Mining Electronic Health Records Data: Network Analysis

of Adverse Health Effects among Victims of Intimate Partner Violence

By: Kathleen Whiting, Larry Y. Liu, Mehmet Koyuturk, Gunnur Karakurt

Introduction• Intimate Partner Domestic Violence (IPV) is a rising global issue• Extensive health, societal, and economic costs• In 2010, 30% of women experienced domestic abuse from an intimate

partner, 25% of which involved violent altercations with serious physical injuries

• There are notable adverse health effects associated with IPV• Ranges from minor injuries to serious disability and death• Common effects include psychological distress, STI’s, gynecological

problems, unplanned pregnancies and complications, gastrointestinal issues, mental health and substance abuse disorders, neurological symptoms, chronic physical ailments

Introduction• Current progress in rooting out key IPV-related health

effects has been lacking• Previous work has included identifying long-term

complications from IPV but no progress on mitigation or prevention• Female victims of violent crimes tend to have overall poorer

health and socio-economic status with worse adverse physical and mental conditions to be treated than female victims of non-violent crimes• They often don’t get treatment until six months after the incident,

and their treatment costs are often 15-24% higher than victims of non-violent crimes in the first 12 months

• Victims of violent crimes tend to present with symptoms that are misattributed to non-violence related causes

Introduction• Electronic Health Record (EHR) mining on adverse

health effects might help to improve screening of IPV• More accurate to place a picture of reoccurring health

effects suffered from known IPV patients in their health records then by self-reports• Easier for healthcare providers to understand and gauge• Could lead to finding associations among common health

effects that are seen in various IPV victims• Has a good sample size to work with when determining

these common health effects

Previous Work• EHR records were sourced via Explorys to identify

symptoms that were more prevalent with noted IPV victims than the general populace• The queries focused on differentiating health effects such

as:• Acute Injuries to violence-related physical injuries• Chronic or persistent conditions• Conditions that were from non-violent or violent causes

• These queries were then sorted into 28 broad categories• These categories affirmed four distinct health problems that

were complications from IPV

Previous WorkDisease Classification Category

Percent (%)

Acute Condition 34.09%Acute Injury 23.14%Chronic 16.18%Disorders 15.60%Cardiovascular 8.60%Pregnancy Related 7.53%Gynocological 7.45%Musculoskeletal 6.18%Mental Health 5.93%Gastrointestinal 5.60%Allergy 5.02%Substance Abuse 4.78%Other 4.57%Nervous system 4.53%Skin related 3.99%Respiratory 3.91%Eyes, Ears, Nose & Throat 3.75%Excretory 3.05%Personal History 2.22%Congenital/Hereditary 1.56%Endocrine 1.56%Neoplasm 1.32%Immune System 1.32%Nutrition 1.19%Procedure 0.82%Neuropathy 0.78%Family History 0.74%Diabetes 0.62%

.

Table 1. Percent of diseases considered significant in IPV falling into each category (28)

Objective /Rationale• Given the queries of adverse health effects sorted into 28

diverse categories, we wanted to see if there were any significant interactions among these health effects from IPV victims

• Using network analysis to generate a map helps to visualize these interactions• Each node on the map represents each of the 28 symptom categories• The edges connecting the nodes represents a relationship between

two categories, which signifies a shared frequency of symptoms coded between the two

• The edge thickness identifies the strength of these relationships

Methodology• A base data set from the previous study was provided

from Explorys (de-identified) • A data set containing 3458 symptom terms that are possibly

associated with IPV (domestic abuse)• The sample size (# of records) was 5870 containing

exclusively female patients between the ages of 18 to 65• Each symptom term was coded to at least one of the 28

broad categories attributed from the previous study• Among the 3458 symptom terms, 2429 terms were deemed

statistically significant @ 95% confidence (Chi-Squared Independence Test) w/ the background data set from previous study

Methodology• The key measurement to generate a network map was

to count the frequency of the occurrence of each category• Each symptom term will be accounted to at least one

category• Each symptom term with two attributed categories will be

counted as a pair• Each symptom term with three or more attributed categories

will be counted for each combinatory pairs of categories that could be associated• This results in two output files needed for mapping: nodes

and edge pairs

Methodology• Using GePhi, we inputted the two files containing the

nodes and edge pairs to generate the network map• The thickness of the edge pairs are determined by the

frequency of occurrence of that edge pair• The thickness of the nodes were determined by the number

of terms attributed to each category • The edge pairs are bidirectional as there was equal

precedence between the nodes in the pairing • The map was rescaled and transformed into a shape that

was more presentable and easier to analyze without compromising the integrity of the map

Results1a

Acute Injury

1b

Acute Condition

2 Chronic3 Substance Abuse4 Mental Health5 Other6 Disorders7 Gynecological8 Pregnancy Related9 Allergy10

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)

Figure 1. Network Map of the 28 categories made up of 2429 symptom terms found to be significantly more prevalent among victims of IPV than the general population. Larger nodes indicate higher coding frequency of the category by itself, while thicker edges reveal the strength of relationship between two nodes by signifying the frequency of any two categories being coded together on a single symptom term. Darker nodes appear more frequently among the pairs than lighter nodes.

Results1a

Acute Injury

1b

Acute Condition

2 Chronic3 Substance Abuse4 Mental Health5 Other6 Disorders7 Gynecological8 Pregnancy Related9 Allergy10

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)

• Out of 2429 symptom terms, there were at least 208 pairs of interactions present to at least one of the symptom terms

• Most significant nodes were 1b and 2, having the great frequency of occurrence

• 1b has the most significant degrees of twelve• Strong pairings with 2, and 19• Moderate pairings with 7, 18, 27,

8• Fair pairings with 3, 20, 21, 22,

23, 24• 2 has five significant degrees

• Strong pairing with 1b• Fair pairings with 4, 19, 20, 22

• It is worth noting that pairing between 1b and 2 could be due to ambiguity of a symptom term that could be chronic or acute

Results1a

Acute Injury

1b

Acute Condition

2 Chronic3 Substance Abuse4 Mental Health5 Other6 Disorders7 Gynecological8 Pregnancy Related9 Allergy10

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)

• 6 also shows significant pairings, but its target connected nodes are more significant in 1b and 2

• Node 1a does have significant symptom terms associated with it but it doesn’t have many terms that are associated with another node

Discussion• The network map showed consistent results to our

knowledge of IPV thus far• IPV victims tend to have complications of chronic and acute

conditions in addition to acute injuries• Chronic and acute conditions also have a strong link towards

conditions related to physiological systems• Chronic and acute conditions are prevalent since these

conditions often requires medical attention• Also these conditions are often related closely to each other

due to ambiguous symptom terms

Discussion• Gynecological and/or Pregnancy-related conditions also are

significant as IPV-related abuse often do severely target these aspects of the victim’s body• Mental health terms are expected to be significant but the discrete

nature of how the diagnosis terms were coded, the network did not reflect those terms• Generally mental health issues are not associated with other physiological

systems• We also hypothesized that stress may play a role in determining

category significance, because aspects of stress is involved to many of the categories that seem associated with IPV in the network map• IPV could also contribute to stress and anxiety as a side effect or

complication

Limitations• The network map does not accurately analyze subtle patterns and

cycles of these associations among the adverse health effects to IPV• The dimension of time was not added in but if added, would open up a new

layer of analysis and a more accurate network map

• The existing Explorys data set did not appear to reflect the generally accepted rate of IPV, that approximately 1 out of every 4 women would encounter an IPV event in their lifetimes• This could be due to the inadequate reporting and accuracy of self-reports of

IPV in potential victims• Healthcare professionals and EHR systems don’t consider IPV as a ‘condition’

or ‘diagnosis’ but as a ‘finding’, masking more subtle cases of IPV from being shown

Conclusion• The network map generated here by EHR mined data

of IPV victims helps to illustrate some of the relationships among adverse health effects associated with IPV• This would help to improve screening and diagnostic

measures to IPV patients as well as understanding any signs• Hope to push EHR and healthcare providers to make IPV

notation a higher priority for better accuracy in reporting and monitoring• Opens up new study angles to affirm new symptom

relationships that could aggress IPV side-effects and complications (like stress)

Personal Next Steps• Using Explorys to query new data sets to see a

relationship of adverse health effects between victims of IPV to Traumatic Brain Injuries (TBI)• Finding out significant diagnosis terms that are heavily seen

in both TBI and IPV patients• Developing a comorbidity relationship between these terms

to the general population (background)• Generating a network analysis and mappings of such

connections to hopefully find more less-likely considered diagnosis terms that can be associated with IPV-induced TBI patients

AcknowledgementsKarakurt Lab• Gunnur Karakurt• Kate Whiting

Advisor• Mehmet Koyuturk

Personal Thanks• Mario Yepez-Olvera

Funding:• Clinical and Translational Science

Collaborative of Cleveland• National Center for Advancing

Translational Sciences – National Institute of Health

Assistance:• Explorys Inc.