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
BOTTOM UP
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.
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
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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.
SEMANTIC MIDDLEWARE
IN LIFE SCIENCES & HEALTHCARE
with i.Know NV
[email protected] -
+32 (0)11 26 89 35 -
+32 (0)11 26 89 99 - www.iknow.be
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:
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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
S ear c h
file
Problem Lists
file
i.Know Engine
E n c od i n g
RDF
EPR
system
Mash-Ups
I.KNOW’S ENGINE PROVIDES THE RIGHT
SEMANTIC MIDDLEWARE
FOR LIFE SCIENCES AND HEALTHCARE.
Life-saving knowledge
on the spot
by i.Know NV
[email protected] -
+32 (0)11 26 89 35 -
+32 (0)11 26 89 99 - www.iknow.be
SITUATION
SEARCH WITH I.KNOW
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).
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:
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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.
DETECT CRUCIAL MEDICAL DATA
with i.Know NV
[email protected] -
+32 (0)11 26 89 35 -
+32 (0)11 26 89 99 - www.iknow.be
THE CHALLENGE
THE SOLUTION
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.
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.
AUTOMATIC ENCODING
with i.Know NV
[email protected] -
+32 (0)11 26 89 35 www.iknow.be
+32 (0)11 26 89 99 -
THE SITUATION
AUTOMATIC ENCODING
Today, critical patient information is difficult to access.
Current tools have three major problems. They are
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.
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not up-to-date
not consistent
not integrated
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.
Semantic Web - RDF
by i.Know NV
[email protected] -
+32 (0)11 26 89 35 -
+32 (0)11 26 89 99 - www.iknow.be
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
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MADE MORE PROGRAMMABLE
FOR INSIGHT
MACHINE UNDERSTANDABLE
EXAMPLE
Information Forensics (IF), i.Know’s patented technology,
consists of a unique Smart Indexing component to
automatically convert unstructured text into RDF schemes.
Raw PubMed article
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.
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).
I.KNOW ENABLES SEMANTIC WEB APPLICATIONS TO
AUTOMATICALLY TRANSFORM RAW TEXTS INTO W3C
STANDARDS , ENABLING COMPUTERS TO REASON UPON DATA.
Concepts in purple
Relations in red arrows
Mapped Gene Ontology in green
Semantic Web - Mashups
by i.Know NV
[email protected] -
+32 (0)11 26 89 35 -
+32 (0)11 26 89 99 - www.iknow.be
DATA ANALYSIS
I.KNOW MASH-UPS
Today’s knowledge society is based on piles of information,
almost exponentially growing every day. Wikipedia,
YouTube, GoogleMaps, Netvibes and blogging or tagging
initiatives
are alli.Know
user interactive
(Web2.0) sites which offer
©2009
N.
the possibility to dispose of information created by and for
its users.
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).
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.
©2005-2009 i.Know NV.
All products and brand names are trademarks and
registered trademarks of their respective companies.
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.
Pharmacovigilance
by i.Know NV
[email protected] -
+32 (0)11 26 89 35 -
+32 (0)11 26 89 99 - www.iknow.be
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
REPRESENTATION
Starting from this semantic accuracy, i.Know offers the right
perspective for all challenges faced by pharmacovigilants:
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.
• 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
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.
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.
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.