Probabilistic Cause-of-death Assignment using Verbal Autopsies.

In regions without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In such regions the majority of deaths occur outside hospitals and are not recorded. Worldwide, fewer than one-third of deaths are assigned a cause, with the least information available from the most impoverished nations. In populations like this, verbal autopsy (VA) is a commonly used tool to assess cause of death and estimate cause-specific mortality rates and the distribution of deaths by cause. VA uses an interview with caregivers of the decedent to elicit data describing the signs and symptoms leading up to the death. This paper develops a new statistical tool known as InSilicoVA to classify cause of death using information acquired through VA. InSilicoVA shares uncertainty between cause of death assignments for specific individuals and the distribution of deaths by cause across the population. Using side-by-side comparisons with both observed and simulated data, we demonstrate that InSilicoVA has distinct advantages compared to currently available methods

Machine learning from crowds a systematic review of its applications

Crowdsourcing opens the door to solving a wide variety of problems that previ-ously were unfeasible in the field of machine learning, allowing us to obtain rela-tively low cost labeled data in a small amount of time. However, due to theuncertain quality of labelers, the data to deal with are sometimes unreliable, forcingpractitioners to collect information redundantly, which poses new challenges in thefield. Despite these difficulties, many applications of machine learning usingcrowdsourced data have recently been published that achieved state of the artresults in relevant problems. We have analyzed these applications following a sys-tematic methodology, classifying them into different fields of study, highlightingseveral of their characteristics and showing the recent interest in the use of crowd-sourcing for machine learning. We also identify several exciting research linesbased on the problems that remain unsolved to foster future research in this field

EVALUATING ARTIFICIAL INTELLIGENCE FOR OPERATIONS IN THE INFORMATION ENVIRONMENT

Recent advances in artificial intelligence (AI) portend a future of accelerated information cycles and intensified technology diffusion. As AI applications become increasingly prevalent and complex, Special Operations Forces (SOF) face the challenge of discerning which tools most effectively address operational needs and generate an advantage in the information environment. Yet, SOF currently lack an end user–focused evaluation framework that could assist information practitioners in determining the operational value of an AI tool. This thesis proposes a practitioner’s evaluation framework (PEF) to address the question of how SOF should evaluate AI technologies to conduct operations in the information environment (OIE). The PEF evaluates AI technologies through the perspective of the information practitioner who is familiar with the mission, the operational requirements, and OIE processes but has limited to no technical knowledge of AI. The PEF consists of a four-phased approach—prepare, design, conduct, recommend—that assesses nine evaluation domains: mission/task alignment; data; system/model performance; user experience; sustainability; scalability; affordability; ethical, legal, and policy considerations; and vendor assessment. By evaluating AI through a more structured, methodical approach, the PEF enables SOF to identify, assess, and prioritize AI-enabled tools for OIE.Outstanding ThesisMajor, United States ArmyApproved for public release. Distribution is unlimited

The role of informal networks in creating knowledge among health-care managers: a prospective case study

Background: Health and well-being services, in common with many public services, cannot be delivered by a single organisation and require co-ordination across several organisations in a locality. There is some evidence, mostly from other sectors, that middle managers play pivotal roles in this co-ordination by developing networks of relationships with colleagues in other organisations. These networks of relationships, established over time, provide contexts in which managers can, collectively, create the knowledge needed to address the challenges they encounter. Relatively little is known, however, about how these knowledge-creation processes work in a health-care context. Aim: This study focuses on how health and well-being managers collectively create knowledge. Our objectives were to develop a better understanding of the way that knowledge is created within and between health-care organisations, across different managerial levels, and of the role played by informal networks in those processes. Methods: The study was undertaken in health and well-being services in three sites in northern England, employing a case study design. The field methods used were landscape mapping, structured data collection for network analysis and latent position cluster analysis, and semi-structured interviews for narrative analysis. Our network modelling approach used the concepts of latent position network models and latent position cluster models. We used these models to identify clusters of people within networks, and people who acted as bridgers between clusters. We then interviewed middle managers who – on the evidence of our cluster models – occupied similar positions in our graphs. The latter were used to produce practice-based narratives of knowledge creation. Results: Our narrative results showed that middle managers were synthesisers, in three different senses. First, they draw on different types of information, from a range of sources – quantitative routine data about populations and services, reports on progress against contractual targets, research evidence, and intelligence from colleagues in other localities. Second, they are able to link national policies and local priorities, and reconcile them with local operational realities. They are not always successful, but can integrate the different approaches and working practices of NHS, local authority, private and voluntary organisations. Third, they are able to link ideas, negotiation and action. We found that the network results were most usefully represented as clusters, explaining relationships between actors. Actors within clusters had common attributes, and as a result we were able to interpret the broad purpose of each of the clusters in the graphs for each site. The most useful number of clusters was three or four for both network types, and for both sampling periods, at each of the three sites. The clusters at all three sites had a mix of organisations represented within them. There was a mix of seniorities of managers in all clusters. Relationships were simultaneously formal and informal: formal contracts were managed in a context of ongoing conversations and negotiations. Relationships were simultaneously stable and fluid, with stable ‘cores’ of managers but memberships that varied substantially between two periods of data collection. Conclusions: Our theory about knowledge creation was broadly supported. Managers of health and well-being services develop and maintain knowledge collectively. Their collective efforts are typically manifested either in projects requiring multiorganisational inputs or in taking ideas from genesis to the delivery of a new service. The cluster modelling suggests that networks of managers are able to maintain relationships, and hence conserve technical and prudential knowledge, over months and years. Priorities for future work include establishing the value of latent cluster modelling in understanding the work of groups and teams in other health and social care settings, and studying knowledge creation in the context of the interorganisational co-ordination of services. Funding: The National Institute for Health Research Health Services and Delivery Research programme

Sentiment Analysis of Online Media

GfKl 2011: Joint Conference of the German Classification Society (GfKl) and the German Association for Pattern Recognition (DAGM) August 31 to September 2, 2011 and the IFCS 2011: Symposium of the International Federation of Classification Societies (IFCS) August 30, 2011, Frankfurt am Main, GermanyA joint model for annotation bias and document classification is presented in the context of media sentiment analysis. We consider an Irish online media data set comprising online news articles with user annotations of negative, positive or irrelevant impact on the Irish economy. The joint model combines a statistical model for user annotation bias and a Naive Bayes model for the document terms. An EM algorithm is used to estimate the annotation bias model, the unobserved biases in the user annotations, the classifier parameters and the sentiment of the articles. The joint modeling of both the user biases and the classifier is demonstrated to be superior to estimation of the bias followed by the estimation of the classifier parameters.Author has checked copyrightDue to be published by Springer as part of their Studies in Classification, Data Analysis, and Knowledge Organization series. Publication date is unknown and there is a 12month embargo after publication - DG 09/11/2012 IF PUBLISHED APPLY EMBARGO, due for publication April 2013 once published requires dc.rights value 'The final publication is available at springerlink.com' and dc.date.copyright '2012 Springer'- OR 18/12/12 Possibly to be published in 2013 - OR 2013-05-29Names J

A System for Sentiment Analysis of Online-Media with TensorFlow

Dieses Dokument stellt einen möglichen Ansatz für denAufbau eines Sentiment-Analysesystems für die deutsche Sprache mittels TensorFlow- und menschenmarkierten Datensätzen vor. Diese Arbeit gibt eine Einführung in Konzepte des maschinellen Lernens und in TensorFlow und zeigt, wie man mit dem Werkzeug einen einfachen RNN erstellt. Der Schwerpunkt des Papiers liegt hauptsächlich auf den unterschiedlichen Ergebnissen, die bei der Verwendung unterschiedlicher Datensätze erzielt wurden.This document presents a possible approach for the construction of a Sentiment Analysis system for the German language by means of TensorFlow and human-labeled data sets. This work gives an introduction to machine learning concepts and to TensorFlow and shows how to build a simple RNN with the tool. The paper’s focus is mainly on the di erent results obtained from using di erent data sets

Sentiment analysis of online media - extracting a trading signal for commodities

Sentiment Analysis is an extensively studied topic that is increasingly applied in financial applications. As a method to forecast asset prices, text classification is used to incorporate the sentiment of investors to enhance the accuracy of price forecasts. This thesis examines a broad range of news websites from the Common Crawl news dataset. The challenging task is applying topic models to extract sentences, targeting the development of crude oil price movementin acoherent way, by evaluationthe accuracy of the model. The results revealed that highly targeted data is necessary to make appropriate price predictions based on news sentiment. Extracting meaningful sentences is a challenging task that needs further investigation. A framework for incorporating and processing the data to prepare it to fit the text classifiers can further enhance the already impressive results of predicting the prices of financial assets based on investors' sentiment