Discovering opioid users’ medical comorbidities: a data mining approach

Yong Mi Kim, Pranay Kathuria, Dursun Delen

Research output: Contribution to journalArticle

Abstract

Background: To combat the opioid crisis, scholars have investigated medical comorbidities associated with opioid use; however, the findings are often contradictory. The main problem resides in the lack of controlling for polydrug use, as the combined use of drugs can cause additive and/or synergistic effects. Methods: This study employed the apriori association rule mining algorithm, which has the capability to discover direct associations between opioid use and its comorbidities and further identify new medical comorbidities buried in the dataset as this method can process thousands of variables. Results: After controlling for polydrug use, findings show that sole opioid use associates with high systolic and diastolic blood pressures and high HbA1c, but the combined use of opioids and benzodiazepine or marijuana did not elevate systolic or diastolic blood pressure. Additionally, by including every variable in the database, this study discovered new medical comorbidities such as elevated red blood cell and gastrointestinal problems, which have not been reported in existing studies. Conclusions: The proposed analytical strategy made significant steps toward resolving the conflicting findings, as the combined use can have additive and/or synergistic effects on the medical comorbidities from opioid use. The newly discovered medical comorbidities offer future research topics. Abbreviations: BZD: benzodiazepine; MRN: marijuana; BPS: blood pressure systolic; BPD: blood pressure diastolic; AST: aspartate aminotransferase; ALT: alanine transaminase; BMI: body mass index; RBC: red blood cell count; BUN: blood urea nitrogen; MPV: mean platelet volume; EMR: electronic medical records; IRB: Institutional Review Board; CHFDW: Cerner HealthFacts® Data Warehouse.

Original languageEnglish
JournalJournal of Substance Use
DOIs
StateAccepted/In press - 1 Jan 2019

Fingerprint

Data Mining
comorbidity
Opioid Analgesics
Comorbidity
Blood Pressure
Research Ethics Committees
Blood Urea Nitrogen
Cannabis
Benzodiazepines
Mean Platelet Volume
Erythrocyte Count
Electronic Health Records
Aspartate Aminotransferases
Alanine Transaminase
Body Mass Index
Erythrocytes
electronics
Databases
drug
cause

Keywords

  • apriori association rule mining
  • data mining
  • electronic medical records
  • medical comorbidity
  • Opioids

Cite this

@article{88c2aadc2f7f4b85bba0c865e125a328,
title = "Discovering opioid users’ medical comorbidities: a data mining approach",
abstract = "Background: To combat the opioid crisis, scholars have investigated medical comorbidities associated with opioid use; however, the findings are often contradictory. The main problem resides in the lack of controlling for polydrug use, as the combined use of drugs can cause additive and/or synergistic effects. Methods: This study employed the apriori association rule mining algorithm, which has the capability to discover direct associations between opioid use and its comorbidities and further identify new medical comorbidities buried in the dataset as this method can process thousands of variables. Results: After controlling for polydrug use, findings show that sole opioid use associates with high systolic and diastolic blood pressures and high HbA1c, but the combined use of opioids and benzodiazepine or marijuana did not elevate systolic or diastolic blood pressure. Additionally, by including every variable in the database, this study discovered new medical comorbidities such as elevated red blood cell and gastrointestinal problems, which have not been reported in existing studies. Conclusions: The proposed analytical strategy made significant steps toward resolving the conflicting findings, as the combined use can have additive and/or synergistic effects on the medical comorbidities from opioid use. The newly discovered medical comorbidities offer future research topics. Abbreviations: BZD: benzodiazepine; MRN: marijuana; BPS: blood pressure systolic; BPD: blood pressure diastolic; AST: aspartate aminotransferase; ALT: alanine transaminase; BMI: body mass index; RBC: red blood cell count; BUN: blood urea nitrogen; MPV: mean platelet volume; EMR: electronic medical records; IRB: Institutional Review Board; CHFDW: Cerner HealthFacts{\circledR} Data Warehouse.",
keywords = "apriori association rule mining, data mining, electronic medical records, medical comorbidity, Opioids",
author = "Kim, {Yong Mi} and Pranay Kathuria and Dursun Delen",
year = "2019",
month = "1",
day = "1",
doi = "10.1080/14659891.2019.1659869",
language = "English",
journal = "Journal of Substance Use",
issn = "1465-9891",
publisher = "Informa Healthcare",

}

Discovering opioid users’ medical comorbidities : a data mining approach. / Kim, Yong Mi; Kathuria, Pranay; Delen, Dursun.

In: Journal of Substance Use, 01.01.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Discovering opioid users’ medical comorbidities

T2 - a data mining approach

AU - Kim, Yong Mi

AU - Kathuria, Pranay

AU - Delen, Dursun

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Background: To combat the opioid crisis, scholars have investigated medical comorbidities associated with opioid use; however, the findings are often contradictory. The main problem resides in the lack of controlling for polydrug use, as the combined use of drugs can cause additive and/or synergistic effects. Methods: This study employed the apriori association rule mining algorithm, which has the capability to discover direct associations between opioid use and its comorbidities and further identify new medical comorbidities buried in the dataset as this method can process thousands of variables. Results: After controlling for polydrug use, findings show that sole opioid use associates with high systolic and diastolic blood pressures and high HbA1c, but the combined use of opioids and benzodiazepine or marijuana did not elevate systolic or diastolic blood pressure. Additionally, by including every variable in the database, this study discovered new medical comorbidities such as elevated red blood cell and gastrointestinal problems, which have not been reported in existing studies. Conclusions: The proposed analytical strategy made significant steps toward resolving the conflicting findings, as the combined use can have additive and/or synergistic effects on the medical comorbidities from opioid use. The newly discovered medical comorbidities offer future research topics. Abbreviations: BZD: benzodiazepine; MRN: marijuana; BPS: blood pressure systolic; BPD: blood pressure diastolic; AST: aspartate aminotransferase; ALT: alanine transaminase; BMI: body mass index; RBC: red blood cell count; BUN: blood urea nitrogen; MPV: mean platelet volume; EMR: electronic medical records; IRB: Institutional Review Board; CHFDW: Cerner HealthFacts® Data Warehouse.

AB - Background: To combat the opioid crisis, scholars have investigated medical comorbidities associated with opioid use; however, the findings are often contradictory. The main problem resides in the lack of controlling for polydrug use, as the combined use of drugs can cause additive and/or synergistic effects. Methods: This study employed the apriori association rule mining algorithm, which has the capability to discover direct associations between opioid use and its comorbidities and further identify new medical comorbidities buried in the dataset as this method can process thousands of variables. Results: After controlling for polydrug use, findings show that sole opioid use associates with high systolic and diastolic blood pressures and high HbA1c, but the combined use of opioids and benzodiazepine or marijuana did not elevate systolic or diastolic blood pressure. Additionally, by including every variable in the database, this study discovered new medical comorbidities such as elevated red blood cell and gastrointestinal problems, which have not been reported in existing studies. Conclusions: The proposed analytical strategy made significant steps toward resolving the conflicting findings, as the combined use can have additive and/or synergistic effects on the medical comorbidities from opioid use. The newly discovered medical comorbidities offer future research topics. Abbreviations: BZD: benzodiazepine; MRN: marijuana; BPS: blood pressure systolic; BPD: blood pressure diastolic; AST: aspartate aminotransferase; ALT: alanine transaminase; BMI: body mass index; RBC: red blood cell count; BUN: blood urea nitrogen; MPV: mean platelet volume; EMR: electronic medical records; IRB: Institutional Review Board; CHFDW: Cerner HealthFacts® Data Warehouse.

KW - apriori association rule mining

KW - data mining

KW - electronic medical records

KW - medical comorbidity

KW - Opioids

UR - http://www.scopus.com/inward/record.url?scp=85071545912&partnerID=8YFLogxK

U2 - 10.1080/14659891.2019.1659869

DO - 10.1080/14659891.2019.1659869

M3 - Article

AN - SCOPUS:85071545912

JO - Journal of Substance Use

JF - Journal of Substance Use

SN - 1465-9891

ER -