عنوان انگلیسی مقاله:
Decision analysis and reinforcement learning in surgical decision-making
ترجمه فارسی عنوان مقاله:
تجزیه و تحلیل تصمیم گیری و یادگیری تقویت در تصمیم گیری جراحی
Sciencedirect - Elsevier - Surgery, 168 (2020) 253-266. doi:10.1016/j.surg.2020.04.049
Tyler J. Loftus, MDa, Amanda C. Filiberto, MDa, Yanjun Li, MSb, Jeremy Balch, MDa, Allyson C. Cook, MDc, Patrick J. Tighe, MDd, Philip A. Efron, MDa, Gilbert R. Upchurch Jr., MDa, Parisa Rashidi, PhDe,f, Xiaolin Li, PhDb, Azra Bihorac, MDc,f,*
Background: Surgical patients incur preventable harm from cognitive and judgment errors made under
time constraints and uncertainty regarding patients’ diagnoses and predicted response to treatment.
Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges
but are poorly understood and rarely used clinically. This review seeks to promote an understanding of
decision analysis and reinforcement learning by describing their use in the context of surgical decisionmaking.
Methods: Cochrane, EMBASE, and PubMed databases were searched from their inception to June 2019.
Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and
machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting
Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.
Results: Requirements for time-consuming manual data entry and crude representations of individual
patients and clinical context compromise many traditional decision-support tools. Decision analysis
methods for calculating probability thresholds can inform population-based recommendations that
jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered
decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a
large set of patient-specific input data to identify actions yielding the greatest probability of achieving a
goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages
for personalized, patient-centered decision-making. Clinical application would require secure
integration of multiple data sources and attention to ethical considerations regarding liability for errors
and individual patient preferences.
Conclusion: Traditional decision-support tools are ill-equipped to accommodate time constraints and
uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair
surgical decision-making. Decision analysis and reinforcement learning have the potential to play
complementary roles in delivering high-value surgical care through sound judgment and optimal decision-