20-10-2012, 04:32 PM
A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts
A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts.doc (Size: 148 KB / Downloads: 81)
ABSTRACT
The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision support, medical imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient management care.
ML is envisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a better, more efficient medical care. This paper describes a ML-based methodology for building an application that is capable of identifying and disseminating healthcare information.
It extracts sentences from published medical papers that mention diseases and treatments, and identifies semantic relations that exist between diseases and treatments.
Our evaluation results for these tasks show that the proposed methodology obtains reliable outcomes that could be integrated in an application to be used in the medical care domain. The potential value of this paper stands in the ML settings that we propose and in the fact that we outperform previous results on the same data set.
Proposed System:
The problems that have been discussed can be removed with the help of machine learning approaches along with the active learning approach. that machine learning can be of three types, by employing the semi structured machine-learning approach to the focused crawlers the system performance can comparatively be improved. Semi-supervised learning is a goal-directed activity, which can be precisely evaluated. In the web context our training data is a small set of labeled documents.
The label is document class, and our goal is to guess the label of an unseen document. In this category we review learning from labeled and unlabeled documents. In some semi-supervised approaches, a learner agent learns from interaction with a dynamic environment. In these environments, providing a set of training data for the agent is very difficult or even impossible, because of the dynamics inherent in the environment and correspondingly huge number of states and actions. One requirement of this model is a measure of the goodness of action that the agent takes in a state.
Machine Learning:
In this module, the upload files are to be learned by the machine (computer). Machine learning is the study of how to make computers learn; the goal is to make computers improve their performance through experience.
Machine learning approaches in Information Retrieval is the ability of a machine to improve its performance based on previous results. Machine learning is an area of artificial intelligence concerned with the development of techniques, which allow computers to "learn". More specifically, machine learning is a method for creating computer programs by the analysis of data sets.
Search Engine:
In this module, the search engine is designed to search for information on the databases like World Wide Web. The search results are generally presented in a list of results often referred to as SERPS, or "search engine results pages".
The information may consist of web pages, images, information and other types of files. Some search engines also mine data available in databases or open directories. Unlike web directories, which are maintained only by human editors, search engines also maintain real-time information by running an algorithm on a web crawler.