Prediction and pattern analysis of medication refill adherence through electronic health records and dispensation data

Abegaz, 2017, Target organ damage and the long term effect of nonadherence to clinical practice guidelines in patients with hypertension: a retrospective cohort study, Int. J. Hypertens., 2090-0384, 749 Galozy, 2020, Pitfalls of medication adherence approximation through ehr and pharmacy records: Definitions, data and computation, Int. J. Med. Informatics, 136, 104092, 10.1016/j.ijmedinf.2020.104092 Andrade, 2006, Methods for evaluation of medication adherence and persistence using automated databases, Pharmacoepidemiol. Drug Saf., 15, 565, 10.1002/pds.1230 Ashfaq, 2020, Data resource profile: regional healthcare information platform in Halland, Sweden, a dedicated environment for healthcare research, Int. J. Epidemiol., 10.1093/ije/dyz262 Baumgartner, 2018, A systematic review of medication adherence thresholds dependent of clinical outcomes, Front. Pharmacol., 9, 10.3389/fphar.2018.01290 Bosworth, 2011, Medication adherence: a call for action, Am. Heart J., 162, 412, 10.1016/j.ahj.2011.06.007 Burnier, 2019, Is there a threshold for medication adherence? Lessons learnt from electronic monitoring of drug adherence, Front. Pharmacol., 9, 10.3389/fphar.2018.01540 Burnier, 2019, Adherence in hypertension, Circ. Res., 124, 1124, 10.1161/CIRCRESAHA.118.313220 Cadarette, 2015, An introduction to health care administrative data, Can. J. Hospital Pharmacy, 68, 232, 10.4212/cjhp.v68i3.1457 Cutler, 2018, Economic impact of medication non-adherence by disease groups: a systematic review, BMJ Open, 8, e016982, 10.1136/bmjopen-2017-016982 Ester, M., peter Kriegel, H., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise, AAAI Press. pp. 226–231. doi:10.5555/3001460.3001507. URL https://dl.acm.org/doi/10.5555/3001460.3001507. Franklin, 2015, Predicting adherence trajectory using initial patterns of medication filling, Am. J. Manag. Care, 21, e537 Franklin, 2016, Observing versus predicting: initial patterns of filling predict long-term adherence more accurately than high-dimensional modeling techniques, Health Serv. Res., 51, 220, 10.1111/1475-6773.12310 Grymonpre, 2006, Validity of a prescription claims database to estimate medication adherence in older persons, Med. Care, 44, 10.1097/01.mlr.0000207817.32496.cb Harrison, 2010, Introduction to Monte Carlo simulation, AIP Conf. Proc., 1204, 17, 10.1063/1.3295638 F.D.R. Hobbs, Cardiovascular disease: different strategies for primary and secondary prevention? Heart 90 (2004) 1217–1223. doi:10.1136/hrt.2003.027680, arXiv:https://heart.bmj.com/content/90/10/1217.full.pdf. https://heart.bmj.com/content/90/10/1217. Kini, 2018, Interventions to improve medication adherence: a review, JAMA, 320, 2461, 10.1001/jama.2018.19271 Krumme, 2017, Predicting 1-year statin adherence among prevalent users: a retrospective cohort study, J. Manag. Care Specialty Pharmacy, 23, 494, 10.18553/jmcp.2017.23.4.494 Krumme, 2016, Can purchasing information be used to predict adherence to cardiovascular medications? An analysis of linked retail pharmacy and insurance claims data, BMJ Open, 6, e011015, 10.1136/bmjopen-2015-011015 Kumamaru, 2018, Using previous medication adherence to predict future adherence, J. Managed Care Specialty Pharmacy, 24, 1146, 10.18553/jmcp.2018.24.11.1146 Lam, 2015, Medication adherence measures: an overview, BioMed Res. Int., 1–12 Lauffenburger, 2018, Predicting adherence to chronic disease medications in patients with long-term initial medication fills using indicators of clinical events and health behaviors, J. Managed Care Specialty Pharmacy, 24, 469, 10.18553/jmcp.2018.24.5.469 Lundberg, 2020, From local explanations to global understanding with explainable ai for trees, Nat. Mach. Intell., 2, 2522, 10.1038/s42256-019-0138-9 Lundberg, 2017, A unified approach to interpreting model predictions, 4765 Lundberg, 2018, Explainable machine-learning predictions for the prevention of hypoxaemia during surgery, Nat. Biomed. Eng., 2, 749, 10.1038/s41551-018-0304-0 J. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Statistics, vol. 1, University of California Press, Berkeley, Calif, 1967, pp. 281–297. https://projecteuclid.org/euclid.bsmsp/1200512992. Paparrizos, 2016, k-shape: Efficient and accurate clustering of time series, ACM SIGMOD Record, 45, 69, 10.1145/2949741.2949758 Pedregosa, 2011, Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825 Prakash, 2019, Target organ damage in newly detected hypertensive patients, J. Family Med. Prim. Care, 8, 2042, 10.4103/jfmpc.jfmpc_231_19 Steiner, 2009, Sociodemographic and clinical characteristics are not clinically useful predictors of refill adherence in patients with hypertension, Circ. Cardiovasc. Qual. Outcomes, 2, 451, 10.1161/CIRCOUTCOMES.108.841635