Augmenting individualized treatment planning via data-driven clinical role model selection

Personalized treatment planning requires various patient-level considerations, including personal risk factors and contraindications. However, existing algorithms for facilitating treatment planning frequently fail to account for uncertainties in their recommendations arising from the frequent updating of risk-scoring tools. We propose an algorithmic framework called D ata-d r iv e n A ugmentation of Treatment Planning via Clinical Role M odel Generation and S election (DreAMS). DreAMS integrates risk-scoring tools and data-driven optimization to augment treatment planning by identifying clinical role models, that is, low-risk patients whose physiological measurements and medications can inform treatment planning for high-risk patients. The problem of optimally generating clinical role models amidst uncertainty in frequently updated risk-scoring tools can be tractably reformulated by leveraging two data sources: (i) a patient-specific database ensuring actionability and (ii) historical data from risk-scoring tools to mitigate risks of erroneously recommending high-risk role models. We develop greedy and active-learning algorithms to solve this problem and derive complexity bounds. We present a case study using multiple datasets containing patients at risk for atherosclerotic cardiovascular disease (ASCVD). DreAMS effectively augments treatment planning for high-risk patients despite frequent updating of ASCVD risk-scoring tools, selecting role models whose predicted ASCVD risk falls within acceptable levels in over 60 % of high-risk patients and outperforming benchmarks by over 20 % .

• 저널Production and Operations Management
• 토픽Machine Learning in Healthcare · Artificial Intelligence
• DOI10.1177/10591478261470667
• 저자Che-Yi Liao, Esmaeil Keyvanshokooh, Francisco Pasquel, Gian‐Gabriel P. Garcia