Category: Publications

Liu, Z., Fauvel, C., Lin, S., Correa-Jaque, P., Webb, A., Vanderpool, R., Kanwar, M., Kraisangka, J., Mathur, P., Perer, A., Everett, A., & Benza, R. L. “Pulmonary Arterial Hypertension Risk Assessment model using Random Forrest and Bayesian Network”. Background and rationale: Existing risk assessment models in pulmonary arterial hypertension (PAH) are powerful in predicating survival. […]

Scott, J.V., Garnett, C.E., Kanwar, M.K., Stockbridge, N.L., Benza, R.L. Enrichment Benefits of Risk Algorithms for Pulmonary Arterial Hypertension Clinical Trials Am J Respir Crit Care Med. Sep 16, 2020 Abstract Rationale: Event-driven primary endpoints are increasingly used in pulmonary arterial hypertension clinical trials, substantially increasing required sample sizes and trial lengths. The U.S. Food […]

Kanwar MK, Gomberg-Maitland M, Hoeper M, Pausch C, Pittow D, Strange G, Anderson J, Zhao C, Scott J, Druzdzel M, Kraisangka J, Lohmueller L, Antaki J, Benza R. Risk stratification in pulmonary arterial hypertension using Bayesian analysis. Eur Respir J. Accepted April 21, 2020. Abstract Background Current risk stratification tools in pulmonary arterial hypertension (PAH) […]

Kraisangka J., Druzdzel M.J., Lohmueller L.C., Kanwar M.K., Antaki J.F., Benza R.L. (2019) Bayesian Network vs. Cox’s Proportional Hazard Model of PAH Risk: A Comparison. In: Riaño D., Wilk S., ten Teije A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science, vol 11526. Springer, Cham Abstract Pulmonary arterial hypertension (PAH) is […]

Kanwar M, Raina A, Lohmueller L, Kraisangka J, Benza R. Current Hypertension Reports. 2019 Apr 25;21(6):45. doi: 10.1007/s11906-019-0950-y. Review. PMID: 31025123 Abstract PURPOSE OF REVIEW: Pulmonary arterial hypertension (PAH) is a chronic, progressive, and incurable disease with significant morbidity and mortality. Despite increasingly available treatment options, PAH patients continue to experience disease progression and increased […]

Kraisangka J, Druzdzel MJ. A Bayesian Network Interpretation of the Cox’s Proportional Hazard Model. Int J Approx Reason. 2018;103:195-211 Abstract Cox’s proportional hazards (CPH) model is quite likely the most popular modeling technique in survival analysis. While the CPH model is able to represent a relationship between a collection of risks and their common effect, […]