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

Overview

Journal J Med Toxicol

Publisher Springer

Specialty Toxicology

Date 2018 Jun 3

PMID 29858745

Citations 8

Authors

Kei Ouchi

Charlotta Lindvall

Peter R Chai

Edward W Boyer

Affiliations

Soon will be listed here.

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.

Citing Articles

The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States.

Mehrpour O, Saeedi F, Abdollahi J, Amirabadizadeh A, Goss F J Res Med Sci. 2023; 28:49.

PMID: 37496638 PMC: 10366979. DOI: 10.4103/jrms.jrms_602_22.

Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System.

Mehrpour O, Saeedi F, Nakhaee S, Tavakkoli Khomeini F, Hadianfar A, Amirabadizadeh A BMC Med Inform Decis Mak. 2023; 23(1):60.

PMID: 37024869 PMC: 10080923. DOI: 10.1186/s12911-022-02095-y.

Ware O, Buresh M, Irvin N, Stitzer M, Sweeney M Drug Alcohol Depend Rep. 2023; 5.

PMID: 36644224 PMC: 9835716. DOI: 10.1016/j.dadr.2022.100093.

Intelligent Telehealth in Pharmacovigilance: A Future Perspective.

Edrees H, Song W, Syrowatka A, Simona A, Amato M, Bates D Drug Saf. 2022; 45(5):449-458.

PMID: 35579810 PMC: 9112241. DOI: 10.1007/s40264-022-01172-5.

The path from big data analytics capabilities to value in hospitals: a scoping review.

Brossard P, Minvielle E, Sicotte C BMC Health Serv Res. 2022; 22(1):134.

PMID: 35101026 PMC: 8805378. DOI: 10.1186/s12913-021-07332-0.

References

Murdoch T, Detsky A . The inevitable application of big data to health care. JAMA. 2013; 309(13):1351-2. DOI: 10.1001/jama.2013.393. View

Deo R, Nallamothu B . Learning About Machine Learning: The Promise and Pitfalls of Big Data and the Electronic Health Record. Circ Cardiovasc Qual Outcomes. 2017; 9(6):618-620. PMC: 5832331. DOI: 10.1161/CIRCOUTCOMES.116.003308. View

Siddiqi N, House A, Holmes J . Occurrence and outcome of delirium in medical in-patients: a systematic literature review. Age Ageing. 2006; 35(4):350-64. DOI: 10.1093/ageing/afl005. View

Carreiro S, Wittbold K, Indic P, Fang H, Zhang J, Boyer E . Wearable Biosensors to Detect Physiologic Change During Opioid Use. J Med Toxicol. 2016; 12(3):255-62. PMC: 4996791. DOI: 10.1007/s13181-016-0557-5. View

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; 2017:465-470. PMC: 5631544. DOI: 10.1109/ICCNC.2017.7876173. View

Keller D, Juberg D, Catlin N, Farland W, Hess F, Wolf D . Identification and characterization of adverse effects in 21st century toxicology. Toxicol Sci. 2012; 126(2):291-7. PMC: 3307604. DOI: 10.1093/toxsci/kfr350. View

Shehab N, Lovegrove M, Geller A, Rose K, Weidle N, Budnitz D . US Emergency Department Visits for Outpatient Adverse Drug Events, 2013-2014. JAMA. 2016; 316(20):2115-2125. PMC: 6490178. DOI: 10.1001/jama.2016.16201. View

Berry S, Miller R . Falls: epidemiology, pathophysiology, and relationship to fracture. Curr Osteoporos Rep. 2008; 6(4):149-54. PMC: 2793090. DOI: 10.1007/s11914-008-0026-4. View

Makar M, Ghassemi M, Cutler D, Obermeyer Z . Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data. Int J Mach Learn Comput. 2016; 5(3):192-197. PMC: 5176360. DOI: 10.7763/IJMLC.2015.V5.506. View

10.

Nadkarni P, Ohno-Machado L, Chapman W . Natural language processing: an introduction. J Am Med Inform Assoc. 2011; 18(5):544-51. PMC: 3168328. DOI: 10.1136/amiajnl-2011-000464. View

11.

Chintha K, Indic P, Chapman B, Boyer E, Carreiro S . Wearable Biosensors to Evaluate Recurrent Opioid Toxicity After Naloxone Administration: A Hilbert Transform Approach. Proc Annu Hawaii Int Conf Syst Sci. 2018; 2018:3247-3252. PMC: 5782803. View

12.

Lilley E, Lindvall C, Lillemoe K, Tulsky J, Wiener D, Cooper Z . Measuring Processes of Care in Palliative Surgery: A Novel Approach Using Natural Language Processing. Ann Surg. 2017; 267(5):823-825. DOI: 10.1097/SLA.0000000000002579. View

13.

Sakuma M, Bates D, 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. DOI: 10.1002/pds.3331. View

14.

Field T, Gurwitz J, Harrold L, Rothschild J, Debellis K, Seger A . Risk factors for adverse drug events among older adults in the ambulatory setting. J Am Geriatr Soc. 2004; 52(8):1349-54. DOI: 10.1111/j.1532-5415.2004.52367.x. View

15.

Bzdok D, Krzywinski M, Altman N . Points of Significance: Machine learning: a primer. Nat Methods. 2018; 14(12):1119-1120. PMC: 5905345. DOI: 10.1038/nmeth.4526. View

16.

Liu R, AbdulHameed M, 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. PMC: 5465578. DOI: 10.1186/s40360-017-0153-6. View

17.

Smith S, Farmer B . 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. PMC: 4469728. DOI: 10.1007/s13181-015-0470-3. View

18.

Barenholtz Levy H . 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):177-187. DOI: 10.1016/j.cger.2017.01.007. View

19.

Chen H, Hu L, Li H, Hong G, Zhang T, Ma J . An Effective Machine Learning Approach for Prognosis of Paraquat Poisoning Patients Using Blood Routine Indexes. Basic Clin Pharmacol Toxicol. 2016; 120(1):86-96. DOI: 10.1111/bcpt.12638. View

20.

Brown J, Hutchison L, Li C, Painter J, Martin B . 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. PMC: 5287350. DOI: 10.1111/jgs.13884. View