Posts Tagged ‘healthcare’

Social Media for Health and Wellness 2.0

The Internet has surpassed physicians as the leading source of health information. With the advent of the social web, Health 2.0 is emerging as a strong segment with 34% of consumers using social resources such as blogs and forums to locate health information. Yet information overload leads to “search engine fatigue” that discourages users.

We advocate a consumer-centric approach to healthcare information access that increases engagement and improves health literacy.  Artificial Intelligence (AI) techniques can be used to support human effort, creating a new generation of “intelligent web” technologies. These technologies can combine the benefits of the “information web” (timely, relevant health information) with those of the “social web” (human interaction, support, comfort). Our vision is to promote well-being and prevention before illness, support and information during illness, and comfort to family and friends in a natural, social, yet private manner.

Invited talk at Humana Innovation Conference: Connect, Collaborate, Create (C3), Louisville, KY, September 23, 2011.

Open Social Learning Communities

With the advent of open education resources, social networking technologies and new pedagogies for online and blended learning, we are in the early stages of a significant disruption in current models of education. The disruption is fueled by a staggering growth in demand. It is estimated that there will be 100 million students qualified to enter universities over the next decade. To educate them, a major university would need to be created every week.

Universities have responded to this need with Open Education Resources—thousands of free, high quality courses, developed by hundreds of faculty, used by millions worldwide. Unfortunately, online courseware does not offer a supporting learning experience or the engagement needed to keep students motivated. Students read less when using e-textbooks; video lectures are boring; and retention and course completion rates are low.

Therein lies the core problem: How to engage a generation of learners who live on the Internet yet tune out of school, who seek interaction on Facebook yet find none on iTunes U, who need community yet are only offered content. We propose a new approach to this problem: open social learning communities, anchored with open content, providing an interactive online study group experience akin to sitting with study buddies on a world-wide campus quad.

This solution is enabled by state-of-the-art web technologies: really real-time collaboration technologies for a highly interactive experience; intelligent recommender systems to help learners connect with relevant content and other learners; mining and analytics to assess learner outcomes; and reputation techniques to establish social capital.  We will discuss these technologies and how they can be combined to address the problem of education in a manner that is highly scalable yet interactive and engaging.

This approach can be used for other types of learning communities. We will show an application to healthcare information access to help consumers learn about their healthcare questions and needs.

Keynote talk at SIPA Conference: Entrepreneurship—Idea Wave 3.0, Mountain View, CA, November 12, 2011.
Keynote talk at the International Conference on Web Intelligence, Mining and Semantics (WIMS-11), Sogndal, Norway, May 27, 2011.

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Intentional analysis of medical conversations for community engagement

With an explosion in the proliferation of user-generated content in communities, information overload is increasing and quality of readily available online content is deteriorating. There is an increasing need for intelligent systems that make use of implicit user-generated knowledge in communities for community engagement. We describe our approach based on modeling user utterances in communities to proactively target the community for exchange of questions and answers. We envision a system that automatically encourages user engagement and participation by routing relevant conversations to users based on individual and community activity levels.

In this paper, we analyze health forum conversations from WebMD, a popular health portal consumer site, and classify them in different acts of speech using Verbal Response Modes (VRM) theory. We describe our approach for modeling an intelligent community recommender to engage participants based on observations from our analysis.

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Intentional analysis of medical conversations for community engagement

by Saurav Sahay, Hua Ai, Ashwin Ram

FLAIRS-11 International Conference on Artificial Intelligence

Conversational Framework for Web Search and Recommendations

We introduce a Conversational Interaction framework as an innovative and natural approach to facilitate easier information access by combining web search and recommendations. This framework includes an intelligent information agent (Cobot) in the conversation to provide contextually relevant social and web search recommendations. Cobot supports the information discovery process by integrating web information retrieval along with proactive connections to relevant users who can participate in real-time conversations. We describe the conversational framework and report on some preliminary experiments in the system.

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Conversational Framework for Web Search and Recommendations

by Saurav Sahay, Ashwin Ram

ICCBR-10 Workshop on Reasoning from Experiences on the Web (WebCBR-10), Alessandria, Italy, 2010.

Collaborative Information Access: A Conversational Search Approach

Knowledge and user-generated content is proliferating on the web in scientific publications, information portals and online social media. This knowledge explosion has continued to outpace technological innovation in efficient information access technologies. In this paper, we describe methods and technologies for “Conversational Search” as an innovative solution to facilitate easier information access and reduce the information overload for users.

Conversational Search is an interactive and collaborative information finding interaction. The participants in this interaction engage in social conversations aided with an intelligent information agent (Cobot) that provides contextually relevant search recommendations. The collaborative and conversational search activity helps users make faster and more informed search and discovery. It also helps the agent learn about conversations with interactions and social feedback to make better recommendations. Conversational search leverages the social discovery process by integrating web information retrieval along with the social interactions.

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Collaborative Information Access: A Conversational Search Approach

by Saurav Sahay, Anu Venkatesh, Ashwin Ram

ICCBR-09 Workshop on Reasoning from Experiences on the Web (WebCBR-09), Seattle, July 2009

iReMedI – Intelligent Retrieval from Medical Information

Effective encoding of information is one of the keys to qualitative problem solving. Our aim is to explore Knowledge Representation techniques that capture meaningful word associations occurring in documents. We have developed iReMedI, a TCBR-based problem solving system as a prototype to demonstrate our idea. For representation we have used a combination of NLP and graph based techniques which we call as Shallow Syntactic Triples, Dependency Parses and Semantic Word Chains. To test their effectiveness we have developed retrieval techniques based on PageRank, Shortest Distance and Spreading Activation methods. The various algorithms discussed in the paper and the comparative analysis of their results provides us with useful insight for creating an effective problem solving and reasoning system.

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iReMedI – Intelligent Retrieval from Medical Information

by Saurav Sahay, Bharat Ravisekar, Anu Venkatesh, Sundaresan Venkatasubramanian, Priyanka Prabhu, Ashwin Ram

9th European Conference on Case-Based Reasoning (ECCBR-08), Trier, Germany

Discovering Semantic Biomedical Relations Utilizing The Web

To realize the vision of a Semantic Web for Life Sciences, discovering relations between resources is essential. It is very difficult to automatically extract relations from Web pages expressed in natural language formats. On the other hand, because of the explosive growth of information, it is difficult to manually extract the relations. In this paper we present techniques to automatically discover relations between biomedical resources from the Web. For this purpose we retrieve relevant information from Web Search engines and Pubmed database using various lexico-syntactic patterns as queries over SOAP web services. The patterns are initially handcrafted but can be progressively learnt. The extracted relations can be used to construct and augment ontologies and knowledge bases. Experiments are presented for general biomedical relation discovery and domain specific search to show the usefulness of our technique.

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Discovering Semantic Biomedical Relations utilizing the Web

by Saurav Sahay, Sougata Mukherjea, Eugene Agichtein, Ernie Garcia, Sham Navathe, Ashwin Ram

ACM Transactions on Knowledge Discovery from Data, 2(1):3, 2008

Semantic Annotation and Inference for Medical Knowledge Discovery

We describe our vision for a new generation medical knowledge annotation and acquisition system called SENTIENT-MD (Semantic Annotation and Inference for Medical Knowledge Discovery). Key aspects of our vision include deep Natural Language Processing techniques to abstract the text into a more semantically meaningful representation guided by domain ontology. In particular, we introduce a notion of semantic fitness to model an optimal level of abstract representation for a text fragment given a domain ontology. We apply this notion to appropriately condense and merge nodes in semantically annotated syntactic parse trees. These transformed semantically annotated trees are more amenable to analysis and inference for abstract knowledge discovery, such as for automatically inferring general medical rules for enhancing an expert system for nuclear cardiology. This work is a part of a long term research effort on continuously mining medical literature for automatic clinical decision support.

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Semantic Annotation and Inference for Medical Knowledge Discovery

by Saurav Sahay, Eugene Agichtein, Baoli Li, Ernie Garcia, Ashwin Ram

NSF Symposium on Next Generation of Data Mining (NGDM-07), Baltimore, MD, October 2007

Domain Ontology Construction from Biomedical Text

NLM’s Unified Medical Language System (UMLS) is a very large ontology of biomedical and health data. In order to be used effectively for knowledge processing, it needs to be customized to a specific domain. In this paper, we present techniques to automatically discover domain-specific concepts, discover relationships between these concepts, build a context map from these relationships, link these domain concepts with the best-matching concept identifiers in UMLS using our context map and UMLS concept trees, and finally assign categories to the discovered relationships. This specific domain ontology of terms and relationships using evidential information can serve as a basis for applications in analysis, reasoning and discovery of new relationships. We have automatically built an ontology for the Nuclear Cardiology domain as a testbed for our techniques.

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Domain Ontology Construction from Biomedical Text

by Saurav Sahay, Baoli Li, Ernie Garcia, Eugene Agichtein, Ashwin Ram

International Conference on Artificial Intelligence (ICAI-07), Las Vegas, NV, June 2007

Detecting Medical Rule Sentences with Semi-Automatically Derived Patterns: A Pilot Study

We propose a semi-supervised method to extract rule sentences from medical abstracts. Medical rules are sentences that give interesting and non-trivial relationship between medical entities. Mining such medical rules is important since the rules thus extracted can be used as inputs to an expert system or in many more other ways. The technique we suggest is based on paraphrasing a set of seed sentences and populating a pattern dictionary of paraphrases of rules. We match the patterns against the new abstract and rank the sentences.

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Detecting Medical Rule Sentences with Semi-Automatically Derived Patterns: A Pilot Study

by Shreekanth Karvaje, Bharat Ravisekar, Baoli Li, Ernie Garcia, Ashwin Ram

International Symposium on Bioinformatics Research and Applications ( ISBRA-07), Atlanta, GA, May 2007