OBJECTIVE: Describe the impact of synchronous vs asynchronous clinical decision support (CDS) on clinician behavior in a single-site randomized, controlled environment. MATERIALS AND METHODS: Mixed effects binomial logistic regression was used to compare the impact of synchronous against asynchronous messaging on neurology orders in a three-arm study. RESULTS: Asynchronous messaging resulted in a significant increase in patient neurology orders for evaluation (Odds ratio, alert-only arm: 1.88; 95% CI: 1.39, 2.55; alert and questionnaire arm: 1.99; 95% CI: 1.52, 2.62). DISCUSSION: Alerts sometimes generate little action on the part of clinicians. In this case, asynchronous inbox messaging significantly increased neurology orders. CONCLUSION: Depending on context, asynchronous messaging may be superior to synchronous messaging when recommending a referral in an outpatient setting.

Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In the present paper, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on two datasets. The first dataset consists of posts from the online health forum r/Cancer and the second dataset is a standard benchmark for topic modeling which consists of a collection of messages posted to 20 different news groups. When compared to the state-of-the-art generative document models (i.e., ETM and NVDM), pPSO is able to produce interpretable clusters. The results indicate that pPSO is able to capture both common topics as well as emergent topics. Moreover, the topic coherence of pPSO is comparable to that of ETM and its topic diversity is comparable to NVDM. The assignment parity of pPSO on a document completion task exceeded 90% for the 20NewsGroups dataset. This rate drops to approximately 30% when pPSO is applied to the same Skip-Gram embedding derived from a limited, corpus-specific vocabulary which is used by ETM and NVDM.

Broader patient-reported experiences in oncology are largely unknown due to the lack of available information from traditional data sources. Online health community data provide an exploratory way to uncover these experiences at a large scale. Analyzing these data can guide further studies towards understanding patients' needs and experiences. However, analysis of online health data is inherently difficult due to the unstructured nature of these data and the variety of ways information can be expressed over text. Specifically, subscribers may not disclose critical information such as the age of the patient in their posts. In fact, the number of health forum posts that explicitly mention the age of the patient is significantly lower than the number of posts that do not include this information in the Reddit r/Cancer health forum under consideration in the present paper. Health-focused studies often need to consider or control for age as a confounder, hence the importance of having sufficient age data. This paper presents a methodology that can help classify health forum posts according to four age groups (0-17, 18-39, 40-64 and 65 + years) even when the posts do not contain explicit mention of the age of the patient. First, the subset of the posts that include explicit mention of the age of the patient is identified. Second, the explicit age clues are removed from these posts and used to train the proposed age classifier. The resulting classifier is able to infer the age of the patient using only implicit age clues with an average true positive rate (TPR) of 71%. This TPR is comparable to the average TPR of 69% obtained from human annotations for the same set of posts.

The identification of the vocation of an unknown heavy-duty vehicle is valuable to parts’ manufacturers. This study proposes a methodology for vocation identification that is based on clustering techniques. Two clustering algorithms are considered: K-Means and Expectation Maximization. These algorithms are used to first construct the operating profile of each vocation from a set of vehicles with known vocations. The vocation of an unknown vehicle is then determined by using one-versus-all or one-versus-one assignment. The one-versus-one assignment is more desirable because it scales with an increasing number of vocations and requires less data to be collected from the unknown vehicles. These characteristics are important to parts’ manufacturers since their parts may be installed in different vocations. Specifically, this paper compares the one-versus-one bracket and the one-versus-one round-robin tournament assignments to the one-versus-all assignment. The tournament assignments are able to scale with an increasing number of vocations. However, the bracket assignment also benefits from a linear time complexity. The results show that despite its scalability and computational efficiency, the bracket vocation identification model has a high accuracy and a comparable precision and recall. The NREL Fleet DNA drive cycle dataset is used to demonstrate these findings.

Due to the limited natural water resources and the increase in population, managing water consumption is becoming an increasingly important subject worldwide. In this paper, we present and compare different machine learning models that are able to predict water demand for Central Indiana. The models are developed for two different time scales: daily and monthly. The input features for the proposed model include weather conditions (temperature, rainfall, snow), social features (holiday, median income), date (day of the year, month), and operational features (number of customers, previous water demand levels). The importance of these input features as accurate predictors is investigated. The results show that daily and monthly models based on recurrent neural networks produced the best results with an average error in prediction of 1.69% and 2.29%, respectively for 2016. These models achieve a high accuracy with a limited set of input features.

Optimizing physical goods distribution by providing increased visibility to trading partners can directly impact product cost. However, current supply chain information systems often lack the ability to cost-effectively relay ground truth information in near real time to all stakeholders and most importantly to the supplier and the customer during the transport of the shipment. This paper presents a solution that addresses this gap through a peer-to-peer architecture that can support the increasing demand for visibility and timely delivery of information during the physical distribution phase of supply chain. Additional features of the proposed solution include scalability, privacy and validity of the information that is delivered to the trading partners. The solution enables small, medium and large businesses to interact in a dynamic and shipment-centric manner through a private blockchain sub-ledger that digitizes the transfer of custody chain for each shipment. Information in this private ledger is augmented by a public event ledger that reflects the movement of the shipment in near real time. Third party monitors are engaged in the validation of the geolocation of the shipments by posting their physical proximity in the form of events to the public ledger.

Influenza immunizations are critical for preventing deadly and costly outbreaks within the United States, yet flu vaccination coverage has annually remained low. Flu vaccination monitoring is important since early detection of anomalies in flu vaccination rates allows faster communication among public health agencies and local governments thereby facilitating rapid deployment of flu vaccination campaigns. Currently, there is no real-time detection mechanism for monitoring flu vaccination rates. Traditional flu vaccination rate tracking systems suffer information lags due to reliance on reports submitted by medical practices which are updated only once a week. In this paper, we demonstrate the feasibility of a real-time, low-cost and labor-efficient alternative monitoring method to the flu vaccination rate surveillance system used by the United States Department of Health and Human Services (HHS). This alternative is based on social network data and specifically Twitter data. We demonstrate the viability of this approach by measuring the level of correlation between the frequency of "tweets" mentioning flu vaccinations and HHS data on flu vaccination rates.

Building a high-quality bioinformatics workflow needs not only good biology knowledge, but also extensive knowledge of existing online bioinformatics services. To facilitate this task, we present a novel approach of automatic workflow composition in SIBIOS system. Our approach regards the task of workflow composition as a planning problem, and uses Graphplan as the solution planner. We extended Graphplan to allow users to specify particular services that they want to use in the workflow, as well as the order those services are used. Automatically generated workflows are used as a baseline for researchers, to be refined to meet their needs. This paper describes the theoretical design and practical evaluation of automatic workflow composition in SIBIOS

Biologists are increasingly relying on online biological databases to conduct their research. Despite the availability of hundreds of such databases, only a handful of them are effectively deployed by the community. The main reason for this is the lack of metadata provided by the databases, such as, the types of data stored, its coverage, and its relationship with other databases. Nevertheless, each database has a potential for supporting existing research investigations. The opportunity to leverage the wealth of data available presents several challenges related to data integration and representation. In this paper, we present BioFacets, a new integration system for Web based biological databases. The main feature of this system is the proposal of a new faceted classification system that allows for dynamic categorization of biological result sets. Other features of the system include query optimization, cache management and support for a meta-search engine that allows the system to efficiently search across multiple online biological databases

Integrating life science Web databases, while important and necessary, is a challenge for current integration systems mainly due to the large number of these databases, their heterogeneity and the fact that their interfaces may change often. BACIIS, a biological and chemical information integration system, is a tightly coupled federated database system that uses the mediator wrapper method in order to retrieve information from several remote Web databases. BACIIS relies on a semi-automated approach for generating and maintaining wrappers in order to provide a scalable system with a limited maintenance overhead. The semi-automatic wrapper induction in BACIIS is efficient because it is based on, but not limited to a domain knowledge. Tests show that the use of ontology increases the accuracy of the wrapper induction. We also present how the wrapper induction system facilitates wrapper update, and assists in the information extraction. By using a wrapper induction system for creation and maintenance of wrappers, scalability, flexibility, and stability of the integrated information system is easily maintained.

Life science Web databases are becoming increasingly essential in conducting everyday biological research. With more than 300 life science Web databases and the growing size of the life science data, searching and managing these complex data requires technology beyond that of traditional database systems. The open research issues include the interoperability of geographically distributed autonomous databases, which are generally only Web-accessible, and the seamless semantic-based integration of these databases with total transparency to the user. In this paper, the implementation of a Biological and Chemical Information Integration System (BACIIS) is presented. BACIIS supports the integration of multiple heterogeneous life science Web databases and allows the execution of global applications that extend beyond the boundaries of individual databases. This paper discusses the architecture of BACIIS. It also discusses the techniques used to extract and integrate data from the various life science Web databases.

Modern biological studies rely on life science Web databases as well as sophisticated Web-based software tools (e.g., homology search tools, modeling and visualization tools). These tools or Web services often have to be combined and integrated in order to support a given study (i.e. an in-silico experiment). The large number of the available Web services makes a service discovery process that can identify the set of services that satisfy a given number of constraints from the pool of all the available Web services crucial. Without such an automated service discovery process the user will have to have a thorough knowledge of all of the existing Web services, a task-which is impractical in the life science domain. In this paper, a scalable approach to service discovery in the biological domain is presented. The approach is based on and guided by a domain ontology.

Summary form only given. A successful approach to building QSAR models was proposed by other researchers. It uses binary particle swarm optimization (BPSO) for feature selection in the first stage, and a back propagation neural network in the second stage to generate a QSAR model based on the features selected in the first stage. We start by reestablishing the results of this approach on an extended number of data sets. A new method is then proposed that addresses the limitation of back propagation. This approach uses particle swarm optimization (PSO) in the second stage for training and bootstrap aggregation (bagging) in order to overcome the instability of PSO. The proposed approach yields robust QSAR models, while reducing the variability due to the choice of the back propagation parameters.

Abstract Gene clustering, the process of grouping related genes in the same cluster, is at the foundation of different genomic studies that aim at analyzing the function of genes. Microarray technologies have made it possible to measure gene expression levels for thousands of genes simultaneously. For knowledge to be extracted from the datasets generated by these technologies, the datasets have to be presented to a scientist in a meaningful way. Gene clustering methods serve this purpose. In this paper, a hybrid clustering approach that is based on self‐organizing maps and particle swarm optimization is proposed. In the proposed algorithm, the rate of convergence is improved by adding a conscience factor to the self‐organizing maps algorithm. The robustness of the result is measured by using a resampling technique. The algorithm is implemented on a cluster of workstations. Copyright © 2004 John Wiley & Sons, Ltd.

In the past decade chemical and biological laboratory experiments have generated an explosive amount of data. As a result, a set applications that manipulate these dynamic, heterogeneous and massive amounts of data have emerged. An example of such applications in the pharmaceutical industry is the computational process involved in the early drug discovery of lead drug candidates for a given target disease. The discovery of lead drug candidates requires both consecutive and random data access to the pharmaceutical drug candidate database. This paper focuses on performance enhancement techniques for the pharmaceutical drug candidate database application. In particular, this paper compares static load balancing and dynamic load balancing in the context of the drug candidate database application. This database application is based on multi-queries. Some of these queries are multi-join queries.

The incompatibilities among complex data formats and various schema used by biological databases that house these data are becoming a bottleneck in biological research. For example, biological data format varies from simple words (e.g. gene name), numbers (e.g. molecular weight) to sequence strings (e.g. nucleic acid sequence), to even more complex data formats such as taxonomy trees. Some information is embedded in narrative text, such as expert comments and publications. Some other information is expressed as graphs or images (e.g. pathways networks). The confederation of heterogeneous web databases has become a crucial issue in today's biological research. In other words, interoperability has to be archieved among the biological web databases and the heterogeneity of the web databases has to be resolved. This paper presents a biological ontology, BAO, and discusses its advantages in supporting the semantic integration of biological web databases are discussed.

The Global Change Master Directory (GCMD) is an Earth science information repository that specifically tracks research data on global climatic change. Building a directory of Earth science metadata that allows the exchange of metadata content among partner organizations is challenging due to the complex issues involved in supporting heterogeneous metadata schema, database schema, database implementation and platforms. This demonstration presents the design of the MD8 (Master Directory v8.0), which allows automated exchange of metadata content among Earth science collaborators through a proposed asynchronous distributed transaction protocol. Specifically, the demonstration focuses on the local data agent that captures local database updates and broadcasts them to other cooperating nodes asynchronously using an announcer.

Recent advances in the development of new, high throughput screening technologies have resulted in a wealth of life science data. The complexity and volume of these data require technology beyond that of traditional database systems. Open research issues include the seamless, semantic-based integration of geographically distributed, autonomous web databases. Biological And Chemical Integration Information System (BACIIS) addresses these issues. This paper discusses the challenges associated with the integration of life science web databases and the techniques used in BACIIS to address these challenges. Particularly, the execution of life science, multidatabase queries is investigated, and an approach that can overcome the limitations of the query capabilities of each individual life science web database participating in the integration is presented. This approach hides the heterogeneity of the individual databases from the user while providing an efficient execution plan for complex, life science, multidatabase queries.

Membrane bound ion channels give rise to many of the electrical signal characteristics exhibited by neurons. Ion channel models of neural function such as that proposed by Hodgkin-Huxley can be represented as a set of differential equations. Solving these differential equations for a given neuron involves finding optimal values for the parameters that define the Hodgkin-Huxley equations. Most often, these parameters are evaluated using an optimization algorithm that takes as input ion channel current data recorded from a neuron using the voltage clamp technique. Real-valued optimization algorithms often fail to find a global optimum for the parameters of the Hodgkin-Huxley differential equations. Here, the authors show that interval analysis based optimization algorithm, a branch and bound algorithm, provides an accurate solution for the Hodgkin-Huxley model.

Large scientific databases often consist of heterogeneous components that differ in the number of attributes and the number of tuples. In this paper, we show that increased performance is achieved when a complex schema is used to represent different components of a data repository. The driving application used in this study is a pharmaceutical drug repository. The types of access patterns associated with this repository consist of the retrieval of a selected set of attribute values for a consecutive or a random set of tuples. These consecutive and random access patterns, which are typical of a drug discovery experiment, are used to measure the data retrieval throughput for different schemas.

An explosion in the amount of data generated through chemical and biological experimentation has been observed in recent years. This rapid proliferation of vast amounts of data has led to a set of cheminformatics and bioinformatics applications that manipulate dynamic, heterogeneous, and massive data. An example of such applications in the pharmaceutical industry is the computational process involved in the early discovery of lead drug candidates for a given target disease. This computational process includes repeated sequential and random accesses to a drug candidate database. Using the above pharmaceutical application, an experimental study was conducted in this paper that shows that for optimal performance, the degree of parallelism exploited in the application should be adjusted according to the drug candidate database instance size and the machine size. Additionally, different degrees of parallelism should be used depending on whether the access to the drug candidate database is random or sequential.