15,186 research outputs found
Social media mining for identification and exploration of health-related information from pregnant women
Widespread use of social media has led to the generation of substantial
amounts of information about individuals, including health-related information.
Social media provides the opportunity to study health-related information about
selected population groups who may be of interest for a particular study. In
this paper, we explore the possibility of utilizing social media to perform
targeted data collection and analysis from a particular population group --
pregnant women. We hypothesize that we can use social media to identify cohorts
of pregnant women and follow them over time to analyze crucial health-related
information. To identify potentially pregnant women, we employ simple
rule-based searches that attempt to detect pregnancy announcements with
moderate precision. To further filter out false positives and noise, we employ
a supervised classifier using a small number of hand-annotated data. We then
collect their posts over time to create longitudinal health timelines and
attempt to divide the timelines into different pregnancy trimesters. Finally,
we assess the usefulness of the timelines by performing a preliminary analysis
to estimate drug intake patterns of our cohort at different trimesters. Our
rule-based cohort identification technique collected 53,820 users over thirty
months from Twitter. Our pregnancy announcement classification technique
achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user
timelines. Analysis of the timelines revealed that pertinent health-related
information, such as drug-intake and adverse reactions can be mined from the
data. Our approach to using user timelines in this fashion has produced very
encouraging results and can be employed for other important tasks where
cohorts, for which health-related information may not be available from other
sources, are required to be followed over time to derive population-based
estimates.Comment: 9 page
Decision trees in epidemiological research
Background:
In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods.
Main text:
We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees.
Conclusions:
Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation
Locating previously unknown patterns in data-mining results: a dual data- and knowledge-mining method
BACKGROUND: Data mining can be utilized to automate analysis of substantial amounts of data produced in many organizations. However, data mining produces large numbers of rules and patterns, many of which are not useful. Existing methods for pruning uninteresting patterns have only begun to automate the knowledge acquisition step (which is required for subjective measures of interestingness), hence leaving a serious bottleneck. In this paper we propose a method for automatically acquiring knowledge to shorten the pattern list by locating the novel and interesting ones. METHODS: The dual-mining method is based on automatically comparing the strength of patterns mined from a database with the strength of equivalent patterns mined from a relevant knowledgebase. When these two estimates of pattern strength do not match, a high "surprise score" is assigned to the pattern, identifying the pattern as potentially interesting. The surprise score captures the degree of novelty or interestingness of the mined pattern. In addition, we show how to compute p values for each surprise score, thus filtering out noise and attaching statistical significance. RESULTS: We have implemented the dual-mining method using scripts written in Perl and R. We applied the method to a large patient database and a biomedical literature citation knowledgebase. The system estimated association scores for 50,000 patterns, composed of disease entities and lab results, by querying the database and the knowledgebase. It then computed the surprise scores by comparing the pairs of association scores. Finally, the system estimated statistical significance of the scores. CONCLUSION: The dual-mining method eliminates more than 90% of patterns with strong associations, thus identifying them as uninteresting. We found that the pruning of patterns using the surprise score matched the biomedical evidence in the 100 cases that were examined by hand. The method automates the acquisition of knowledge, thus reducing dependence on the knowledge elicited from human expert, which is usually a rate-limiting step
Knowledge Representation in Digital Agriculture: A Step Towards Standardised Model
In recent years, data science has evolved significantly. Data analysis and
mining processes become routines in all sectors of the economy where datasets
are available. Vast data repositories have been collected, curated, stored, and
used for extracting knowledge. And this is becoming commonplace. Subsequently,
we extract a large amount of knowledge, either directly from the data or
through experts in the given domain. The challenge now is how to exploit all
this large amount of knowledge that is previously known for efficient
decision-making processes. Until recently, much of the knowledge gained through
a number of years of research is stored in static knowledge bases or
ontologies, while more diverse and dynamic knowledge acquired from data mining
studies is not centrally and consistently managed. In this research, we propose
a novel model called ontology-based knowledge map to represent and store the
results (knowledge) of data mining in crop farming to build, maintain, and
enrich the process of knowledge discovery. The proposed model consists of six
main sets: concepts, attributes, relations, transformations, instances, and
states. This model is dynamic and facilitates the access, updates, and
exploitation of the knowledge at any time. This paper also proposes an
architecture for handling this knowledge-based model. The system architecture
includes knowledge modelling, extraction, assessment, publishing, and
exploitation. This system has been implemented and used in agriculture for crop
management and monitoring. It is proven to be very effective and promising for
its extension to other domains
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