining the query containment relationship between the symbolic expressions. While the latter approach addresses all the limitations of the former technique, it only proves the equivalence of queries under set semantics (i.e., output tables must not contain duplicate tuples). However, in practice, database applications use bag semantics (i.e., output tables may contain duplicate tuples) In this paper, we introduce a novel symbolic approach for proving query equivalence under bag semantics. We transform the problem of proving query equivalence under bag semantics to that of proving the existence of a bijective, identity map between tuples returned by the queries on all valid inputs. We classify SQL queries into four categories, and propose a set of novel category-specific verification algorithms. We implement this symbolic approach in SPES and demonstrate that it proves the equivalence of a larger set of query pairs (95/232) under bag semantics compared to the SOTA tools based on algebraic (30/232) and symbolic approaches (67/232) under set and bag semantics, respectively. Furthermore, SPES is 3X faster than the symbolic tool that proves equivalence under set semantics.
MITOMAP (http://www.MITOMAP.org), a database for the human mitochondrial genome, has grown rapidly in data content over the past several years as interest in the role of mitochondrial DNA (mtDNA) ...variation in human origins, forensics, degenerative diseases, cancer and aging has increased dramatically. To accommodate this information explosion, MITOMAP has implemented a new relational database and an improved search engine, and all programs have been rewritten. System administrative changes have been made to improve security and efficiency, and to make MITOMAP compatible with a new automatic mtDNA sequence analyzer known as Mitomaster.
In this paper we have provided an extensive survey of the databases and other resources related to the current research in bioinformatics and the issues that confront the database researcher in ...helping the biologists. Initially we give an overview of the concepts and principles that are fundamental in understanding the basis of the data that has been captured in these databases. We briefly trace the evolution of biological advances and point out the importance of capturing data about genes, the fundamental building blocks that encode the characteristics of life and proteins that are the essential ingredients for sustaining life. The study of genes and proteins is becoming extremely important and is being known as genomics and proteomics, respectively. Whereas there are numerous databases related to various subfields of biology, we have maintained a focus on genomic and proteomic databases which are the crucial stepping stones for other fields and are expected to play an important role in the future applications of biology and medicine. A detailed listing of these databases with information about their sizes, formats and current status is presented. Related databases like molecular pathways and interconnection network databases are mentioned, but their full coverage would be beyond the scope of a single paper. We comment on the peculiar nature of the data in biology that presents special problems in organizing and accessing these databases. We also discuss the capabilities needed for database development and information management in the bioinformatics arena with particular attention to ontology development. Two research case studies based on our own research are summarized dealing with the development of a new genome database called Mitomap and the creation of a framework for discovery of relationships among genes from the biomedical literature. The paper concludes with an overview of the applications that will be driven from these databases in medicine and healthcare. A glossary of important terms is provided at the end of the paper.
Tabular data are pervasive. Although tables often describe multivariate data without explicit definitions of a network, it may be advantageous to explore the data by modeling it as a graph or network ...for analysis. Even when a given table design specifies a network structure, analysts may want to look at multiple networks from different perspectives, at different levels of abstraction, and with different edge semantics. We present a system called Ploceus that offers a general approach for performing multidimensional and multilevel network–based visual analysis on multivariate tabular data. Powered by an underlying relational algebraic framework, Ploceus supports flexible construction and transformation of networks through a direct manipulation interface and integrates dynamic network manipulation with visual exploration through immediate feedback mechanisms. We report our findings on the learnability and usability of Ploceus and propose a model of user actions in visualization construction using Ploceus.
How do company insiders trade? Do their trading behaviors differ based on their roles (e.g., chief executive officer vs. chief financial officer)? Do those behaviors change over time (e.g., impacted ...by the 2008 market crash)? Can we identify insiders who have similar trading behaviors? And what does that tell us? This work presents the first academic, large-scale exploratory study of insider filings and related data, based on the complete Form 4 fillings from the U.S. Securities and Exchange Commission. We analyze 12 million transactions by 370 thousand insiders spanning 1986–2012, the largest reported in academia. We explore the temporal and network-based aspects of the trading behaviors of insiders, and make surprising and counterintuitive discoveries. We study how the trading behaviors of insiders differ based on their roles in their companies, the types of their transactions, their companies’ sectors, and their relationships with other insiders. Our work raises exciting research questions and opens up many opportunities for future studies. Most importantly, we believe our work could form the basis of novel tools for financial regulators and policymakers to detect illegal insider trading, help them understand the dynamics of the trades, and enable them to adapt their detection strategies toward these dynamics.
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