23,334 research outputs found
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Using temporal patterns in medical records to discern adverse drug events from indications.
Researchers estimate that electronic health record systems record roughly 2-million ambulatory adverse drug events and that patients suffer from adverse drug events in roughly 30% of hospital stays. Some have used structured databases of patient medical records and health insurance claims recently-going beyond the current paradigm of using spontaneous reporting systems like AERS-to detect drug-safety signals. However, most efforts do not use the free-text from clinical notes in monitoring for drug-safety signals. We hypothesize that drug-disease co-occurrences, extracted from ontology-based annotations of the clinical notes, can be examined for statistical enrichment and used for drug safety surveillance. When analyzing such co-occurrences of drugs and diseases, one major challenge is to differentiate whether the disease in a drug-disease pair represents an indication or an adverse event. We demonstrate that it is possible to make this distinction by combining the frequency distribution of the drug, the disease, and the drug-disease pair as well as the temporal ordering of the drugs and diseases in each pair across more than one million patients
Controlled Language and Baby Turing Test for General Conversational Intelligence
General conversational intelligence appears to be an important part of
artificial general intelligence. Respectively, it requires accessible measures
of the intelligence quality and controllable ways of its achievement, ideally -
having the linguistic and semantic models represented in a reasonable way. Our
work is suggesting to use Baby Turing Test approach to extend the classic
Turing Test for conversational intelligence and controlled language based on
semantic graph representation extensible for arbitrary subject domain. We
describe how the two can be used together to build a general-purpose
conversational system such as an intelligent assistant for online media and
social network data processing.Comment: 10 pages, 4 figure
Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records
The paper presents a systematic review of state-of-the-art approaches to
identify patient cohorts using electronic health records. It gives a
comprehensive overview of the most commonly de-tected phenotypes and its
underlying data sets. Special attention is given to preprocessing of in-put
data and the different modeling approaches. The literature review confirms
natural language processing to be a promising approach for electronic
phenotyping. However, accessibility and lack of natural language process
standards for medical texts remain a challenge. Future research should develop
such standards and further investigate which machine learning approaches are
best suited to which type of medical data
Recent Advances in Zero-shot Recognition
With the recent renaissance of deep convolution neural networks, encouraging
breakthroughs have been achieved on the supervised recognition tasks, where
each class has sufficient training data and fully annotated training data.
However, to scale the recognition to a large number of classes with few or now
training samples for each class remains an unsolved problem. One approach to
scaling up the recognition is to develop models capable of recognizing unseen
categories without any training instances, or zero-shot recognition/ learning.
This article provides a comprehensive review of existing zero-shot recognition
techniques covering various aspects ranging from representations of models, and
from datasets and evaluation settings. We also overview related recognition
tasks including one-shot and open set recognition which can be used as natural
extensions of zero-shot recognition when limited number of class samples become
available or when zero-shot recognition is implemented in a real-world setting.
Importantly, we highlight the limitations of existing approaches and point out
future research directions in this existing new research area.Comment: accepted by IEEE Signal Processing Magazin
Reverse enGENEering of regulatory networks from Big Data: a guide for a biologist
Omics technologies enable unbiased investigation of biological systems
through massively parallel sequence acquisition or molecular measurements,
bringing the life sciences into the era of Big Data. A central challenge posed
by such omics datasets is how to transform this data into biological knowledge.
For example, how to use this data to answer questions such as: which functional
pathways are involved in cell differentiation? Which genes should we target to
stop cancer? Network analysis is a powerful and general approach to solve this
problem consisting of two fundamental stages, network reconstruction and
network interrogation. Herein, we provide an overview of network analysis
including a step by step guide on how to perform and use this approach to
investigate a biological question. In this guide, we also include the software
packages that we and others employ for each of the steps of a network analysis
workflow
Verification, Validation and Integrity of Distributed and Interchanged Rule Based Policies and Contracts in the Semantic Web
Rule-based policy and contract systems have rarely been studied in terms of
their software engineering properties. This is a serious omission, because in
rule-based policy or contract representation languages rules are being used as
a declarative programming language to formalize real-world decision logic and
create IS production systems upon. This paper adopts an SE methodology from
extreme programming, namely test driven development, and discusses how it can
be adapted to verification, validation and integrity testing (V&V&I) of policy
and contract specifications. Since, the test-driven approach focuses on the
behavioral aspects and the drawn conclusions instead of the structure of the
rule base and the causes of faults, it is independent of the complexity of the
rule language and the system under test and thus much easier to use and
understand for the rule engineer and the user.Comment: A.Paschke: Verification, Validation, Integrity of Rule Based Policies
and Contracts in the Semantic Web, 2nd International Semantic Web Policy
Workshop (SWPW'06), Nov. 5-9, 2006, Athens, GA, US
A User-based Visual Analytics Workflow for Exploratory Model Analysis
Many visual analytics systems allow users to interact with machine learning
models towards the goals of data exploration and insight generation on a given
dataset. However, in some situations, insights may be less important than the
production of an accurate predictive model for future use. In that case, users
are more interested in generating of diverse and robust predictive models,
verifying their performance on holdout data, and selecting the most suitable
model for their usage scenario. In this paper, we consider the concept of
Exploratory Model Analysis (EMA), which is defined as the process of
discovering and selecting relevant models that can be used to make predictions
on a data source. We delineate the differences between EMA and the well-known
term exploratory data analysis in terms of the desired outcome of the analytic
process: insights into the data or a set of deployable models. The
contributions of this work are a visual analytics system workflow for EMA, a
user study, and two use cases validating the effectiveness of the workflow. We
found that our system workflow enabled users to generate complex models, to
assess them for various qualities, and to select the most relevant model for
their task
AI in software engineering : current developments and future prospects
Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning method
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Building the graph of medicine from millions of clinical narratives.
Electronic health records (EHR) represent a rich and relatively untapped resource for characterizing the true nature of clinical practice and for quantifying the degree of inter-relatedness of medical entities such as drugs, diseases, procedures and devices. We provide a unique set of co-occurrence matrices, quantifying the pairwise mentions of 3 million terms mapped onto 1 million clinical concepts, calculated from the raw text of 20 million clinical notes spanning 19 years of data. Co-frequencies were computed by means of a parallelized annotation, hashing, and counting pipeline that was applied over clinical notes from Stanford Hospitals and Clinics. The co-occurrence matrix quantifies the relatedness among medical concepts which can serve as the basis for many statistical tests, and can be used to directly compute Bayesian conditional probabilities, association rules, as well as a range of test statistics such as relative risks and odds ratios. This dataset can be leveraged to quantitatively assess comorbidity, drug-drug, and drug-disease patterns for a range of clinical, epidemiological, and financial applications
Ontology-Based Users & Requests Clustering in Customer Service Management System
Customer Service Management is one of major business activities to better
serve company customers through the introduction of reliable processes and
procedures. Today this kind of activities is implemented through e-services to
directly involve customers into business processes. Traditionally Customer
Service Management involves application of data mining techniques to discover
usage patterns from the company knowledge memory. Hence grouping of
customers/requests to clusters is one of major technique to improve the level
of company customization. The goal of this paper is to present an efficient for
implementation approach for clustering users and their requests. The approach
uses ontology as knowledge representation model to improve the semantic
interoperability between units of the company and customers. Some fragments of
the approach tested in an industrial company are also presented in the paper.Comment: 15 pages, 4 figures, published in Lecture Notes in Computer Scienc
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