Improving longitudinal health data analysis with stochastic models for predicting disease trajectories and optimizing treatment strategies
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Published:
Jun 30, 2023
Keywords:
Diabetes management, Disease trajectories, Longitudinal health data analysis, Stochastic models, Treatment strategies
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Abstract
Longitudinal health data analysis helps diagnose and treat disease. Traditional deterministic models fail to represent longitudinal data's unpredictability and uncertainty, limiting their forecast accuracy and decision-making capacities. This research improves Longitudinal Health Data Analysis by adding stochastic models for disease trajectories and therapy optimization. The research begins with a stochastic model that accounts for the complicated dynamics of illness progression and therapy responses. This model captures individual variability and probability outcomes using patient-specific factors, features, and treatment information. Numerical examples demonstrate the model's practicality. The numerical example shows that the stochastic model may forecast illness trajectories and optimize treatment choices. The model predicts illness development probabilistically, helping understand disease dynamics and identify high-risk patients. Simulating and probabilistically estimating therapeutic interventions optimizes treatment options. Personalized therapy decision-making improves patient outcomes. Longitudinal Health Data Analysis should use stochastic models, the study suggests. These models improve disease prediction, therapy optimization, and personalized healthcare decision-making by capturing variability and uncertainty. Advanced modeling methodologies and real-world data validation are next. The research could change illness management and clinical care
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Hasanah, N., Tarigan, N. ., Amri, S. ., Saodah, S. ., & Sapnita, S. (2023). Improving longitudinal health data analysis with stochastic models for predicting disease trajectories and optimizing treatment strategies. Journal of Intelligent Decision Support System (IDSS), 6(2), 79-87. https://doi.org/10.35335/idss.v6i2.141
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Cetin, K. O., Der Kiureghian, A., & Seed, R. B. (2002). Probabilistic models for the initiation of seismic soil liquefaction. Structural Safety, 24(1), 67–82.
Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., & Iniesta, R. (2021). The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry, 20(2), 154–170.
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Cnaan, A., Laird, N. M., & Slasor, P. (1997). Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Statistics in Medicine, 16(20), 2349–2380.
Cooper, K., Brailsford, S. C., & Davies, R. (2007). Choice of modelling technique for evaluating health care interventions. Journal of the Operational Research Society, 58(2), 168–176.
DiMatteo, M. R., Sherbourne, C. D., Hays, R. D., Ordway, L., Kravitz, R. L., McGlynn, E. A., Kaplan, S., & Rogers, W. H. (1993). Physicians’ characteristics influence patients’ adherence to medical treatment: results from the Medical Outcomes Study. Health Psychology, 12(2), 93.
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Duan, H., Sun, Z., Dong, W., He, K., & Huang, Z. (2019). On clinical event prediction in patient treatment trajectory using longitudinal electronic health records. IEEE Journal of Biomedical and Health Informatics, 24(7), 2053–2063.
Durrleman, S., Pennec, X., Trouvé, A., Braga, J., Gerig, G., & Ayache, N. (2013). Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision, 103, 22–59.
Falkenström, F., Finkel, S., Sandell, R., Rubel, J. A., & Holmqvist, R. (2017). Dynamic models of individual change in psychotherapy process research. Journal of Consulting and Clinical Psychology, 85(6), 537.
Fang, R., Pouyanfar, S., Yang, Y., Chen, S.-C., & Iyengar, S. S. (2016). Computational health informatics in the big data age: a survey. ACM Computing Surveys (CSUR), 49(1), 1–36.
Glare, P., Sinclair, C., Downing, M., Stone, P., Maltoni, M., & Vigano, A. (2008). Predicting survival in patients with advanced disease. European Journal of Cancer, 44(8), 1146–1156.
Gueorguieva, R., & Krystal, J. H. (2004). Move over anova: progress in analyzing repeated-measures data andits reflection in papers published in the archives of general psychiatry. Archives of General Psychiatry, 61(3), 310–317.
Halfon, N., & Hochstein, M. (2002). Life course health development: an integrated framework for developing health, policy, and research. The Milbank Quarterly, 80(3), 433–479.
Hingorani, A. D., van der Windt, D. A., Riley, R. D., Abrams, K., Moons, K. G. M., Steyerberg, E. W., Schroter, S., Sauerbrei, W., Altman, D. G., & Hemingway, H. (2013). Prognosis research strategy (PROGRESS) 4: stratified medicine research. Bmj, 346.
Jeanpierre, L., & Charpillet, F. (2004). Automated medical diagnosis with fuzzy stochastic models: monitoring chronic diseases. Acta Biotheoretica, 52, 291–311.
Johansson, Å., Andreassen, O. A., Brunak, S., Franks, P. W., Hedman, H., Loos, R. J. F., Meder, B., Melén, E., Wheelock, C. E., & Jacobsson, B. (2023). Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification. Journal of Internal Medicine.
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Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018). The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 24(11), 1716–1720.
Lambert, M. J., Hansen, N. B., & Finch, A. E. (2001). Patient-focused research: using patient outcome data to enhance treatment effects. Journal of Consulting and Clinical Psychology, 69(2), 159.
Lee, C., Yoon, J., & Van Der Schaar, M. (2019). Dynamic-deephit: A deep learning approach for dynamic survival analysis with competing risks based on longitudinal data. IEEE Transactions on Biomedical Engineering, 67(1), 122–133.
Li, J., Huang, G. H., Zeng, G., Maqsood, I., & Huang, Y. (2007). An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination. Journal of Environmental Management, 82(2), 173–188.
Lichter, P. R., Musch, D. C., Gillespie, B. W., Guire, K. E., Janz, N. K., Wren, P. A., Mills, M. P. H. R. P., & Group, C. S. (2001). Interim clinical outcomes in the Collaborative Initial Glaucoma Treatment Study comparing initial treatment randomized to medications or surgery. Ophthalmology, 108(11), 1943–1953.
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 6(1), 1–10.
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246.
Mould, D. R., & Upton, R. N. (2012). Basic concepts in population modeling, simulation, and model‐based drug development. CPT: Pharmacometrics & Systems Pharmacology, 1(9), 1–14.
Mullens, W., Damman, K., Testani, J. M., Martens, P., Mueller, C., Lassus, J., Tang, W. H. W., Skouri, H., Verbrugge, F. H., & Orso, F. (2020). Evaluation of kidney function throughout the heart failure trajectory–a position statement from the Heart Failure Association of the European Society of Cardiology. European Journal of Heart Failure, 22(4), 584–603.
Nagin, D. S., & Odgers, C. L. (2010). Group-based trajectory modeling in clinical research. Annual Review of Clinical Psychology, 6, 109–138.
Ni, Y. Q., Wang, Y. W., & Zhang, C. (2020). A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data. Engineering Structures, 212, 110520.
Noblet, C., Duthy, M., Coste, F., Saliou, M., Samain, B., Drouet, F., Papazyan, T., & Moreau, M. (2022). Implementation of volumetric-modulated arc therapy for locally advanced breast cancer patients: Dosimetric comparison with deliverability consideration of planning techniques and predictions of patient-specific QA results via supervised machine learning. Physica Medica, 96, 18–31.
Perveen, S., Shahbaz, M., Saba, T., Keshavjee, K., Rehman, A., & Guergachi, A. (2020). Handling irregularly sampled longitudinal data and prognostic modeling of diabetes using machine learning technique. IEEE Access, 8, 21875–21885.
Prideaux, A. R., Song, H., Hobbs, R. F., He, B., Frey, E. C., Ladenson, P. W., Wahl, R. L., & Sgouros, G. (2007). Three-dimensional radiobiologic dosimetry: Application of radiobiologic modeling to patient-specific 3-dimensional imaging–based internal dosimetry. Journal of Nuclear Medicine, 48(6), 1008–1016.
Robinson, I. (1990). Personal narratives, social careers and medical courses: analysing life trajectories in autobiographies of people with multiple sclerosis. Social Science & Medicine, 30(11), 1173–1186.
Rogers, J. A., Polhamus, D., Gillespie, W. R., Ito, K., Romero, K., Qiu, R., Stephenson, D., Gastonguay, M. R., & Corrigan, B. (2012). Combining patient-level and summary-level data for Alzheimer’s disease modeling and simulation: a beta regression meta-analysis. Journal of Pharmacokinetics and Pharmacodynamics, 39, 479–498.
Services, U. S. D. of H. and H. (2010). Multiple chronic conditions—a strategic framework: optimum health and quality of life for individuals with multiple chronic conditions. Washington, DC: US Department of Health and Human Services, 2.
Shear, M. K., Reynolds, C. F., Simon, N. M., Zisook, S., Wang, Y., Mauro, C., Duan, N., Lebowitz, B., & Skritskaya, N. (2016). Optimizing treatment of complicated grief: A randomized clinical trial. JAMA Psychiatry, 73(7), 685–694.
Shilo, S., Rossman, H., & Segal, E. (2020). Axes of a revolution: challenges and promises of big data in healthcare. Nature Medicine, 26(1), 29–38.
Smith, C. D., Balatbat, C., Corbridge, S., Dopp, A. L., Fried, J., Harter, R., Landefeld, S., Martin, C. Y., Opelka, F., & Sandy, L. (2018). Implementing optimal team-based care to reduce clinician burnout. Nam Perspectives, 8(9), 1–13.
Smith, G. D. (2011). Epidemiology, epigenetics and the ‘Gloomy Prospect’: embracing randomness in population health research and practice. International Journal of Epidemiology, 40(3), 537–562.
Stirrup, O. T., Babiker, A. G., & Copas, A. J. (2016). Combined models for pre-and post-treatment longitudinal biomarker data: an application to CD4 counts in HIV-patients. BMC Medical Research Methodology, 16(1), 1–21.
Stone, A., Shiffman, S., Atienza, A., & Nebeling, L. (2007). The science of real-time data capture: Self-reports in health research. Oxford University Press.
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