Martina Doneda

@unibg.it

PostDoc at Department of Production, Management and Information Engineering
University of Bergamo

Martina Doneda


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https://researchid.co/martinadoneda

RESEARCH, TEACHING, or OTHER INTERESTS

Management Science and Operations Research, Biomedical Engineering
15

Scopus Publications

55

Scholar Citations

4

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Operations Research for Pediatric Elective Surgery Planning: Example of a Mathematical Model
    Martina Doneda, Sara Costanzo, Giuliana Carello, Amulya Kumar Saxena, Gloria Pelizzo
    Bioengineering, 2026
    The management of operating rooms (ORs) is one of the most studied topics in operations research applied to healthcare. In particular, scheduling elective surgeries in a pediatric and teaching hospital can be a challenge because disruptions occur frequently. The aim of our research was to create a mathematical programming model to schedule day hospital (DH) patients, considering possible disruptions and defining how to best manage the rescheduling process. Our study originates from a collaboration between a high-volume pediatric surgery department and operations research practitioners. The possible disruptions we consider are emergencies and same-day cancellations of planned hospital operations. Elective DH surgeries are scheduled considering the waiting list and the patients’ clinical priorities, generating a nominal schedule. This schedule is optimized in conjunction with a series of back-up schedules to guarantee that OR activity immediately recovers in case of a disruption. An ILP-based approach to the problem is proposed. We enumerate a representative subset of the possible emergency and no-show scenarios, and for each of them a back-up plan is designed. The approach reschedules patients, minimizing disruptions with respect to the nominal schedule, and applies an as-soon-as-possible policy in case of emergencies to ensure that all patients receive timely care. The approach is shown to be effective in managing disruptions, ensuring that the waiting list is managed properly, with a balanced mix of urgent and less urgent patients. It provides an effective solution for scheduling patients in a pediatric hospital, considering the unique features of such facilities.
  • Robust personnel rostering: How accurate should absenteeism predictions be?
    Martina Doneda, Pieter Smet, Giuliana Carello, Ettore Lanzarone, Greet Vanden Berghe
    Journal of Scheduling, 2026
  • On data & decision-making: perspectives from healthcare applications considering representativeness and analytical scope
    Martina Doneda
    4or, 2026
  • A Discrete-Event Simulation Model to Configure Operating Rooms for Robotic Cardiac Surgery
    Martina Doneda, Elena Elzi, Alfonso Agnino, Ascanio Graniero, Laura Giroletti, Matteo Parrinello, Giovanni Albano, Gabriele Tunesi, Ettore Lanzarone
    Mdm Policy and Practice, 2026
    Background. Robotic cardiac surgery (RCS) has emerged as a promising alternative in clinical practice to overcome the limitations of minimally invasive techniques. However, the integration of RCS with surgical process management is key in taking full advantage of its benefits. Aim. We assess the performance of RCS interventions as a function of operating room (OR) layout, using a discrete-event simulation (DES) tool, which allows the simulation of different RCS procedures in different layouts. Methods. A DES model was developed for 2 types of RCS, atrial fibrillation ablation and mitral valve repair, to analyze them in the presence of different OR layouts. Data on the activities and timings of all operators in the OR, used to feed the DES, were collected on site at Humanitas Gavazzeni Hospital, Bergamo, Italy, through direct recording during RCS procedures. Results. The advantages and disadvantages of different OR layouts were highlighted and quantified through a series of key performance indicators and qualitative outcomes, including the overall duration of the entire surgical process, the distance covered by the surgical team, and their utilization. Specifically, the characteristics of a new, larger OR in the considered hospital were assessed prior to the actual transfer of the RCS department in the new OR. Conclusion. This work provided valuable insights and recommendations to RCS operators, which were put in practice, specifically tailoring OR configurations to RCS procedural characteristics. Highlights Discrete event simulation (DES) is used for the first time to improve the performance of robotic cardiac surgery (RCS), an application that presents unique challenges. The flexible DES model for RCS can parametrize various factors related to both operating rooms and procedures. The impact of these factors is evaluated on a set of KPIs. New insights into the positioning of equipment and personnel in the OR are provided, allowing to formulate informed recommendations for RCS providers.
  • A decision support tool for the location, districting and dimensioning of Community Health Houses
    Martina Doneda, Ettore Lanzarone, Carlotta Franchi, Sara Mandelli, Angelo Barbato, Alessandro Nobili, Giuliana Carello
    Health Care Management Science, 2025
    Community Health Houses (CHHs) are new entities in the Italian National Health Service that have been envisaged to provide proximity care to an increasingly aging population, and bear some similarities to other facilities in countries that have historically focused on public healthcare. This work proposes an integrated decision support system (DSS) for their planning, envisioned during the aftermath of the COVID-19 pandemic, which highlighted the frailty of the existing system. The DSS is based on an integer linear programming (ILP) model that simultaneously makes location, districting and dimensioning decisions for CHH, and accounts for accessibility and equity requirements. Based on Italian law yet designed in a parametrized way that makes it adaptable to several contexts, the DSS is able to design a hub and spoke network, which considers the provision of both mandatory and additional services. The sizes of the former are determined by directly taking into account the population served, while those of the latter are determined according to the specific demand for these services, accounting for diverse needs arising from different territories. The DSS also uses territorial units that refer to recognizable administrative areas. This ensures that the districting is easily recognized and accepted by the population. In addition to the ILP formulation, three decomposition-based matheuristics are proposed, which allow suitable solutions to be found within a reasonable time also for large and heterogeneous instances, while maintaining the flexibility of the ILP formulation. Computational results on synthetic realistic instances validated the DSS, while its application to a real-life case in a Northern Italian province demonstrated the effectiveness of the heuristic approaches and provided a proof of concept for its practical application.
  • A Machine Learning Tool to Predict Survival After First Surgery in Peripheral Artery Disease Patients
    Martina Doneda, Ettore Lanzarone, Fabio Riccardo Pisa, Bianca Pane, Giovanni Pratesi, Giovanni Spinella
    Journal of Cardiovascular Translational Research, 2025
    The aim of this study was to develop and validate a machine learning tool for predicting survival in PAD patients who received surgical treatment. We used the data from 1,615 patients who underwent PAD surgery from 2005 to 2020. Gradient boosted decision trees (GBDTs) were used to predict mortality at one, three and five years after the first surgery, while predictor importance was assessed using the SHAP values method. The area under the curve (AUC) of the receiver operating characteristic curve of the one-, three and five-year prediction models were 0.86, 0.84 and 0.80, respectively. Disease stage was the most important predictor, along with age, chronic kidney disease status, hospital length-of-stay and total number of comorbidities. Presence of dyslipidemia was slightly predictive of one- and three-year mortality. Simple clinical and demographic parameters can be used to train a GBDT model capable of predicting PAD follow-up mortality. Graphical Abstract
  • Exploring mortality representation and the impact of COVID-19 in Modena: insights from “An ECG-based machine-learning approach for mortality risk assessment in a large European population”
    P. Giovanardi, C. Vernia, M. Doneda
    Journal of Electrocardiology, 2025
  • An ECG-based machine-learning approach for mortality risk assessment in a large European population
    Martina Doneda, Ettore Lanzarone, Claudio Giberti, Cecilia Vernia, Andi Vjerdha, Federico Silipo, Paolo Giovanardi
    Journal of Electrocardiology, 2025
    AIMS: Through a simple machine learning approach, we aimed to assess the risk of all-cause mortality after 5 years in a European population, based on electrocardiogram (ECG) parameters, age, and sex. METHODS: The study included patients between 40 and 90 years old who underwent ECG recording between January 2008 and October 2022 in the metropolitan area of Modena, Italy. Exclusion criteria established a patient cohort without severe ECG abnormalities, namely, tachyarrhythmias, bradyarrhythmias, Wolff-Parkinson-White syndrome, second- or third- degree AV block, bundle-branch blocks, more than three premature beats, poor signal quality, and presence of pacemakers and implantable cardioverter- defibrillators. Mortality was assessed using a set of logistic regression models, differentiated by age group, to which the Akaike Information Criterion was applied. Model fitting was evaluated using confusion matrix-related performance metrics, the area under the receiver operating characteristic (ROC) curve (AUC), and the predictive significance against the no-information rate (NIR). RESULTS: 53692 patients were enrolled, of whom 14353 (26.73 %) died within 5 years of ECG registration. The logistic regression model distinguished between those who died and those who survived based on the predicted mortality probability for all age groups, obtaining a significant difference between the predicted mortality and the NIR in 14 of the 55 age groups. Good accuracy and performance metrics were observed, resulting in an average AUC of 0.779. CONCLUSIONS: The proposed model showed a good predictive performance in patients without severe ECG abnormalities. Therefore, this study highlights the potential of ECGs as prognostic rather than diagnostic tools.
  • A three-stage matheuristic for home blood donation appointment reservation and collection routing
    Martina Doneda, Semih Yalçındağ, Ettore Lanzarone
    Flexible Services and Manufacturing Journal, 2024
    In Western countries, the so-called Blood Donation Supply Chain (BDSC) provides blood units to several health services. Its first echelon is the collection of unit from donors, which requires a careful management because an unbalanced supply of units to the rest of the chain could trigger alternating periods of blood shortage and wastage. However, the management of blood collection is only marginally studied in the literature, in comparison to other BDSC echelons. In this work, we propose a new organizational model for blood collection, in which blood is collected at donor’s homes, and provide a decision support tool for its management. This new model provides a novel contribution to the understudied blood collection echelon and, at the same time, it responds to the emerging need of delocalization of health services. The proposed decision support tool consists of an interconnected matheuristic framework with three decision stages: (i) a planning model to create the donation slots that will be assigned to donors, (ii) an online allocation of these slots using a flexible set of criteria, and (iii) a Multi-Trip Vehicle Routing Problem with Time Windows (MTVRP-TW) to route the bloodmobiles that collect blood at donors’ homes. The main goals are to balance the production of blood units between days and to minimize the distance travelled by the bloodmobile fleet, while respecting time windows negotiated with donors. This framework also has the feature of immediately providing a list of slots to choose from when a donor makes a booking request. The decision support tool has been tested on data from a real Italian provider. Results confirm its effectiveness, and the capability of providing good quality and economically sustainable solutions in reasonable timeframes.
  • Surgical planning of arteriovenous fistulae in routine clinical practice: A machine learning predictive tool
    Martina Doneda, Sofia Poloni, Michela Bozzetto, Andrea Remuzzi, Ettore Lanzarone
    Journal of Vascular Access, 2024
    Background: Arteriovenous fistula (AVF) is the preferred vascular access (VA) for hemodialysis, but it is associated with high non-maturation and failure rates. Predicting patient-specific AVF maturation and postoperative changes in blood flow volumes (BFVs) and vessel diameters is of fundamental importance to support the choice of optimal AVF location and improve VA survival. The goal of this study was to employ machine learning (ML) in order to give physicians a fast and easy-to-use tool that provides accurate patient-specific predictions, useful to make AVF surgical planning decisions. Methods: We applied a set of ML approaches on a dataset of 156 patients. Both parametric and non-parametric ML approaches, taking preoperative data as input, were exploited to predict maturation, postoperative BFVs, and diameters. The best approach associated with lowest cross-validation errors between predictions and real measurements was then chosen to provide estimates and quantify prediction errors. Results: The k-NN was the best approach to predict brachial BFV, AVF maturation, and other VA variables, and it was also associated with the least computational effort. With this approach, the confusion matrices proved the high accuracy of the prediction for AVF maturation (96.8%) and the low absolute error distribution for the continuous BFV and diameter variables. Conclusions: Our data-based approach provided accurate patient-specific predictions for different AVF configurations, requiring short computational time as compared to a physical model we previously developed. By supporting VA surgical planning, this fast computing approach could allow AVF surgical planning and help reducing the rate of non-maturation, which might ultimately have a broad impact on the management of hemodialysis patients.
  • Predicting employee absenteeism to generate robust rosters
    Proceedings of the 14th International Conference on the Practice and Theory of Automated Timetabling Patat 2024, 2024
  • The Predictive Effects of Resting-State and Task-Related Prefrontal and Vagal Activity on Cognitive Performances Results From a Machine Learning Based Approach
    Martina Doneda, Virginia Maria Borsa, Agostino Brugnera, Angelo Compare, Maria Luisa Rusconi, Kaoru Sakatani, Ettore Lanzarone
    Journal of Psychophysiology, 2024
  • A Comparison of Fairness Metrics for Health Care Problems
    Martina Doneda, Ettore Lanzarone, Giuliana Carello
    Airo Springer Series, 2023
  • Using emotional text mining to assess the culture of blood donation in Italy
    Silvia Monaco, Martina Doneda, Ettore Lanzarone, Rachele Mariani
    Psicologia Della Salute, 2023
  • A discrete-event simulation model for analysing and improving operations in a blood donation centre
    Martina Doneda, Semih Yalçındağ, Inês Marques, Ettore Lanzarone
    Vox Sanguinis, 2021

RECENT SCHOLAR PUBLICATIONS

  • Operations Research for Pediatric Elective Surgery Planning: Example of a Mathematical Model
    M Doneda, S Costanzo, G Carello, AK Saxena, G Pelizzo
    Bioengineering 13 (2), 186 , 2026
    2026
  • A Discrete-Event Simulation Model to Configure Operating Rooms for Robotic Cardiac Surgery
    M Doneda, E Elzi, A Agnino, A Graniero, L Giroletti, M Parrinello, ...
    MDM Policy & Practice 11 (1), 23814683251410133 , 2026
    2026
  • Robust personnel rostering: how accurate should absenteeism predictions be?
    M Doneda, P Smet, G Carello, E Lanzarone, G Vanden Berghe
    Journal of Scheduling 29 (1), 67-82 , 2026
    2026
    Citations: 8
  • An ILP approach to urgent patient management in an orthopedic department
    M Doneda, A Brigatti, S Goglio, S Scetti, F Chiodini, L Grion, G Carello, ...
    10th AIROYoung Workshop and Ph. D. School. Optimization between determinism … , 2026
    2026
  • A decision support tool for the location, districting and dimensioning of Community Health Houses
    M Doneda, E Lanzarone, C Franchi, S Mandelli, A Barbato, A Nobili, ...
    Health Care Management Science, 1-24 , 2025
    2025
  • A data-driven tool for operating rooms advance scheduling
    M Doneda, G Gabrielli, G Carello
    Book of Abstracts, 60 , 2025
    2025
  • A machine learning tool to predict survival after first surgery in peripheral artery disease patients
    M Doneda, E Lanzarone, FR Pisa, B Pane, G Pratesi, G Spinella
    Journal of Cardiovascular Translational Research 18 (5), 1459-1469 , 2025
    2025
  • Prediction accuracy versus rescheduling flexibility in elective surgery management
    P Smet, M Doneda, E Lanzarone, G Carello
    arXiv preprint arXiv:2507.15566 , 2025
    2025
  • Trade-offs between elective surgery rescheduling and length-of-stay prediction accuracy
    P Smet, M Doneda, E Lanzarone, G Carello
    arXiv e-prints, arXiv: 2507.15566 , 2025
    2025
  • A decision support tool for the location, districting and dimensioning problem of Community Health Houses
    M Doneda, E Lanzarone, C Franchi, S Mandelli, A Barbato, A Nobili, ...
    On data & decision-making: perspectives from healthcare applications … , 2025
    2025
  • A data-driven approach for robust operating theater scheduling
    G Gabrielli, M Doneda, G Carello
    On data & decision-making: perspectives from healthcare applications … , 2025
    2025
  • Elective surgery scheduling considering length-of-stay estimation: evaluating the impact of predictive error through simulation
    P Smet, M Doneda, G Carello, E Lanzarone
    On data & decision-making: perspectives from healthcare applications … , 2025
    2025
  • An ILP-based tool for managing operating room scheduling in the orthopedics department
    M Doneda, S Goglio, S Scetti, F Chiodini, L Grion, G Carello, E Lanzarone, ...
    HCSE 2025. 7th International Conference on Health Care Systems Engineering … , 2025
    2025
  • Simulated machine learning: evaluating the impact of predictive error on healthcare planning
    M Doneda, P Smet, E Lanzarone, G Carello
    HCSE 2025. 7th International Conference on Health Care Systems Engineering … , 2025
    2025
  • Elective surgery planning considering length-of-stay: Evaluating the role of prediction accuracy and rescheduling policies
    M Doneda, P Smet, E Lanzarone, G Carelllo
    Book of Abstracts, 173 , 2025
    2025
  • Simulating the impact of errors on length-of-stay predictions and rescheduling policies on elective surgery planning
    M Doneda, P Smet, E Lanzarone, G Carello
    Conference abstract book of the 51st meeting of the EURO Working Group on … , 2025
    2025
  • Robust operating room scheduling with probabilistic regression
    M Doneda, G Gabrielli, G Carello
    9th AIROYoung Wokshop , 2025
    2025
  • Exploring mortality representation and the impact of COVID-19 in Modena: insights from “An ECG-based machine-learning approach for mortality risk assessment in a large European …
    P Giovanardi, C Vernia, M Doneda
    Journal of Electrocardiology 90, 1-4 , 2025
    2025
  • An ECG-based machine-learning approach for mortality risk assessment in a large European population
    M Doneda, E Lanzarone, C Giberti, C Vernia, A Vjerdha, F Silipo, ...
    Journal of Electrocardiology 88, 153850 , 2025
    2025
    Citations: 6
  • A three-stage matheuristic for home blood donation appointment reservation and collection routing: M. Doneda et al.
    M Doneda, S Yalçındağ, E Lanzarone
    Flexible Services and Manufacturing Journal 36 (4), 1222-1252 , 2024
    2024
    Citations: 4

MOST CITED SCHOLAR PUBLICATIONS

  • A discrete‐event simulation model for analysing and improving operations in a blood donation centre
    M Doneda, S Yalçındağ, I Marques, E Lanzarone
    Vox sanguinis 116 (10), 1060-1075 , 2021
    2021
    Citations: 25
  • Robust personnel rostering: how accurate should absenteeism predictions be?
    M Doneda, P Smet, G Carello, E Lanzarone, G Vanden Berghe
    Journal of Scheduling 29 (1), 67-82 , 2026
    2026
    Citations: 8
  • Surgical planning of arteriovenous fistulae in routine clinical practice: a machine learning predictive tool
    M Doneda, S Poloni, M Bozzetto, A Remuzzi, E Lanzarone
    The Journal of Vascular Access 25 (4), 1170-1179 , 2024
    2024
    Citations: 8
  • An ECG-based machine-learning approach for mortality risk assessment in a large European population
    M Doneda, E Lanzarone, C Giberti, C Vernia, A Vjerdha, F Silipo, ...
    Journal of Electrocardiology 88, 153850 , 2025
    2025
    Citations: 6
  • A three-stage matheuristic for home blood donation appointment reservation and collection routing: M. Doneda et al.
    M Doneda, S Yalçındağ, E Lanzarone
    Flexible Services and Manufacturing Journal 36 (4), 1222-1252 , 2024
    2024
    Citations: 4
  • A disruption-restoration-based MILP model for elective surgical scheduling in a children's hospital using scenarios
    M Doneda, G Pelizzo, S Costanzo, G Carello
    arXiv preprint arXiv:2408.12518 , 2024
    2024
    Citations: 2
  • The predictive effects of resting-state and task-related prefrontal and vagal activity on cognitive performances
    M Doneda, VM Borsa, A Brugnera, A Compare, ML Rusconi, K Sakatani, ...
    Journal of Psychophysiology , 2023
    2023
    Citations: 2
  • Operations Research for Pediatric Elective Surgery Planning: Example of a Mathematical Model
    M Doneda, S Costanzo, G Carello, AK Saxena, G Pelizzo
    Bioengineering 13 (2), 186 , 2026
    2026
  • A Discrete-Event Simulation Model to Configure Operating Rooms for Robotic Cardiac Surgery
    M Doneda, E Elzi, A Agnino, A Graniero, L Giroletti, M Parrinello, ...
    MDM Policy & Practice 11 (1), 23814683251410133 , 2026
    2026
  • An ILP approach to urgent patient management in an orthopedic department
    M Doneda, A Brigatti, S Goglio, S Scetti, F Chiodini, L Grion, G Carello, ...
    10th AIROYoung Workshop and Ph. D. School. Optimization between determinism … , 2026
    2026
  • A decision support tool for the location, districting and dimensioning of Community Health Houses
    M Doneda, E Lanzarone, C Franchi, S Mandelli, A Barbato, A Nobili, ...
    Health Care Management Science, 1-24 , 2025
    2025
  • A data-driven tool for operating rooms advance scheduling
    M Doneda, G Gabrielli, G Carello
    Book of Abstracts, 60 , 2025
    2025
  • A machine learning tool to predict survival after first surgery in peripheral artery disease patients
    M Doneda, E Lanzarone, FR Pisa, B Pane, G Pratesi, G Spinella
    Journal of Cardiovascular Translational Research 18 (5), 1459-1469 , 2025
    2025
  • Prediction accuracy versus rescheduling flexibility in elective surgery management
    P Smet, M Doneda, E Lanzarone, G Carello
    arXiv preprint arXiv:2507.15566 , 2025
    2025
  • Trade-offs between elective surgery rescheduling and length-of-stay prediction accuracy
    P Smet, M Doneda, E Lanzarone, G Carello
    arXiv e-prints, arXiv: 2507.15566 , 2025
    2025
  • A decision support tool for the location, districting and dimensioning problem of Community Health Houses
    M Doneda, E Lanzarone, C Franchi, S Mandelli, A Barbato, A Nobili, ...
    On data & decision-making: perspectives from healthcare applications … , 2025
    2025
  • A data-driven approach for robust operating theater scheduling
    G Gabrielli, M Doneda, G Carello
    On data & decision-making: perspectives from healthcare applications … , 2025
    2025
  • Elective surgery scheduling considering length-of-stay estimation: evaluating the impact of predictive error through simulation
    P Smet, M Doneda, G Carello, E Lanzarone
    On data & decision-making: perspectives from healthcare applications … , 2025
    2025
  • An ILP-based tool for managing operating room scheduling in the orthopedics department
    M Doneda, S Goglio, S Scetti, F Chiodini, L Grion, G Carello, E Lanzarone, ...
    HCSE 2025. 7th International Conference on Health Care Systems Engineering … , 2025
    2025
  • Simulated machine learning: evaluating the impact of predictive error on healthcare planning
    M Doneda, P Smet, E Lanzarone, G Carello
    HCSE 2025. 7th International Conference on Health Care Systems Engineering … , 2025
    2025