Healthcare informatics focuses on the efficient and effective acquisition, management, and use of information in healthcare. Advancing health informatics has been declared a grand challenge by the National Academy of Engineering and is a major area of emphasis for agencies such as the Centers for Medicare and Medicaid Services. As such, it has been identified as an area of national need. Sample uses of AI in health informatics includes expert systems for decision support, machine learning and data mining to discover patterns across patients, image analysis to assist in diagnosis, and natural language processing to extract information from free text medical documents. The areas of interest for this track include healthcare decision support, medical image processing, machine learning and data mining in healthcare, processing and managing patient records, syndromic surveillance, drug discovery, and personalization of clinical care.

As artificial intelligence (AI) gains more momentum in the healthcare sector, CIOs' use of these technologies has expanded. And as a result, the industry is now moving quickly, crafting solutions to meet this growing demand. To this point, according to the findings of a recent Accenture report based on C-suite executive responses from more than 100 health organizations, AI is poised to become the new user interface (UI) in health IT. The report noted, "The growing role of AI in healthcare is moving beyond a back-end tool to the forefront of the consumer and clinician experience, becoming a new user interface that underpins the ways individuals transact and interact with systems. Emphasizing its growing importance of AI, more than four-fifths (84 percent) of healthcare executives surveyed as part of the research believe that AI will revolutionize the way they gain information from and interact with consumers, and nearly three-quarters (72 percent) of health organizations surveyed are already using virtual assistants to create better customer interactions."

Conventional text classification models make a bag-of-words assumption reducing text, fundamentally a sequence of words, into word occurrence counts per document. Recent algorithms such as word2vec and fastText are capable of learning semantic meaning and similarity between words in an entirely unsupervised manner using a contextual window and doing so much faster than previous methods. Each word is represented as a vector such that similar meaning words such as 'strong' and 'powerful' are in the same general Euclidian space. Open questions about these embeddings include their usefulness across classification tasks and the optimal set of documents to build the embeddings. In this work, we demonstrate the usefulness of embeddings for improving the state of the art in classification for our tasks and demonstrate that specific word embeddings built in the domain and for the tasks can improve performance over general word embeddings (learnt on news articles, Wikipedia or PubMed).

Prashant Natarajan Iyer (AKA "PN") is an analytics and data science professional based out of the Silicon Valley, CA. He is currently Director of Product Management for Healthcare products. His experience includes progressive & leadership roles in business strategy, product management, and customer happiness at eCredit.com, He is currently coauthoring HIMSS' next book on big data and machine learning for healthcare executives - along with Herb Smaltz PhD and John Frenzel MD. He is a huge fan of SEC college football, Australian Cattle Dogs, and the hysterically-dubbed original Iron Chef TV series.

Healthcare informatics focuses on the efficient and effective acquisition, management, and use of information in healthcare.

Introduction to the papers presented at the 2013 FLAIRS Conference Special Track on Artificial Intelligence in 
Healthcare Informatics.