Please , provide free source code identifying disease treatment relations in short texts

SYNOPSIS:

The Machine Learning field has gained its thrust in almost any domain of research and just recently has become a reliable tool in the medical domain. The experiential 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, well-organized medical care. It 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.

INTRODUCTION:

People care deeply about their health and want to be, now more than ever, in charge of their health and healthcare. Life is more hectic than has ever been, the medicine that is practiced today is an Evidence-Based Medicine in which medical expertise is not only based on years of practice but on the latest discoveries as well. Tools that can help us manage and better keep track of our health such as Google Health1 and Microsoft HealthVault2 are reasons and facts that make people more powerful when it comes to healthcare knowledge and management. The traditional healthcare system is also becoming one that embraces the Internet and the electronic world. Electronic Health Records (hereafter, EHR) are becoming the standard in the healthcare domain.

Health information recording and clinical data repositories: immediate access to patient diagnoses, allergies, and lab test results that enable better and time-efficient medical decisions;Medication management: rapid access to information regarding potential adverse drug reactions, immunizations, supplies, etc;

Decision support: the ability to capture and use quality medical data for decisions in the workflow of healthcare; andObtain treatments that are tailored to specific health needs: rapid access to information that is focused on certain topics.

Our objective for this work is to show what Natural Language Processing (NLP) and Machine Learning (ML) techniques what representation of information and what classification algorithms are suitable to use for identifying and classifying relevant medical information in short texts.

EXISTING SYSTEM:

The traditional healthcare system is also becoming one that hug the Internet and the electronic world. Electronic Health Records (EHR) is becoming the standard in the healthcare domain. Researches and studies show that the potential benefits of having an EHR system are:

Health information recording and clinical data repositories immediate access to patient diagnoses, allergies, and lab test results that enable better and time-efficient medical decisions;

Medication management rapid access to information regarding potential adverse drug reactions, immunizations, supplies, etc;

Decision support the ability to capture and use quality medical data for decisions in the workflow of healthcare; and Obtain treatments that are tailored to specific health needs—rapid access to information that is focused on certain topics.

DISADVANTAGE:

In order to embrace the views that the EHR system has, we need better, faster, and more reliable access to information.

All research discoveries come and enter the repository at high rate, making the process of identifying and disseminating reliable information a very difficult task.

PROPOSED SYSTEM:

The propose system approach, this work is to show what Natural Language Processing (NLP) and Machine Learning (ML) techniques what demonstration of information and what classification algorithms are suitable to use for identifying and classifying relevant medical information in short texts. We recognize the fact that tools able of identifying reliable information in the medical domain stand as construction blocks for a healthcare system that is up-to-date with the latest discoveries. In this examine, we focus on diseases and treatment information, and the relation that exists between these two entities. The approach used to solve the two proposed tasks is based on NLP and ML techniques. In a standard supervised ML setting, a training set and a test set are required. The training set is used to train the ML algorithm and the test set to test its performance.

ADVANTAGE:

The advantage that if a feature appears more than once in a sentence, this means that it is important and the frequency value representation will capture the feature’s value will be greater than that of other features.