17,318 research outputs found
Triaging Content Severity in Online Mental Health Forums
Mental health forums are online communities where people express their issues
and seek help from moderators and other users. In such forums, there are often
posts with severe content indicating that the user is in acute distress and
there is a risk of attempted self-harm. Moderators need to respond to these
severe posts in a timely manner to prevent potential self-harm. However, the
large volume of daily posted content makes it difficult for the moderators to
locate and respond to these critical posts. We present a framework for triaging
user content into four severity categories which are defined based on
indications of self-harm ideation. Our models are based on a feature-rich
classification framework which includes lexical, psycholinguistic, contextual
and topic modeling features. Our approaches improve the state of the art in
triaging the content severity in mental health forums by large margins (up to
17% improvement over the F-1 scores). Using the proposed model, we analyze the
mental state of users and we show that overall, long-term users of the forum
demonstrate a decreased severity of risk over time. Our analysis on the
interaction of the moderators with the users further indicates that without an
automatic way to identify critical content, it is indeed challenging for the
moderators to provide timely response to the users in need.Comment: Accepted for publication in Journal of the Association for
Information Science and Technology (2017
A survey on utilization of data mining approaches for dermatological (skin) diseases prediction
Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data
Discovering human activities from binary data in smart homes
With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual’s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods
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