The Qualitative way to analyse data Smart Indexing by i.know NV

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1 The Qualitative way to analyse data Smart Indexing by i.know NV i.know s Qualitative Analytics and Knowledge Streaming principles are based on a Smart Index of all your data. This Smart Indexing process uses a unique and qualitative methodology in order to track all possible relevant information and structure it into concepts and relations. TOP DOWN Traditional solutions are working with top down approaches. These approaches identify terms based on predefined thesauri and ontologies. They are lacking the precision enterprises require today. BOTTOM UP i.know s Smart Indexing automatically identifies all complex terms in data. The length of the terms doesn t matter. Smart Indexing discovers e.g. heparin binding growth factor as one meaningfull unit. Traditional solutions are limited. They only detect parts of complex terms. They have to pre-define the entire complex term in order to avoid fatal errors. But the process of predefining on such a high level is time-consuming and impossible for knowledge workers to keep up with since enterprise data is growing exponentially. The figure shows the difference between Top Down and Bottom Up approaches. The blue parts are meaningful units such as heparin binding growth factor. The green parts are the elements which top down approaches identify. It immediately becomes clear why top down approaches are less precise in enterprise data analyses: they identify in the blue heparin binding growth factor only heparin or binding or growth. What does Smart Indexing mean for enterprise data applications Navigate knowledge via a direct window by using meaning full word groups. Reveal the relations and links between your data. Speed up the creation of domain specific ontologies by mapping the right standard codes with high precision. Distribute relevant knowledge to employees with different profiles and classify knowledge according to the topics of their interest. Use the associative power of Smart Indexing to allow employees to find their way to related documents, related items or related entities. I.KNOW ZOOMS IN ON YOUR QUALITATIVE DATA. BECAUSE IT S QUALITY THAT MATTERS.

2 SEMANTIC MIDDLEWARE IN LIFE SCIENCES & HEALTHCARE with i.know NV In Life Sciences and Healthcare, semantic accuracy is often life critical. i.know s technology interacts with and feeds all sorts of applications, such as Electronic Health Records, leading to Smartly Integrated systems. i.know s Engine is effective and flexible and acts perfectly as semantic middleware, guaranteeing the right, fast and scalable solutions: SEMANTIC SEARCH OF LARGE DATABASES OF ANY KIND AUTOMATIC PROBLEM LIST GENERATION AUTOMATIC ENCODING OF MEDICAL DOCUMENTS IMMEDIATE RDF CONVERSION ENRICHMENT OF KNOWLEDGE THROUGH MASH-UPS Search file Problem Lists file i.know Engine Encoding RDF EPR system Mash-Ups I.KNOW S ENGINE PROVIDES THE RIGHT SEMANTIC MIDDLEWARE FOR LIFE SCIENCES AND HEALTHCARE.

3 Life-saving knowledge on the spot by i.know NV SITUATION Today, physicians dispose of huge amounts of information, which could be an enormous source of knowledge. However, they do not succeed to integrate the evidence from scientific material with clinical experience (EBM). SEARCH WITH I.KNOW i.know developed a Web based interface which offers physicians intuitive access to medical information sources such as the National Library of Medicine s PubMed which speeds up search for medical evidence. Physicians need a knowledge window on objective medical literature. In this way they will dispose of medical information which is: Up-to-date Easily accessible Highly precise CURRENT APPROACH Since current medical search strategies do not detect all meaningful units such as chronic recurrent multifocal osteomyelitis a lot of important evidence gets lost in the haystack of medical data. Physicians though, need a precise, and thus a sufficient approach to medical evidence. I.KNOW i.know automatically extracts all meaningful elements for 100%. Highly complex terms such as human recombinant cyp3a4, their synonyms and spelling variants are detected in medical information. A clear navigation structure with suggestions (at the right) guides physicians to the right specialized terms. Certainly in the medical field, this feature yields a lot of advantages. Knowledge Window clearly indicates the semantic difference between cyp3a4 activity and cyp3a4 gene. A protein activity is something completely different from a gene. I.KNOW IMPROVES HEALTHCARE INFORMATION i.know s applications improve the quality of care by constructing highly precise access to medical evidence (EBM) and by facilitating its maintenance. i.know defines the right word meaning and indicates the semantic difference between complex terms. In this way, i.know allows physicians to retrieve all evidence based medicine information and to use the meaning of that information accurately, automatically and with unseen precision. The same highly precise application obviously can be tuned to other medical application domains: adverse drug reporting systems, clinical decision-support systems and other medical information sources such as Cochrane Library, Embase, Ovid and, Electronic Patient Records (EPR) can automatically and with high precision be integrated with all these sources. I.KNOW ENABLES A FAST, FLEXIBLE AND ACCURATE RETRIEVAL OF MEDICAL DATA.

4 DETECT CRUCIAL MEDICAL DATA with i.know NV THE CHALLENGE Today, physicians dispose of huge amounts of information, which could be an enormous source of knowledge. However, they do not always succeed to find the needle in the haystack. THE SOLUTION i.know developed an interface which offers physicians intuitive access to and insight in patient notes. Electronic Patient Record Systems (EPR s) contain vast amounts of patient notes, but are too cluttered too keep track of the situation. Physicians need an intelligent view on patient notes, with an immediate detection of relevant terms and critical problems. CLASSICAL APPROACH Since current medical search and analysis strategies do not detect all meaningful units such as chronic recurrent multifocal osteomyelitis, a lot of important evidence gets lost in the haystack of medical data. Physicians though, need a precise and thus a sufficient approach and analysis of medical records. THE I.KNOW WAY i.know automatically extracts all meaningful elements for 100%. Highly complex terms such as human recombinant cyp3a4, their synonyms, negations and spelling variants are detected in medical information. By Smart Indexing, Matching and Mapping, i.know automatically produces a problem list with the relevant, right and crucial data appearing in patient notes. The patient note is automatically analysed, and the relevant and significant terms, negations and problems are highlighted in the text itself. An automatically generated problem list appears beneath the patient note, allowing for quick and accurate response. I.KNOW IMPROVES HEALTHCARE INFORMATION i.know s applications improve the quality of care by constructing highly precise access to medical evidence (EBM) and by facilitating its maintenance. i.know defines the right word meaning and indicates the semantic difference between complex terms. In this way, i.know allows physicians to retrieve all medical information and to use the meaning of that information accurately, automatically and with unseen precision. The same highly precise application obviously can be tuned to other medical application domains: adverse drug reporting systems, clinical decision-support systems and other medical information sources can automatically and with high precision be integrated with all these sources. I.KNOW KEEPS TRACK OF ALL RELEVANT MEDICAL DATA.

5 AUTOMATIC ENCODING with i.know NV (0) (0) THE SITUATION Today, critical patient information is difficult to access. Current tools have three major problems. They are not up-to-date not consistent not integrated AUTOMATIC ENCODING In collaboration with ebiz@work i.know developed an end application for billing information. Since Top Down approaches do not detect all meaningful units such as chronic recurrent multifocal osteomyelitis, a lot of medical specialists have to keep analyzing the data. Healthcare solutions should automatically convert text into consistent medical terminology and codes (like ICD9) with high precision. TOP DOWN Pre-defined taxonomies, controlled vocabularies or ontologies extract diagnosis, disease or drugs from patient notes and convert them to codes. This is an imprecise, and thus an insufficient approach. I.KNOW Firstly, i.know automatically extracts all meaningful elements (also highly complex terms such as chronic proximal diabetic neuropathic foot ulcer ) from medical information. Secondly, i.know defines the right context of medical information. Hypertension e.g. can be described in the context of the disease hypertension, as the result of an adverse drug effect or as a symptom (etiology). Thanks to a highly precise definition and disambiguation of medical data, i.know s applications convert the data to the right codes. i.know automates the process. All meaningful units are highlighted and automatically converted into the matching code(s) which eliminates medical errors caused by misinterpretation of data. Hospitals get a flexible and consistent system for dealing with their data. I.KNOW IMPROVES HEALTHCARE INFORMATION i.know s applications improve the quality of care by constructing highly precise access to medical evidence (EBM) and by facilitating its maintenance. i.know defines the right word meaning and indicates the semantic difference between complex terms. In this way, i.know allows physicians to retrieve all medical information and to use the meaning of that information accurately, automatically and with unseen precision. The same highly precise application obviously can be tuned to other medical application domains: adverse drug reporting systems, clinical decision-support systems and other medical information sources can automatically and with high precision be integrated with all these sources. I.KNOW FACILITATES CONSISTENT, ACCURATE AND AUTOMATIC ENCODING OF MEDICAL DATA.

6 Semantic Web - RDF by i.know NV Today s Web contains a huge collection of information merely designed for human consumption. Since machines do not understand the meaning behind texts, they are limited to passively assist humans in retrieving data by blindly representing information. Nevertheless, machines could massively improve information processes if humans could help them to understand concepts such as persons, events, places, etc. and the way in which these concepts are related to each other. We call this Semantic Intelligence. i.know NV developed a unique technology which automatically transforms texts into machine understandable languages (XML, RDF, OWL). The Information Forensics (IF) technology allows computers and humans to communicate. Hence, computers actually get to know what the information means. They autonomously identify all meaningful concepts in the gathered data sources on-the-fly while reducing the need of human intervention. They find patterns in data sources, resulting in new insights for end-users. RAW DATA MADE MORE PROGRAMMABLE FOR INSIGHT MACHINE UNDERSTANDABLE Information Forensics (IF), i.know s patented technology, consists of a unique Smart Indexing component to automatically convert unstructured text into RDF schemes. Smart Indexing (SI) automatically structures text with unseen precision- into relations and concepts (or concept clusters), regardless of their length, synonyms or spelling variants. Moreover, concepts such as persons, events, etc. are linked together in real-time via these relations. The concepts and concept clusters generated in the XMLformat can be the semantically relevant start for expanding existing databases and can be automatically expressed in Semantic Web formats such as RDF. EXAMPLE Raw PubMed article IF concepts in green - IF relations in red One sentence in an automatically generated RDF scheme: FROM DATA TO INSIGHT It is clear that IF is the missing link to combine unstructured, human understandable texts with formal, machine understandable languages such as ontologies and RDF schemes. In other words, IF enables automatic integration with Semantic Web Services such as RDF aware agents. Moreover, computers become able to reason upon the information by making data query-ready for specific languages (RDFQL, SPARQL). Concepts in purple Relations in red arrows Mapped Gene Ontology in green I.KNOW ENABLES SEMANTIC WEB APPLICATIONS TO AUTOMATICALLY TRANSFORM RAW TEXTS INTO W3C STANDARDS, ENABLING COMPUTERS TO REASON UPON DATA.

7 Semantic Web - Mashups by i.know NV DATA ANALYSIS Today s knowledge society is based on piles of information, almost exponentially growing every day. Wikipedia, YouTube, GoogleMaps, Netvibes and blogging or tagging initiatives 2009 are all i.know user interactive N. (Web2.0) sites which offer the possibility to dispose of information created by and for its users. A serious problem is rising, though. The abundancy of information leads to spamming (non) interested users by pushing information which most of the time is not even relevant to them. Since information aggregation is no longer an issue today, we have to focus on combining information sources in a useful and intelligent way. The success of added value services depends on the quality of the data analysis. i.know connects and links incoming information with articles which provide on their turn relevant background information. In that respect, an exact, fast and automatic data analysis forms the crucial starting point. In the upper example, Arch Biochem Biophysics e.g. is one unit as such, and should lead to information about the entire term and not to irrelevant particles as Arch. i.know s Information Forensics (IF) Technology offers the means to that end. I.KNOW MASH-UPS The above given sample text is one of the many millions of public PubMed articles. i.know used in a first step its IF technology to identify not-predefined meaningful terms (Arch Biochem Biophysics, Galveston, CYP3A4, bromocriptine). In a second phase i.know mapped these automatically detected meaningful units immediately to other publicly available information sources, thus adding extra background information. In this example i.know mapped the gene CYP3A4 automatically with the renown UniProt gene database. That way, physicians using PubMed are immediately linked through to the gene database where they can gather interesting extra information for their diagnosis. Another detected meaningful term in the PubMed abstract is Arch Biochem Biophysics, a highly recommended scientific magazine. i.know detected the magazine and guides physicians in one click to the magzine s database. A final example is Galveston which i.know, after having identified, mapped with Google Maps. Physicians immediately see the exact location of the university of Texas medical branch i.know NV. All products and brand names are trademarks and registered trademarks of their respective companies.

8 Pharmacovigilance by i.know NV Life scientists, pharmacists and (inter)national supervising organisations are facing major challenges nowadays. Every day, numerous medicines and treatments are invented, implemented, tested and described in high-level literature as well as in concrete patient notes and records. By consequence, clinicians and pharmacists have to cope with a vast pile of knowledge scattered over various and heterogeneous documents. Gaining a perfect insight into this scattered knowledge is a mission impossible for humans alone; therefore, several (semi-) automatic data mining tools are designed in order to assist the human researcher in extracting the right information out of the knowledge pile, but often they are lacking the semantic precision pharmacovigilants require. i.know nv combines linguistics and semantics in its software tools and applications and offers a quite revolutionary solution for these pharmacovigilance needs. i.know has developed for EMEA, the European Medicines Agency, an innovative tool for dealing with their gigantic information load. SOLUTION Starting from this semantic accuracy, i.know offers the right perspective for all challenges faced by pharmacovigilants: Search: i.know s Knowledge Window represents all relevant knowledge in a well-organized way Duplicates and version control: i.know automatically allows for an internal comparison of similar documents An orderly surview on a gigantic data pile, by a clear and orderly representation of key semantic elements A correct and precise dealing with negations, in order that the right information is not overlooked Real knowledge extraction out of a large pile of data REPRESENTATION The Patient Note/ Individual Case Safety Report (ICSR) is automatically analysed, and the relevant and significant terms, negations and problems are highlighted in the text itself. i.know will detect the relevant medical terms and will intelligently deal with complex negations and nuanced diagnostics, resulting in a key-concept problem list beneath. The pharmacovigilance challenges as to bringing information to the surface regarding medical history onset of action re-challenge and de-challenge etc. can be overcome by this approach. Of course, this interface can be adapted for the end user to every possible need. The highly flexible XML output of i.know s Smart Indexing Engine guarantees full integration with other applications. I.KNOW S KNOWLEDGE WINDOW AUTOMATICALLY BRINGS RELATED ITEMS AND CONTEXTUAL PATHWAYS TO THE SURFACE, IN ORDER TO FACILITATE AN INTUITIVE AND QUALITATIVE NAVIGATION THROUGH AND ANALYSIS OF ANY INFORMATION PILE.