Theses and Dissertations

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Subject: search.filters.subject.Biomedical

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    Biomedical information retrieval
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Purabia, Pooja R.; Majumder, Prasenjit
    It is well known that the volume of biomedical literature is growing exponentially and that scientists are being overwhelmed when they sift through the scope and diversity of this unstructured knowledge to find relevant information. TREC Precision Medicine 2017 is a track focusing on retrieving relevant scientific abstract and clinical trials from PubMed and Clinicaltrails.gov for cancer patients given their medical case. This report describes the system architecture for the TREC 2017 Precision Medicine Track. I explored query expansion techniques using wellknown broad knowledge sources such as Metamap and Entrez database. I used different pseudo relevance feedback technique like TF-IDF, BO1 and Local Context Analysis to retrieve relevant medical abstracts. I have used hidden aspects of topic like precision medicine and treatment aspect to improve the scores. I report infNDCG, R-Prec and P@10 scores.
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    Integrating semantics into biomedical information retrieval
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Thakrar, Fenny; Majumder, Prasenjit
    Integrating semantics into Biomedical Information Retrieval is concerned with studying the meaning of concepts and focusing on their relationships. We have used semantic document representation approach to applying domain-specific knowledge into the information retrieval system. Single and multi word concepts are extracted from the document using an external semantic structure UMLS Metathesaurus. Word sense disambiguation is performed on the extracted concepts to disambiguate different concept senses. And, the document is represented in the form of UMLS concepts. The documents and queries are represented in semantic space and fed to an information retrieval system to rank those documents, according to the given query. We have performed experiments on TREC 2014 CDS Task data and its 30 queries. Two types of retrieval techniques namely single word and multi word retrieval are experimented. The results obtained using conceptual information retrieval are compared with the results obtained using traditional term based retrieval. The conceptual IR approach proved better compared to term based IR system for the evaluation metrics MAP, P10 and RPrec. And, single word retrieval proved better compared to multi word retrieval technique for conceptual IR. Also, query expansion in conceptual IR system proved better compared to non query expanded conceptual IR system.
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    Query expansion in biomedical information retrieval
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Sankhavara, Jainisha; Majumder, Prasenjit
    Retrieving relevant information from biomedical documents is a new challenging task. Health related articles from the literature of biomedical and life sciences are a good source of knowledge for searching information relevant to a patient’s medical case report. Medical case reports describe patients’ medical condition i.e. medical history, current symptoms, tests performed, undergoing treatments etc. The articles related to medical case reports can be useful for clinicians to best care their patients. For example, a successful treatment described in an article for patients of a particular age group, having particular medical history and symptoms might be advisory to the patients having similar medical case report. This thesis focuses on applying query expansion techniques and fusing them for biomedical domain, especially while retrieving biomedical articles from the literature relevant to a particular case report. Along with the traditional query expansion techniques, query expansion using external medical knowledge is also carried out and compared with the state-of-the-art query expansion technique i.e. Incremental Blind Feedback. For the external knowledge source, UMLS Metathesaurus is used which is a network of medical related concepts. Text REtrieval Conference provided the data for this research as a part of Clinical Decision Support track in 2014 for which results of traditional query expansion techniques and fusion with manual feedback are reported. The fusion run gives consistent results for considered evaluation metrics. The results of Incremental Blind Feedback technique are comparable to the best of TREC CDS-2014. While considering the type of queries, the queries of type ’diagnosis’ and ’treatment’ performed better than that of ’test’.