Comparison of Bayesian and Regression Schemes to Model Public Health Services

Authors: Sotirios Raptis

Abstract:

Bayesian reasoning (BR) or Linear (Auto) Regression (AR/LR) can predict different sources of data using priors or other data, and can link social service demands in cohorts, while their consideration in isolation (self-prediction) may lead to service misuse ignoring the context. The paper advocates that BR with Binomial (BD), or Normal (ND) models or raw data (.D) as probabilistic updates can be compared to AR/LR to link services in Scotland and reduce cost by sharing healthcare (HC) resources. Clustering, cross-correlation, along with BR, LR, AR can better predict demand. Insurance companies and policymakers can link such services, and examples include those offered to the elderly, and low-income people, smoking-related services linked to mental health services, or epidemiological weight in children. 22 service packs are used that are published by Public Health Services (PHS) Scotland and Scottish Government (SG) from 1981 to 2019, broken into 110 year series (factors), joined using LR, AR, BR. The Primary component analysis found 11 significant factors, while C-Means (CM) clustering gave five major clusters.

Keywords: Bayesian probability, cohorts, data frames, regression, services, prediction.

References:

[1] ByXu., HRISTINA PASHOVA† AND PATRICK J. HEAGERTY (2017), Comparing Healthcare Utilization Patterns Via Global Differences in the Endorsement of Current Procedural Terminology Codes , The annals of applied statistics, Vol. 11, no. 3, 1349–1374, doi: 10.1214/17 − aoas1028
[2] Simon Bottery . https : //www.kingsfund.org.uk/about − us/whos − who/simon − bottery?page = 2 . The King’s Fund
[3] Public Health Scotland (2020) . Data and intelligence. A – Z Subject Index. https : //www.isdscotland.org/A − to − Z − index/index.asp
[4] Scottish Government (2019). Statistics Service Health and Social Care Data . https : //statistics.gov.scot/datahome
[5] Vittorio Lippi (2019). Incremental Principal Component Analysis: Exact implementation and continuity corrections.arXiv: 1901:07922v2; stat:ML; 13May2019. https : //arxiv.org/pdf/1901.07922.pdf
[6] Ian Litchfield (2019). Can pathways of patients with long-term conditions in UK primary care? A study protocol. BMJ Open, 2018. https : //bmjopen.bmj.com/content/8/12/e019947
[7] Dimitris Bertsimas, Colin Pawlowski, Ying Daisy Zhuo (2018) From Predictive Methods to Missing Data Imputation: An Optimisation Approach, Journal of Machine Learning Research 18 (2018) 1-39
[8] E.M. Mirkes (2018), T.J. Coats, J. Levesley, A.N. Gorban (2018). From Predictive Methods to Missing Data Imputation: An Optimisation Approach. Journal of Machine Learning Research, 18 (2018),1-39. http : //dx.doi.org/10.1016/j.compbiomed.2016.06.004
[9] deRooij M. (2018). Transitional modelling of experimental longitudinal data with missing values. Adv Data AnalClassif, 12,107–130. https : //link.springer.com/article/10.1007/s11634 − 015 − 0226 − 6
[10] Muge Capan (2020), Stephen Hoover, et al. (2019), Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit Multitask learning and benchmarking with clinical time series data, Appl. Clin. Inform.. 2019,7(2):275–289. https : //dx.doi.org/10.4338%2FACI − 2015 − 09 − RA − 0127
[11] Langton, J.M. (2018), Wong, S.T., Burge, F. et al. (2015), Population segments as a tool for health care performance reporting: an exploratory study in the Canadian province of British Columbia, BMC Fam Pract,21-98(2020). https : //doi.org/10.1186/s12875 − 020 − 01141 − w
[12] Vimal Mishra (2019), MD, MMCI, Shin-Ping Tu, MD, MPH, Joseph Heim, PhD, Heather Masters, MD, Lindsey Hall, MPH, Ralph R. Clark, MD, Alan W. Dow, MD (2019), Predicting the Future: Using Simulation Modeling to Forecast Patient Flow on General Medicine Units, J. Hosp. Med.,2019,1,9-15. doi:10.12788/jhm.3081
[13] Guersel, Gueney (2019), Healthcare, uncertainty, and fuzzy logic. Digital Medicine, 2016,2,101-12. https : //www : researchgate : net = publication = 310817255Healthcareuncertaintyandfuzzylogic
[14] Deborah A.Marshall, LinaBurgos-Liz et al. (2015), Applying Dynamic Simulation Modeling Methods in Health Care Delivery Research—The SIMULATE Checklist:Report of the ISPOR Simulation Modeling Emerging Good Practices Task Force, Value in Health, volume 18,Issue 2,March 2015,143-144. https : //doi.org/10.1016/j.jval.2014.12.001
[15] D. Ben-Tovim, J. Filar, et al. (2019), Hospital Event Simulation Model: Arrivals to Discharge, 21st International Congress on Modelling and Simulation. Gold Coast,Australia. https : //www.mssanz.org.au/modsim2015/H2/bentovim.pdf
[16] David J Spiegelhalter, Jonathan P Myles, David R Jones(1999) An introduction to bayesian methods in health technology assessment , BMJ 1999; 319, doi: https://doi.org/10.1136/bmj.319.7208.508
[17] Anupreet Porwal, d Adrian E. Rafterya Comparing methods for statistical inference with model uncertainty , PNAS 2022 Vol. 119 No. 16 e2120737119, ttps : //www.pnas.org/doi/pdf/10.1073/pnas.2120737119
[18] Gredell Devin (2019), Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS), Data. Sci Rep.,9 5721 (2019). https : //pubmed.ncbi.nlm.nih.gov/30952873/
[19] Bebbington E, Furniss, D . (2015), Linear regression analysis of Hospital Episode Statistics predicts a large increase in demand for elective hand surgery in England, J. Plast. Reconstr. Aesthet. Surg, 2015, Feb,68(2),243-51. doi:10.1016/j.bjps.2014.10.011
[20] Uematsu, H., Yamashita, K., Kunisawa, S., Otsubo, T., & Imanaka, Y. (2017), Prediction of pneumonia hospitalization in adults using health checkup data, PloS one,12(6),e0180159. https : //doi.org/10.1371/journal.pone.0180159
[21] Juang WC, Huang SJ, Huang FD, Cheng PW, Wann SR. (2017), Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan, BMJ Open, 2017, Dec 1,7(11),e018628. DOI: 10.1136/bmjopen − 2017 − 018628
[22] Harutyunyan, H., Khachatrian, H., Kale, D.C. et al. (2019), Multitask learning and benchmarking with clinical time series data, Sci .Data,6,96(2019).https : //doi.org/10.1038/s41597−019−0103−9
[23] Shivapratap Gopakumar (2016), Truyen Tran, Wei Luo, Dinh Phung, JMIR Medical Informatics 4(3):e25 . DOI: 10.2196/medinform.5650 .
[24] Bui C., Pham N., Vo A., Tran A., Nguyen A., Le T. (2017), Time Series Forecasting for Healthcare Diagnosis and Prognostics with the Focus on Cardiovascular Diseases, Vo Van T.; Nguyen Le T.;
[25] Liew, B.X.W., Peolsson, A., Rugamer, D. et al. (2020), Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach, Sci.Rep,10,16782(2020). https : //doi.org/10.1038/s41598 − 020 − 73740 − 7
[26] Dunsmuir WT (2019), Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models, Behav Res Methods,2016,Jun,48(2),783-802. DOI:10.3758/s13428 − 015 − 0611 − 2
[27] Yang, C., Delcher, C., Shenkman, E. et al. (2019), Expenditure variations analysis using residuals for identifying high health care utilizers in a state Medicaid program, BMC Med Inform Decis Mak, 19,131(2019). https : //doi.org/10.1186/s12911/019/0870/4
[28] Daniel J. Morgan, Bill Bame, Paul Zimand, et al. (2019), Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions, JAMA Netw Open,2019,Mar,2
[29] Marno Verbeek, A Guide to Modern Econometrics, John Wiley & Sons. DOI10.3917/rfs.593.0475. https : //www.researchgate.net/publication/227488993 A Guide to Modern Econometrics
[30] Aitor Lewkowycz and Ethan S Dyer and Guy Gur-Ari and Jascha Sohl-dickstein and Yasaman Bahri (2020) . The large learning rate phase of deep learning, ICLR 2021 Conference . https : //arxiv.org/abs/2003.02218
[31] Liu C, Zhang X, Nguyen TT, et al. (2021), Partial least squares regression and principal component analysis: similarity and differences between two popular variable reduction approaches, General Psychiatry,2022; 35 : e100662(2021). doi: 10.1136/gpsych − 2021 − 100662
[32] Lyall DM, Inskip HM, Mackay D, Deary IJ, McIntosh AM, Hotopf M, Kendrick T, Pell JP, Smith DJ. Low birth weight and features of neuroticism and mood disorder in 83545 participants of the UK Biobank cohort . BJPsych Open. 2016 Jan 28,2(1):38-44. DOI:10.1192/ bjpo.bp.115.002154. PMID : 27703752, PMCID : PMC4995581