Skip to main content

Advertisement

SpringerLink
Log in

Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department

  • Preliminary Research
  • Published:
Journal of Medical Toxicology Aims and scope Submit manuscript

Abstract

Adverse drug events (ADEs) are common and have serious consequences in older adults. ED visits are opportunities to identify and alter the course of such vulnerable patients. Current practice, however, is limited by inaccurate reporting of medication list, time-consuming medication reconciliation, and poor ADE assessment. This manuscript describes a novel approach to predict, detect, and intervene vulnerable older adults at risk of ADE using machine learning. Toxicologists’ expertise in ADE is essential to creating the machine learning algorithm. Leveraging the existing electronic health records to better capture older adults at risk of ADE in the ED may improve their care.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kohn LT, Corrigan JM, Donaldson MS, editors. To err is human: building a safer health system. Washington (DC); 2000.

  2. Lucado J, Paez K, Elixhauser A. Medication-Related Adverse Outcomes in U.S. Hospitals and Emergency Departments, 2008: Statistical Brief #109. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville (MD); 2006.

  3. Beijer HJ, de Blaey CJ. Hospitalisations caused by adverse drug reactions (ADR): a meta-analysis of observational studies. Pharm World Sci. 2002;24(2):46–54.

    Article  PubMed  CAS  Google Scholar 

  4. Berry SD, Miller RR. Falls: epidemiology, pathophysiology, and relationship to fracture. Curr Osteoporos Rep. 2008;6(4):149–54.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Tinetti ME, Kumar C. The patient who falls: “It’s always a trade-off”. JAMA. 2010;303(3):258–66. https://doi.org/10.1001/jama.2009.2024.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Alagiakrishnan K, Wiens CA. An approach to drug induced delirium in the elderly. Postgrad Med J. 2004;80(945):388–93. https://doi.org/10.1136/pgmj.2003.017236.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Siddiqi N, House AO, Holmes JD. Occurrence and outcome of delirium in medical in-patients: a systematic literature review. Age Ageing. 2006;35(4):350–64. https://doi.org/10.1093/ageing/afl005.

    Article  PubMed  Google Scholar 

  8. Leendertse AJ, Egberts AC, Stoker LJ, van den Bemt PM, Group HS. Frequency of and risk factors for preventable medication-related hospital admissions in the Netherlands. Arch Intern Med. 2008;168(17):1890–6. https://doi.org/10.1001/archinternmed.2008.3.

    Article  PubMed  Google Scholar 

  9. Nagurney JM, Fleischman W, Han L, Leo-Summers L, Allore HG, Gill TM. Emergency department visits without hospitalization are associated with functional decline in older persons. Ann Emerg Med. 2017;69(4):426–33. https://doi.org/10.1016/j.annemergmed.2016.09.018.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Mangino PD. Role of the pharmacist in reducing medication errors. J Surg Oncol. 2004;88(3):189–94. https://doi.org/10.1002/jso.20127.

    Article  PubMed  Google Scholar 

  11. Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ, Budnitz DS. US emergency department visits for outpatient adverse drug events, 2013–2014. JAMA. 2016;316(20):2115–25. https://doi.org/10.1001/jama.2016.16201.

    Article  PubMed  Google Scholar 

  12. American College of Emergency Physicians TAGS, Emergency Nurses Association, and the Society for Academic Emergency Medicine. The Geriatric Emergency Department Guidelines. 2013. https://www.saem.org/docs/default-source/saem-documents/education/geri_ed_guidelines_final.pdf?sfvrsn=6.

  13. Kubota KJ, Chen JA, Little MA. Machine learning for large-scale wearable sensor data in Parkinson’s disease: concepts, promises, pitfalls, and futures. Mov Disord. 2016;31(9):1314–26. https://doi.org/10.1002/mds.26693.

    Article  PubMed  Google Scholar 

  14. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013;309(13):1351–2. https://doi.org/10.1001/jama.2013.393.

    Article  PubMed  CAS  Google Scholar 

  15. Janke AT, Overbeek DL, Kocher KE, Levy PD. Exploring the potential of predictive analytics and big data in emergency care. Ann Emerg Med. 2016;67(2):227–36. https://doi.org/10.1016/j.annemergmed.2015.06.024.

    Article  PubMed  Google Scholar 

  16. Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15:233–4. https://doi.org/10.1038/nmeth.4642.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Bzdok D, Krzywinski M, Altman N. Points of significance: machine learning: a primer. Nat Methods. 2017;14(12):1119–20. https://doi.org/10.1038/nmeth.4526.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Sakuma M, Bates DW, Morimoto T. Clinical prediction rule to identify high-risk inpatients for adverse drug events: the JADE study. Pharmacoepidemiol Drug Saf. 2012;21(11):1221–6. https://doi.org/10.1002/pds.3331.

    Article  PubMed  Google Scholar 

  19. Field TS, Gurwitz JH, Harrold LR, Rothschild J, DeBellis KR, Seger AC, et al. Risk factors for adverse drug events among older adults in the ambulatory setting. J Am Geriatr Soc. 2004;52(8):1349–54. https://doi.org/10.1111/j.1532-5415.2004.52367.x.

    Article  PubMed  Google Scholar 

  20. Kuo PJ, Wu SC, Chien PC, Rau CS, Chen YC, Hsieh HY, et al. Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan. BMJ Open. 2018;8(1):e018252. https://doi.org/10.1136/bmjopen-2017-018252.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, et al. Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach. Acad Emerg Med. 2016;23(3):269–78. https://doi.org/10.1111/acem.12876.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Liu R, AbdulHameed MDM, Kumar K, Yu X, Wallqvist A, Reifman J. Data-driven prediction of adverse drug reactions induced by drug-drug interactions. BMC Pharmacol Toxicol. 2017;18(1):44. https://doi.org/10.1186/s40360-017-0153-6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Lilley EJ, Lindvall C, Lillemoe KD, Tulsky JA, Wiener DC, Cooper Z. Measuring processes of Care in Palliative Surgery: a novel approach using natural language processing. Ann Surg. 2017:1. https://doi.org/10.1097/SLA.0000000000002579.

  24. Judson R, Elloumi F, Setzer RW, Li Z, Shah I. A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model. BMC Bioinformatics. 2008;9:241. https://doi.org/10.1186/1471-2105-9-241.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Zhang Y, Wong YS, Deng J, Anton C, Gabos S, Zhang W, et al. Machine learning algorithms for mode-of-action classification in toxicity assessment. BioData Min. 2016;9:19. https://doi.org/10.1186/s13040-016-0098-0.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Chen H, Hu L, Li H, Hong G, Zhang T, Ma J, et al. An effective machine learning approach for prognosis of paraquat poisoning patients using blood routine indexes. Basic Clin Pharmacol Toxicol. 2017;120(1):86–96. https://doi.org/10.1111/bcpt.12638.

    Article  PubMed  CAS  Google Scholar 

  27. Wang J, Fang H, Carreiro S, Wang H, Boyer E. A new mining method to detect real time substance use events from wearable biosensor data stream. Int Conf Comput Netw Commun. 2017;2017:465–70. https://doi.org/10.1109/ICCNC.2017.7876173.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Smith SW, Farmer BM. Toxicology in the Service of Patient and Medication Safety: a selected glance at past and present innovations. J Med Toxicol. 2015;11(2):245–52. https://doi.org/10.1007/s13181-015-0470-3.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Keller DA, Juberg DR, Catlin N, Farland WH, Hess FG, Wolf DC, et al. Identification and characterization of adverse effects in 21st century toxicology. Toxicol Sci. 2012;126(2):291–7. https://doi.org/10.1093/toxsci/kfr350.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Brown JD, Hutchison LC, Li C, Painter JT, Martin BC. Predictive validity of the beers and screening tool of older persons’ potentially inappropriate prescriptions (STOPP) criteria to detect adverse drug events, hospitalizations, and emergency department visits in the United States. J Am Geriatr Soc. 2016;64(1):22–30. https://doi.org/10.1111/jgs.13884.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Levy HB. Polypharmacy reduction strategies: tips on incorporating American Geriatrics Society Beers and Screening Tool of Older People’s Prescriptions Criteria. Clin Geriatr Med. 2017;33(2):177–87. https://doi.org/10.1016/j.cger.2017.01.007.

    Article  PubMed  Google Scholar 

  32. Reeve E, Wiese MD. Benefits of deprescribing on patients’ adherence to medications. Int J Clin Pharm. 2014;36(1):26–9. https://doi.org/10.1007/s11096-013-9871-z.

    Article  PubMed  Google Scholar 

  33. Cafri GLL, Paxton EW, Fan J. Predicting risk for adverse health events using random forest. J Appl Stat. 2017:1–16. https://doi.org/10.1080/02664763.2017.1414166.

  34. Makar M, Ghassemi M, Cutler DM, Obermeyer Z. Short-term mortality prediction for elderly patients using Medicare claims data. Int J Mach Learn Comput. 2015;5(3):192–7. https://doi.org/10.7763/IJMACHINELEARNINGC.2015.V5.506.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc. 2011;18(5):544–51. https://doi.org/10.1136/amiajnl-2011-000464.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Forsyth AW, Barzilay R, Hughes KS, Lui D, Lorenz KA, Enzinger A, et al. Machine learning methods to extract documentation of breast cancer symptoms from electronic health records. J Pain Symptom Manag. 2018;55:1492–9. https://doi.org/10.1016/j.jpainsymman.2018.02.016.

    Article  Google Scholar 

  37. Deo RC, Nallamothu BK. Learning about machine learning: the promise and pitfalls of big data and the electronic health record. Circ Cardiovasc Qual Outcomes. 2016;9(6):618–20. https://doi.org/10.1161/CIRCOUTCOMES.116.003308.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Obermeyer Z, Lee TH. Lost in thought—the limits of the human mind and the future of medicine. N Engl J Med. 2017;377(13):1209–11. https://doi.org/10.1056/NEJMp1705348.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Carreiro S, Wittbold K, Indic P, Fang H, Zhang J, Boyer EW. Wearable biosensors to detect physiologic change during opioid use. J Med Toxicol. 2016;12(3):255–62. https://doi.org/10.1007/s13181-016-0557-5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Chintha KK, Indic P, Chapman B, Boyer EW, Carreiro S. Wearable biosensors to evaluate recurrent opioid toxicity after naloxone administration: a Hilbert transform approach. Proc Annu Hawaii Int Conf Syst Sci. 2018;2018:3247–52.

    PubMed  PubMed Central  Google Scholar 

Download references

Funding

Dr. Boyer is supported by the National Institutes of Health 1K24DA037109. Dr. Ouchi is supported by the Grants for Early Medical and Surgical Subspecialists’ Transition to Aging Research award from the National Institute on Aging (1R03AG056449), the Emergency Medicine Foundation (EMF), and the Society of Academic Emergency Medicine (SAEM).

Author information

Authors and Affiliations

  1. Department of Emergency Medicine, Brigham and Women’s Hospital, 75 Francis St, Neville 200, Boston, MA, 02125, USA

    Kei Ouchi, Peter R. Chai & Edward W. Boyer

  2. Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA

    Kei Ouchi, Peter R. Chai & Edward W. Boyer

  3. Serious Illness Care Program, Ariadne Labs, Boston, MA, USA

    Kei Ouchi

  4. Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, MA, USA

    Kei Ouchi & Charlotta Lindvall

  5. Division of Palliative Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Charlotta Lindvall

  6. The Fenway Institute, Boston, MA, USA

    Peter R. Chai & Edward W. Boyer

Authors
  1. Kei Ouchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charlotta Lindvall
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter R. Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Edward W. Boyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kei Ouchi.

Ethics declarations

Conflict of Interest

The authors declare there are no additional conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ouchi, K., Lindvall, C., Chai, P.R. et al. Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department. J. Med. Toxicol. 14, 248–252 (2018). https://doi.org/10.1007/s13181-018-0667-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13181-018-0667-3

Keywords

Search

Navigation