Systematic Literature Review
A health care organization must find publications relevant to a particular case with complex biomedical & socio-demographic criteria.
In order to populate treatment efficiency templates from clinical trials, we must collect data across a varying array of people of specific demographics and medical problems, the treatments that they underwent, and the outcome of those treatments. In typical document retrieval, the more specific your search need, the more general your search results are.
The Lymba Solution
CHiPS™ for Health Care: Complex search queries made simple.
Solution
Collaborative High Precision Search (CHiPS™) - Lymba’s intelligent search system - allows to formulate long complex queries in plain English and takes into account synonyms and inference to provide relevant results. CHiPS™ uses K-Extractor™ for deep semantic analysis of biomedical publications, extracting important medical and socio-demographic concepts as well as relations between them. The search query undergoes the same semantic analysis to recognize expressed criteria. The criteria are semantically matched to information in the publications to yield high precision results.
Input
Unstructured data: The last 10 years of MEDLINE®/PubMed®'s biomedical journal citation data, which comprises over 25 million articles from clinical trials, life science journals, and online books.
Query: Lymba provides an interactive user interface as well as an Application Program Interface (API) to allow the customer to perform detailed natural language queries, or to use an entire document as a query.
Sorted list of search results based on semantic similarity. Each result is paired with a result profile which allows you to, at a glance, determine the most relevant documents and why they are relevant before opening.
Output
Key Features
Ontologies: K-Extractor™ recognizes concepts from existing medical lexical resources including UMLS Metathesaurus, SNOMED-CT, Medline (health signs, symptoms, diseases, medication, etc) as well as socio-demographic characteristics and relations (race, gender, age, nationality, insurance, financial status, education level) and semantic relations between these concepts.
Big Data: Extracted information is efficiently stored to allow fast access. For this use case the system supports real-time querying across several multimillion document collections all on a single PC.
Document Comparison View: CHiPS™ doesn't just surface sorted results, it can also explain the semantic matches it finds.