Papers by Swarupananda Bissoyi
2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), 2012
A novel concept of Disease Based Temporal Score (DT-Score) is introduced to efficiently represent... more A novel concept of Disease Based Temporal Score (DT-Score) is introduced to efficiently represent periodic laboratory test data. Through this score, temporal state of an organ can be represented by summarizing periodic laboratory test data. The score can be used to indicate early trend for chronic abnormalities and thus results in an effective wellness measure. Many of these chronic abnormalities have a late manifestation and are major contributors for healthcare cost and mortality. Resources of Unified Medical Language System (UMLS) are introduced for automatic generation of relational tree between laboratory test, disease and organs with relative rank. Doctor's annotations are used to create reference score and data mining techniques are employed in deriving a mathematical model for estimating the DT-Score. A novel human body based summarization is employed for an intuitive view of the DT-Score and resultant temporal state of the associated organ. The proposed method enables an efficient temporal summarization of high volume of laboratory data and eventually reduces the cognitive load on physician. This method has potential to impact larger population as this can be effectively built over low cost regular laboratory test.
Estimating personalized risk ranking using laboratory test and medical knowledge (UMLS)
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
In this paper, we introduce a Concept Graph Engine (CG-Engine) that generates patient specific pe... more In this paper, we introduce a Concept Graph Engine (CG-Engine) that generates patient specific personalized disease ranking based on the laboratory test data. CG-Engine uses the Unified Medical Language System database as medical knowledge base. The CG-Engine consists of two concepts namely, a concept graph and its attributes. The concept graph is a two level tree that starts at a laboratory test root node and ends at a disease node. The attributes of concept graph are: Relation types, Semantic types, Number of Sources and Symmetric Information between nodes. These attributes are used to compute the weight between laboratory tests and diseases. The personalized disease ranking is created by aggregating the weights of all the paths connecting between a particular disease and contributing abnormal laboratory tests. The clinical application of CG-Engine improves physician's throughput as it provides the snapshot view of abnormal laboratory tests as well as a personalized disease ranking.
Knowledge acquisition under uncertainty
using rough set theory was first stated as a concept
and ... more Knowledge acquisition under uncertainty
using rough set theory was first stated as a concept
and was introduced by Z.Pawlak in1981. A
collection of rules is acquired, on the basis of
information stored in a data base-like system, called
an information system. Uncertainty implies
inconsistencies, which are taken into account, so that
the produced are categorized into certain and
possible with the help of rough set theory. The
approach presented belongs to the class of methods
of learning from examples. The taxonomy of all
possible expert classifications, based on rough set
theory, is also established. It is shown that some
classifications are theoretically (and, therefore, in
practice) forbidden. For a set of conditions of the
information system, and a given action of an expert,
lower and upper approximations of a classification,
generated are computed in a straightforward way,
using their simple definitions. Such approximations
are the basis of rough set theory. From these
approximations, certain and possible rules may be
determined. Certain rules have been propagated
separately during the inference process, producing
new certain rules. Similarly, possible rules are likely
to propagate in a parallel way. Example on the
basic of knowledge Acquisition has been discussed in
brief.
Knowledge engineering refers to the
building, maintaining and development of knowledgebased syste... more Knowledge engineering refers to the
building, maintaining and development of knowledgebased systems. It has a great deal in common with
softare engineering and is related to many computer
science domains such as artifiial intelligence, database,
data mining, expert systems, decision support system and
geographic information system. The main motto of rough
sets is: "Let the data speak for themselves". Rough sets
are based theory of sets. Main applications of rough sets
theory are attribute reduction, rule generation and
prediction. This article outlines conceptualization and
implementation of an intelligent system capable of
extracting knowledge from databases.
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Papers by Swarupananda Bissoyi
using rough set theory was first stated as a concept
and was introduced by Z.Pawlak in1981. A
collection of rules is acquired, on the basis of
information stored in a data base-like system, called
an information system. Uncertainty implies
inconsistencies, which are taken into account, so that
the produced are categorized into certain and
possible with the help of rough set theory. The
approach presented belongs to the class of methods
of learning from examples. The taxonomy of all
possible expert classifications, based on rough set
theory, is also established. It is shown that some
classifications are theoretically (and, therefore, in
practice) forbidden. For a set of conditions of the
information system, and a given action of an expert,
lower and upper approximations of a classification,
generated are computed in a straightforward way,
using their simple definitions. Such approximations
are the basis of rough set theory. From these
approximations, certain and possible rules may be
determined. Certain rules have been propagated
separately during the inference process, producing
new certain rules. Similarly, possible rules are likely
to propagate in a parallel way. Example on the
basic of knowledge Acquisition has been discussed in
brief.
building, maintaining and development of knowledgebased systems. It has a great deal in common with
softare engineering and is related to many computer
science domains such as artifiial intelligence, database,
data mining, expert systems, decision support system and
geographic information system. The main motto of rough
sets is: "Let the data speak for themselves". Rough sets
are based theory of sets. Main applications of rough sets
theory are attribute reduction, rule generation and
prediction. This article outlines conceptualization and
implementation of an intelligent system capable of
extracting knowledge from databases.