A Conceptual Model for Network Decision Support Systems

We introduce the concept of a network DSS (NWDSS) consisting of fluid, heterogeneous nodes of human and machine agents, connected by wireless technology, which may enter and leave the network at unpredictable times, yet must also cooperate in decision-making activities. We describe distinguishing properties of the NWDSS and propose a 3-tier conceptual model comprised of digital infrastructure, transactive memory systems and emergent collaborative decision-making. We suggest a decision loop of Sense-Analyze-Adapt-Memory leveraging TMS as a starting point for addressing the agile collaborative requirements of emergent decision-making. Several examples of innovative NWDSS services are presented from Naval Postgraduate School field experiments

Automated self-service modeling: predictive analytics as a service

The article of record as published may be found at https://doi.org/10.1007/s10257-011-0185-1Research into service provision and innovation is becoming progres- sively more important as automated service-provision via the web matures as a technology. We describe a web-based targeting platform that uses advanced dynamic model building techniques to conduct intelligent reporting and modeling. The impact of the automated targeting services is realized through a knowledge base that drives the development of predictive model(s). The knowledge base is comprised of a rules engine that guides and evaluates the development of an automated model-building process. The template defines the model classifier (e.g., logistic regression, multinomial logit, ordinary least squares, etc.) in concert with rules for data filling and transformations. Additionally, the template also defines which variables to test (‘‘include’’ rules) and which variables to retain (‘‘keep’’ rules). The ‘‘final’’ model emerges from the iterative steps undertaken by the rules engine, and is utilized to target, or rank, the best prospects. This automated modeling approach is designed to cost-effectively assist businesses in their tar- geting activities—independent of the firm’s size and targeting needs. We describe how the service has been utilized to provide ‘‘targeting services’’ for a small to medium business direct marketing campaign, and for direct sales-force targeting in a larger firm. Empirical results suggest that the automated modeling approach provides superior ‘‘service’’ in terms of cost and timing compared to more tra- ditional manual service provision

Resume of Daniel Roy Dolk, 1993-10

Naval Postgraduate School Faculty ResumeDr. Dolk joined the faculty of the Naval Postgraduate School in August 1982 where he is currently an assistant professor of MIS

MODEL INTERPRETATION AND EXPLAINABILITY Towards Creating Transparency in Prediction Models

Explainable AI (XAI) has a counterpart in analytical modeling which we refer to as model explainability. We tackle the issue of model explainability in the context of prediction models. We analyze a dataset of loans from a credit card company and apply three stages: execute and compare four different prediction methods, apply the best known explainability techniques in the current literature to the model training sets to identify feature importance (FI) (static case), and finally to cross-check whether the FI set holds up under “what if” prediction scenarios for continuous and categorical variables (dynamic case). We found inconsistency in FI identification between the static and dynamic cases. We summarize the “state of the art” in model explainability and suggest further research to advance the field

Unified Explanations in Machine Learning Models: A Perturbation Approach

A high-velocity paradigm shift towards Explainable Artificial Intelligence (XAI) has emerged in recent years. Highly complex Machine Learning (ML) models have flourished in many tasks of intelligence, and the questions have started to shift away from traditional metrics of validity towards something deeper: What is this model telling me about my data, and how is it arriving at these conclusions? Previous work has uncovered predictive models generating explanations contrasting domain experts, or excessively exploiting bias in data that renders a model useless in highly-regulated settings. These inconsistencies between XAI and modeling techniques can have the undesirable effect of casting doubt upon the efficacy of these explainability approaches. To address these problems, we propose a systematic, perturbation-based analysis against a popular, model-agnostic method in XAI, SHapley Additive exPlanations (Shap). We devise algorithms to generate relative feature importance in settings of dynamic inference amongst a suite of popular machine learning and deep learning methods, and metrics that allow us to quantify how well explanations generated under the static case hold. We propose a taxonomy for feature importance methodology, measure alignment, and observe quantifiable similarity amongst explanation models across several datasets

Model Integration and Modeling Languages: A Process Perspective

The article of record as published may be found at https://www.jstor.org/stable/23010778Development of large-scale models often involves—or, certainly could benefit from—linking existing models. This process is termed model integration and involves two related aspects: (1) the coupling of model representations, and (2) the coupling of the processes for evaluating, or executing, instances of these representations. Given this distinction, we overview model integration capabilities in existing executable modeling languages, discuss current theoretical approaches to model integration, and identify the limiting assumptions implicitly made in both cases. In particular, current approaches assume away issues of dynamic variable correspondence and synchronization in composite model execution. We then propose a process-oriented conceptualization and associated constructs that overcome these limiting assumptions. The constructs allow model components to be used as building blocks for more elaborate composite models in ways unforeseen when the components were originally developed. While we do not prove the sufficiency of the constructs over the set of all model types and integration configurations, we present several examples of model integration from various domains to demonstrate the utility of the approach

Model integration and a theory of models

Model integration extends the scope of model management to include the dimension of manipulation as well. This invariably leads to comparisons with database theory. Model integration is viewed from four perspectives: Organizational, definitional, procedural, and implementational. Strategic modeling is discussed as the organizational motivation for model integration. Schema and process integration are examined as the logical and manipulation counterparts of model integration corresponding to data definition and manipulation, respectively. A model manipulation language based on structured modeling and communicating structured models is suggested which incorporates schema and process integration. The use of object-oriented concepts for designing and implementing integrated modeling environments is discussed. Model integration is projected as the springboard for building a theory of models equivalent in power to relational theory in the database community.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31033/1/0000710.pd

Estimating burden of influenza-associated influenza-like illness and severe acute respiratory infection at public healthcare facilities in Romania during the 2011/12-2015/16 influenza seasons.

BACKGROUND: Influenza is responsible for substantial morbidity and mortality, but there is limited information on reliable disease burden estimates, especially from middle-income countries in the WHO European Region. OBJECTIVES: To estimate the incidence of medically attended influenza-associated influenza-like illness (ILI) and hospitalizations due to severe acute respiratory infection (SARI) presenting to public healthcare facilities in Romania. PATIENTS/METHODS: Sentinel influenza surveillance data for ILI and SARI from 2011/12-2015/16, including virological data, were used to estimate influenza-associated ILI and SARI incidence/100 000 and their 95% confidence intervals (95% CI). RESULTS: The overall annual incidence of ILI and influenza-associated ILI per 100 000 persons in Romania varied between 68 (95% CI: 61-76) and 318 (95% CI: 298-338) and between 23 (95% CI: 19-29) and 189 (95% CI: 149-240), respectively. The highest ILI and influenza incidence was among children aged 0-4 years. We estimated that SARI incidence per 100 000 persons was 6 (95% CI: 5-7) to 9 (95% CI: 8-10), of which 2 (95% CI: 1-2) to 3 (95% CI: 2-4) were due to influenza. Up to 0.3% of the Romanian population were annually reported with ILI, and 0.01% was hospitalized with SARI, of which as much as one-third could be explained by influenza. CONCLUSIONS: This evaluation was the first study estimating influenza burden in Romania. We found that during each influenza season, a substantial number of persons in Romania suffer from influenza-related ILI or are hospitalized due to influenza-associated SARI

Late-Winter Abundance and Substrate Associations of Benthos in Pool 13, Upper Mississippi River

In Pool 13 on the Upper Mississippi River substantial differences were observed in macroinvertebrate abundance and substrate composition among six habitat types: tailwater, main channel, main channel border, side channel, slough, and lake. Densities were highest in slough, main channel border, and lake habitats, but the number of taxa was greatest in tailwater and main channel habitats. The abundance of 38 of the 42 identified taxa was significantly correlated with either the percentage of at least one particle size fraction or the percentage of organic matter in the substrate. Variation in substrate composition among the six habitat types appeared to influence macroinvertebrate abundance

Prevalence and clinical profile of microcephaly in South America pre-Zika, 2005-14: prevalence and case-control study

Submitted by Sandra Infurna ([email protected]) on 2017-12-07T10:08:10Z No. of bitstreams: 1 eduardo_castilla_etal_IOC_2017.pdf: 420663 bytes, checksum: 1bda2b904f26e96d052e0b767bc85897 (MD5)Approved for entry into archive by Sandra Infurna ([email protected]) on 2017-12-07T10:25:41Z (GMT) No. of bitstreams: 1 eduardo_castilla_etal_IOC_2017.pdf: 420663 bytes, checksum: 1bda2b904f26e96d052e0b767bc85897 (MD5)Made available in DSpace on 2017-12-07T10:25:41Z (GMT). No. of bitstreams: 1 eduardo_castilla_etal_IOC_2017.pdf: 420663 bytes, checksum: 1bda2b904f26e96d052e0b767bc85897 (MD5) Previous issue date: 2017Universidade Federal do Rio de Janeiro, Rio de Janeiro. Instituto de Biologia. Departamento de Genética. Estudo colaborativo latino-americano de malformações congênitas. Rio de Janeiro, RJ, Brasil / Instituto Nacional de Genética Médica Populacional. Porto Alegre, RS, Brasil.Ulster University. Institute of Nursing and Health Research. Maternal Fetal and Infant Research Centre. Newtownabbey, Northern Ireland, UK.Instituto Nacional de Genética Médica Populacional. Porto Alegre, RS, Brasil / ECLAMC at Center for Medical Education and Clinical Research (CEMIC-CONICET). Buenos Aires, Argentina.Universidade Federal do Rio de Janeiro, Rio de Janeiro. Instituto de Biologia. Departamento de Genética. Estudo colaborativo latino-americano de malformações congênitas. Rio de Janeiro, RJ, Brasil / Instituto Nacional de Genética Médica Populacional. Porto Alegre, RS, Brasil.Instituto Nacional de Genética Médica Populacional. Porto Alegre, RS, Brasil / ECLAMC at Center for Medical Education and Clinical Research (CEMIC-CONICET). Buenos Aires, Argentina.Instituto Nacional de Genética Médica Populacional. Porto Alegre, RS, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Epidemiologia de Malformações Congênitas. Estudo Colaborativo Latino-americano de Malformações Congênitas. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Genética Médica Populacional. Porto Alegre, RS, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Epidemiologia de Malformações Congênitas. Estudo Colaborativo Latino-americano de Malformações Congênitas. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Genética Médica Populacional. Porto Alegre, RS, Brasil / ECLAMC at Center for Medical Education and Clinical Research (CEMIC-CONICET). Buenos Aires, Argentina.Objective To describe the prevalence and clinical spectrum of microcephaly in South America for the period 2005-14, before the start of the Zika epidemic in 2015, as a baseline for future surveillance as the Zika epidemic spreads and as other infectious causes may emerge in future.Design Prevalence and case-control study.Data sources ECLAMC (Latin American Collaborative Study of Congenital Malformations) database derived from 107 hospitals in 10 South American countries, 2005 to 2014. Data on microcephaly cases, four non-malformed controls per case, and all hospital births (all births for hospital based prevalence, resident within municipality for population based prevalence). For 2010-14, head circumference data were available and compared with Intergrowth charts.Results 552 microcephaly cases were registered, giving a hospital based prevalence of 4.4 (95% confidence interval 4.1 to 4.9) per 10 000 births and a population based prevalence of 3.0 (2.7 to 3.4) per 10 000. Prevalence varied significantly between countries and between regions and hospitals within countries. Thirty two per cent (n=175) of cases were prenatally diagnosed; 29% (n=159) were perinatal deaths. Twenty three per cent (n=128) were associated with a diagnosed genetic syndrome, 34% (n=189) polymalformed without a syndrome diagnosis, 12% (n=65) with associated neural malformations, and 26% (n=145) microcephaly only. In addition, 3.8% (n=21) had a STORCH (syphilis, toxoplasmosis, other including HIV, rubella, cytomegalovirus, and herpes simplex) infection diagnosis and 2.0% (n=11) had consanguineous parents. Head circumference measurements available for 184/235 cases in 2010-14 showed 45% (n=82) more than 3 SD below the mean, 24% (n=44) between 3 SD and 2 SD below the mean, and 32% (n=58) larger than -2 SD.Conclusion Extrapolated to the nearly 7 million annual births in South America, an estimated 2000-2500 microcephaly cases were diagnosed among births each year before the Zika epidemic began in 2015. Clinicians are using more than simple metrics to make microcephaly diagnoses. Endemic infections are important enduring causes of microcephaly