Seminar M.Sc Students
Predicting User Decisions in DSS-Assisted Signal Detection
Omri Bouchnick, M.Sc. student in the School of Industrial & Intelligent Systems Engineering
Advisor: Prof. Joachim Meyer &Mr. Yoav Ben Yaakov
Abstract:
Can single decisions be predicted in a Decision Support System (DSS) context? This thesis addresses this question through a controlled signal-detection experiment. A large-scale online study was conducted (N=569 participants, 3x11x11 factorial design) where users classified noisy stimuli as "Signal" or "Noise" while receiving DSS recommendations of varying reliability. Machine learning models were then trained to predict individual decisions using information available at decision time, including current-trial features and behavioral history.
The models achieved strong predictive performance: an XGBoost classifier reached 86.3% accuracy (AUC=0.937) for within-user prediction and 83.3% accuracy (AUC=0.910) when generalizing to entirely new users - substantially outperforming baseline heuristics. Feature importance analysis (SHAP) revealed that the DSS recommendation was the dominant predictor, followed by stimulus evidence and the known sensitivities of the DSS and human. Notably, removing behavioral history features reduced accuracy by only 2%, indicating that decisions are driven primarily by immediate context rather than past behavior.
Behavioral analyses provided additional insight: users followed the DSS on 76.6% of trials, and override decisions were significantly slower than follows, suggesting that disagreement requires cognitive effort rather than reflecting snap judgments. Despite reasonable performance (75.6% accuracy), participants left substantial value on the table: 60% would have done better by simply always following the DSS, and all fell far short of the optimal 86.2% achievable by integrating both DSS and stimulus evidence. These findings demonstrate that human-DSS decisions are highly predictable, supporting the design of adaptive interfaces that intervene at moments of likely conflict while highlighting persistent inefficiencies in human reliance strategies.
Bio:
Omri Bouchnick is an M.Sc. candidate in Industrial Engineering at Tel Aviv University and a Business-Data Science Analyst. His research focuses on human-automation interaction, trust calibration, and decision-making under uncertainty.
Contact:
E-Mail: bouchnick@mail.tau.ac.il
LinkedIn: https://www.linkedin.com/in/omri-bo/
Modeling Algorithmic Decision Support for Femur-Fracture Prevention Surgery in Prostate Cancer Patients
Snir David Malka, M.Sc. student in the School of Industrial & Intelligent Systems Engineering
Advisor: Prof. Joachim Meyer &Mr. Yoav Ben Yaakov
Abstract:
Bone metastases from prostate cancer frequently involve the femur and can lead to a pathologic fracture, an event associated with loss of function, urgent surgery, and likely morbidity. Clinicians therefore face a difficult decision: whether to recommend prophylactic femoral fixation to prevent fracture, or to continue surveillance. This decision is complicated by uncertainty in individual fracture risk, competing mortality risk, surgical complications, and the time-varying nature of both cancer progression and functional status. This research develops a decision-support model designed to evaluate the benefit of an algorithmic system that predicts impending fractures. We compare clinical outcomes under “wait” versus “fix” strategies, with and without algorithmic fracture risk predictions, and quantify how the system's value changes as a function of patient characteristics and system properties. The model explicitly represents key pathways (fracture, postoperative recovery trajectories, and death) and evaluates outcomes in terms of QALYs (Quality Adjusted Life Year) and economic impact. The goal is to clarify when and for whom a predictive algorithm meaningfully improves decisions and outcomes.
Bio:
Snir David Malka is an M.Sc. student in the School of Industrial and Intelligent Systems Engineering at Tel Aviv University, supervised by Prof. Joachim Meyer. His research focuses on decision-support modeling in healthcare, with an emphasis on dynamic, patient-specific clinical decisions under uncertainty. In his thesis, Snir develops a Markov decision process framework to guide prophylactic fixation decisions for prostate cancer patients with femoral metastases.
E-Mail: snirmalka123@gmail.com
LinkedIn: https://il.linkedin.com/in/snir-malka/
