Can NLP be helpful in health care industry ?

Health care is one of the industries which is way behind in successfully transforming health-related big data into meaningful data through which analytics can be performed.The main reason for this lagging behind is the availability of unstructured data. There is an increasing amount of usage of electronic health records for patients for current few years. Electronic health records consist of a variety of unstructured data like radiology images, pathology reports, patient feedback, physician notes, admission and discharge notes and other clinical reports. All of these unstructured data are used to perform analytics which will help healthcare payers and health systems.

Natural Language Processing is the best way to extract useful information from these unstructured data. For example, the clinician-generated narrative consists of patient's most accurate and complete picture of medical history which contains valuable information that can be missed if these narratives are in structured format.

Other tasks which can be performed by NLP in Healthcare industries are:-

Summarization of long narrative texts such as identifying key concepts and phrases present in a clinical note or academic journal.
Transferring data elements present in unstructured text to structured electronic health record.
Converting data that is readable by machine into natural language for reporting and other purposes.

Currently, the major focus in health-care industry is to use NLP for clinical decision support. IBM also started the investigation on how NLP and machine learning can help to tell whether a patient is suffering from heart disease or not.They realized that it's not only clinical data relevant to a disease that is sufficient for predicting heart failure but also the patient's social and behavioral factors that are recorded in clinical notes which are not in structural data of electronic health record.

Other uses of NLP in clinical decision support are as follows:-

In 2013, the Department of Veterans Affairs used NLP techniques to review around 2 billion Electronic health records documents for indications of Post-traumatic stress disorder, depression, and potential self-harm in veteran patients.
At the University of California Los Angeles, researchers analyzed electronic free text to flag patients with liver damage caused by cirrhosis.
Researchers from the University of Alabama found that NLP identification of reportable cancer cases was 22.6 percent more accurate and precise than a manual review of medical records.

Comments

NLP in Video Games

From the last few decades, NLP (Natural Language Processing) has obtained a high level of success in the field of Computer Science, Artificial Intelligence and Computational Logistics. NLP can also be used in video games, in fact, it is very interesting to use NLP in video games, as we can see games like Serious Games includes Communication aspects. In video games, the communication includes linguistic information that is passed either through spoken content or written content. Now the question is why and where can we use NLP in video games? There are some games that are related to pedagogy or teaching (Serious Games). So, NLP can be used in these games to achieve these objectives in the real sense. In other games, one can use the speech control using NLP so that the player can play the game by concentrating only on visuals rather on I/O. These things at last increases the realism of the game. Hence, this is the reason for using NLP in games. We ...

Word embeddings and an application in SMT

We all are aware of (not so) recent advancements in word representation, such as Word2Vec, GloVe etc. for various NLP tasks. Let's try to dig a little deeper of how they work, and why they are so helpful! The basics, what is a Word vector? We need a mathematical way of representing words so as to process them. We call this representation, a word vector. This representation can be as simple as a one-hot encoded vector having the size of the vocabulary. For ex, if we had 3 words in our vocabulary {man, woman, child}, we can generate word vectors in the following manner Man : {0, 0, 1} Woman : {0, 1, 0} Child : {1, 0, 0} Such an encoding cannot be used to for any meaningful comparisons, other than checking for equality. In vectors such as Word2Vec, a word is represented as a distribution over some dimensions. Each word is assigned some particular weight for each of the dimensions. Picking up the previous example, this time the vectors can be as following (assuming a 2 dime...

Discourse Analysis

NLP makes machine to understand human language but we are facing issues like word ambiguity, sarcastic sentiments analysis and many more. One of the issue is to predict correctly relation between words like " Patrick went to the club on last Friday. He met Richard ." Here, ' He' refers to 'Patrick'. This kind of issue makes Discourse analysis one of the important applications of Natural Language Processing. What is Discourse Analysis ? The word discourse in linguistic terms means language in use. Discourse analysis may be defined as the process of performing text or language analysis, which involves text interpretation and knowing the social interactions. Discourse analysis may involve dealing with morphemes, n-grams, tenses, verbal aspects, page layouts, and so on. It is often used to refer to the analysis of conversations or verbal discourse. It is useful for performing tasks, like A naphora Resolution (AR) , Named Entity Recognition (NE...

Amalgam

Search This Blog