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001 AAI3728239
005 20181030085013.5
006 m o u
007 cr mn||||a|a||
008 181030s2015 xx sbm 000 0 eng d
020 9781339136318
035 (MiAaPQ)AAI3728239
035 (MiAaPQ)umn:16425
040 MiAaPQ|beng|cMiAaPQ|dNTU
100 1 Sarkar, Chandrima
245 10 Improving Predictive Modeling in High Dimensional,
Heterogeneous and Sparse Health Care Data
264 0 |c2015
300 1 online resource (140 pages)
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
500 Source: Dissertation Abstracts International, Volume: 77-
03(E), Section: B
500 Advisers: Jaideep Srivastava; Sarah Cooley
502 Thesis (Ph.D.)--University of Minnesota, 2015
504 Includes bibliographical references
520 In the past few decades predictive modeling has emerged as
an important tool for exploratory data analysis and
decision making in health care. Predictive modeling is a
commonly used statistical and data mining technique that
works by analyzing historical and current data and
generating a model to help predict future outcomes. It
gives us the power to discover hidden relationships in
volumes of data and use those insights to confidently
predict the outcome of future events and interactions. In
health care, complex models can be created to combine
patient information like demographic and clinical
information from care providers, in order to predict and
improve model accuracy. Predictive modeling in health care
seeks out subtle data patterns to enhance decision making
such as care providers can recommend prescription drugs
and services based on patient profile
520 Although all predictive techniques have different
strengths and weaknesses, model accuracy is mostly
dependent on the raw input data with various features used
to train a predictive model. Model building often requires
data pre-processing in order to reduce the impact of the
skewed property of the data or outliers. This helps by
significantly improving performance. From hundreds of
available raw data fields, a subset is selected and fields
are pre-processed before being presented to a predictive
modeling technique. For example, there can be thousands of
variables consisting of genetic, clinical and demographic
information for different groups of patients. Therefore
detecting significant variables for a particular group of
patient can enhance model accuracy. Hence, the secret
behind a good predictive model often times depends on good
pre-processing and more so than the technique used to
train the model
520 While the above responsibilities of an effective and
efficient data pre-processing mechanism and its usage with
predictive modeling in health care data are better
understood, three key challenges were identified that
faces this data pre-processing task. These include, 1)
High dimensionality: The challenge of high-dimensionality
arises in diverse fields, ranging from health care and
computational biology to financial engineering and risk
management. This work identifies that there is no single
feature selection strategy that is robust towards
different families of classification or prediction
algorithm. The existing feature selection techniques
produce different results with different predictive
models. This can be a problem when deciding about the best
predictive model to use while working with real high
dimensional health care data and especially without domain
experts
520 2) Heterogeneity in the data and data redundancy: Most of
the real world data is heterogeneous in nature, i.e. the
population consists of overlapping homogeneous groups. In
health care, Electronic Health Records (EHR) data consists
of diverse groups of patients with a wide range of diverse
health conditions. This thesis identifies that predictive
modeling with a single learning model over heterogeneous
data can result in inconclusive results and ineffective
explanation of an outcome. Therefore, it has been proposed
in this thesis that, there is a need for data segmentation
/ co-clustering technique that extracts groups from data
while removing insignificant features and extraneous rows,
giving result to an improved predictive modeling with a
learning model
520 3) Data sparseness: When a row is created, storage is
allocated for every column, irrespective of whether a
value exists for a given field. This gives rise to sparse
data which has a relatively high percentage of the
variable's cells, missing the actual data. In health care,
not all patients undergo every possible medical
diagnostics and lab results are equally sparse. Such
Sparse information or missing values causes predictive
models to produce inconclusive results. One primitive
technique is manual imputation of missing values by the
domain experts. Today, this scenario is almost impossible
as the data is huge and high dimensional in nature. A
variety of statistical and machine learning based missing
value estimation techniques exist which estimates missing
values by statistical analysis of the data set available.
However, most of these techniques do not consider the
importance of a domain expert's opinion in estimating
missing data. It has been proposed in this thesis that
techniques that use statistical information from the data
as well as opinion of the experts can estimate missing
values more effectively. This imputation procedure can
results in non-sparse data which is closer to the ground
truth and that improves predictive modeling
520 In this thesis, the following computational approaches has
been proposed for handling challenges described above for
an effective and improved predictive modeling -- 1) For
handling high-dimensional data a novel robust rank
aggregation-based feature selection technique has been
developed using exclusive rank aggregation strategies by
Borda (1781) and Kemeny (1959). The concept of robustness
of a feature selection algorithm has been introduced,
which can be defined as the property that characterizes
the stability of a ranked feature set toward achieving
similar classification accuracy across a wide range of
classifiers. This concept has been quantified with an
evaluation measure namely, the robustness index (RI). The
concept of inter-rater agreement for improving the quality
of the rank aggregation approach for feature selection has
also been proposed in this thesis
520 2) The concept of a co-clustering has been proposed that
is dedicated towards improving predictive modeling. The
novel idea of Learning based Co-Clustering (LCC) has been
developed as an optimization problem for a more effective
and improved predictive analysis. An important property of
this algorithm is that there is no need to specify the
number of co-clusters. A separate model testing framework
has also been proposed in this work, for reducing model
over-fitting and for a more accurate result. The
methodology has been evaluated on health care data as a
case study as well as several other publicly available
data sets
520 3) A missing value imputation technique based on domain
expert's knowledge and statistical analysis of the
available data has been proposed in this thesis. The
medical domain of HSCT has been chosen for the case study
and the domain expert's knowledge is a group of stem cell
transplant physician's opinion. The machine learning
approach developed can be defined as -- rule mining with
expert knowledge and similarity scoring based missing
value imputation. This technique has been developed and
validated using real world medical data set. The results
demonstrate the effectiveness and utility of this
technique in practice
533 Electronic reproduction.|bAnn Arbor, Mich. :|cProQuest,
|d2018
538 Mode of access: World Wide Web
650 4 Computer science
650 4 Health care management
655 7 Electronic books.|2local
690 0984
690 0769
710 2 ProQuest Information and Learning Co
710 2 University of Minnesota.|bComputer Science
773 0 |tDissertation Abstracts International|g77-03B(E)
856 40 |uhttps://pqdd.sinica.edu.tw/twdaoapp/servlet/
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