1. Mining Electronic Health Records: Data-Driven Analysis of
Adverse Health Effects among Victims of Intimate Partner Violence
Kate Whiting a, Larry Y. Liub, Mehmet Koyuturkc,b, & Günnur Karakurta
a Department of Psychiatry, UH Mood Disorders Program, b Center for Proteomics & Bioinformatics, and c Department of Electrical Engineering & Computer Science
Case Western Reserve University, Cleveland, Ohio
BACKGROUND
DISCUSSION
OBJECTIVES
METHODOLOGY
• Intimate Partner Violence (IPV) is an extensive problem worldwide, with
detrimental health, societal, and economic costs
• In 2010, approximately 30% of women experienced domestic abuse from an
intimate partner, 25% of which involved violent altercations with serious physical
injuries (Black et al., 2011)
• Adverse health effects found to be associated with IPV include numerous
psychological and physical ailments.
• Electronic Health Record (EHR) mining provides a unique perspective on how to
analyze and identify trends in IPV-related healthcare utilization, which may
eventually enable us to improve screening and treatment methods.
• Determine if there are any significant interactions among these health effect
categories associated with IPV
• Calculate the strength of the relationships between these categories and generate
a network map to visualize these interactions in a more concise and efficient way
• Demonstrate that data mining and network analysis are valid methods for
exploring this problem
•For this study, we wanted to create a network map to better understand how these
categories interact. We sorted the 2429 terms by the frequency of occurrence in each
of their noted categories, with each term accounted to at least one category to a node
and terms with two more categories to all of their possible pair combinations
•These output node and edge pair counts were placed in GePhi to generate the
network map, where 208 interaction pairs were observed among the 2429 terms
•Node size indicates the number of terms attributed to each category, edge thickness
shows the frequency of occurrence of that edge pair of interactions, and darker nodes
appeared more frequently among the pairs than lighter nodes. Edge pairs are
bidirectional, because equal precedence was given to each node in pairing
•The network map was rescaled and transformed into a shape that was more
presentable and easier to analyze without compromising the integrity of the map
PREVIOUS STUDY
• In a previous study, we used the
Explorys platform to query the
EHR data of adult females aged
18-65 with the finding ‘domestic
violence’ (N=5870)
• After comparing this extracted
data to data from a background
query, we were left with a dataset
containing 3458 symptom terms,
of which 2429 terms were
significantly more prevalent
among the IPV population at a
95% significance level (Chi-
Squared Independence Test)
• We then coded these symptom
terms to 28 broad categories, and
found that IPV is predominantly
associated with four types of
health problems: acute, chronic,
gynecological, and
mental/behavioral health 0 200 400 600 800 1000 1200
Acute Condition
Acute Injury
Disorders
Cardiovascular
Musculoskeletal
Gynocological
Neoplasm
Pregnancy Related
Gastrointestinal
Eyes, Ears, Nose & Throat
Allergy
Skin related
Other
Nervous system
Chronic
Mental Health
Excretory
Respiratory
Substance Abuse
Endocrine
Personal History
Immune System
Congenital/Hereditary
Family History
Diabetes
Neuropathy
Procedure
Nutrition
1a Acute Injury
1b Acute Condition
2 Chronic
3 Substance Abuse
4 Mental Health
5 Other
6 Disorders
7 Gynecological
8 Pregnancy Related
9 Allergy
10 Procedure
11 Congenital/Hereditary
12 Nutrition
13 Neoplasm
14 Personal History
15 Family History
16 Neuropathy
17 Diabetes
18 Gastrointestinal
19 Cardiovascular
20 Nervous System
21 Respiratory
22 Musculoskeletal
23 Eyes, Ears, Nose & Throat
24 Excretory
25 Endocrine
26 Immune System
27 Skin Related (not burns)
RESULTS
•Most significant nodes were 1b (acute condition) and 2 (chronic), with 1b having
12 significant degrees, and 2 having six significant degrees
•Common pairings focus around acute or chronic conditions, with strong links to
gynecological and pregnancy related conditions, as well as gastrointestinal and
cardiovascular issues
•The significance of acute and chronic conditions may be ambiguous, because many
symptoms were not specific enough to determine acute vs chronic status
•Node 1a (acute injury) has few symptom terms associated with it, but this is not
surprising because most terms coded as acute injury would be unlikely to be cross-
coded with other categories.
CONCLUSIONS
ACKNOWLEDGEMENTS
•The network map showed results consistent with current knowledge of IPV related
health concerns (Campbell et al., 2002). Victims tend to have both acute and
chronic complications ranging across numerous physiological systems including
neurological, gastrointestinal, cardiovascular, and gynecological.
•The significance of gynecological and/or Pregnancy-related conditions may be
related to the targeting of these body areas by IPV perpetrators.
•Although we expected mental health to be more significant, it did not show strong
relationships, probably because mental health symptoms were generally not cross-
coded (similar to acute injury category)
•We suspect that stress may play a key role in category significance, because IPV is
known to cause stress for victims, and many of the significant categories are
known to interact negatively with stress.
•Since we were unable to incorporate temporal variables, the network map does not
accurately analyze subtle patterns and cycles of these adverse health effects. We
hope future research will be able to account for them dimension of time.
•The network map generated here by EHR mined data of IPV victims shows
evidence of significant relationships between adverse health effects and IPV.
•We acknowledge that the data extracted for this analysis encompasses only the
most severe instances of IPV. We hope to develop screening tools in the future to
aid clinicians with IPV diagnosis and treatment, and to improve IPV reporting.
•To that end, future research will further investigate comorbidity of adverse health
effects with IPV using network analysis.
This publication was made possible in part by the Clinical and Translational Science Collaborative of Cleveland, NIH/NCRR CTSA KL2TR000440 from the
National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH roadmap for Medical Research. Its
contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Special thanks to Explorys, Inc., an IBM
company. Correspondence regarding this research can be sent to Dr. Gunnur Karakurt: gkk6@case.edu