I am currently involved in teaching the laboratory sessions of the Foundations of Data Science course during the Fall semester. In the Spring semester, I am available for Master’s thesis supervision.

My proposed thesis topics focus on the application of machine learning to dyadic interactions in psychotherapy, cancer development, and data mining from electronic health records (EHRs). However, I am also open to supervising projects in other areas, provided the student demonstrates a strong interest and commitment to the chosen topic.

Some of the previous thesis projects that I supervised or co-supervised in the past years:

• Predicting the Onset of Metastasis by Multimodal Machine Learning in Lung cancer and Melanoma patients
• Evaluating Explainability Techniques for Machine Learning in Healthcare

• Predicting Chronic Kidney Disease in Jalisco - An XAI-Enhanced Multimodal Approach with Geospatial and Demographic Data

• Predicting Symptom Change in Functional Somatic Syndromes through Multimodal Machine Learning and Explainability

• Integrating Genetics and Epigenetics for Predicting the Mutational Landscape in Lung Adenocarcinoma

My research focuses on the development and application of machine learning methods in healthcare and psychotherapy. I am particularly interested in dyadic interactions in psychotherapy, where I explore how machine learning can capture and model the complex dynamics between patients and therapists. In parallel, I apply machine learning to Electronic Health Records (EHRs) to predict disease progression, aiming to support early intervention and improve clinical outcomes.

A central theme of my work is the design of novel explainability methods that make machine learning models more transparent and trustworthy for practitioners. Across these domains, I focus on multimodal data and the modelling of interactions between modalities, as I believe that integrating diverse sources of information provides deeper insights and more robust predictions.

A selection from Stockholm University publication database

• A Workflow for Generating Patient Counterfactuals in Lung Transplant Recipients

  2023. Franco Rugolon, Maria Bampa, Panagiotis Papapetrou. Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 291-306

  Conference

  Lung transplantation is a critical procedure performed in end-stage pulmonary patients. The number of lung transplantations performed in the USA in the last decade has been rising, but the survival rate is still lower than that of other solid organ transplantations. First, this study aims to employ machine learning models to predict patient survival after lung transplantation. Additionally, the aim is to generate counterfactual explanations based on these predictions to help clinicians and patients understand the changes needed to increase the probability of survival after the transplantation and better comply with normative requirements. We use data derived from the UNOS database, particularly the lung transplantations performed in the USA between 2019 and 2021. We formulate the problem and define two data representations, with the first being a representation that describes only the lung recipients and the second the recipients and donors. We propose an explainable ML workflow for predicting patient survival after lung transplantation. We evaluate the workflow based on various performance metrics, using five classification models and two counterfactual generation methods. Finally, we demonstrate the potential of explainable ML for resource allocation, predicting patient mortality, and generating explainable predictions for lung transplantation.

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• A Workflow for Creating Multimodal Machine Learning Models for Metastasis Predictions in Melanoma Patients

  2025. Franco Rugolon (et al.). Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 87-102

  Conference

  Melanoma is the most common form of skin cancer, responsible for thousands of deaths annually. Novel therapies have been developed, but metastases are still a common problem, increasing the mortality rate and decreasing the quality of life of those who experience them. As traditional machine learning models for metastasis prediction have been limited to the use of a single modality, in this study we aim to explore and compare different unimodal and multimodal machine learning models to predict the onset of metastasis in melanoma patients to help clinicians focus their attention on patients at a higher risk of developing metastasis, increasing the likelihood of an earlier diagnosis.

  We use a patient cohort derived from an Electronic Health Record, and we consider various modalities of data, including static, time series, and clinical text. We formulate the problem and propose a multimodal ML workflow for predicting the onset of metastasis in melanoma patients. We evaluate the performance of the workflow based on various classification metrics and statistical significance. The experimental findings suggest that multimodal models outperform the unimodal ones, demonstrating the potential of multimodal ML to predict the onset of metastasis.

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• MASICU: A Multimodal Attention-based classifier for Sepsis mortality prediction in the ICU

  2024. Lena Mondrejevski (et al.). 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), 326-331

  Conference

  Sepsis poses a significant threat to public health, causing millions of deaths annually. While treatable with timely intervention, accurately identifying at-risk patients remains challenging due to the condition’s complexity. Traditional scoring systems have been utilized, but their effectiveness has waned over time. Recognizing the need for comprehensive assessment, we introduce MASICU, a novel machine learning model architecture tailored for predicting ICU sepsis mortality. MASICU is a novel multimodal, attention-based classification model that integrates interpretability within an ICU setting. Our model incorporates multiple modalities and multimodal fusion strategies and prioritizes interpretability through different attention mechanisms. By leveraging both static and temporal features, MASICU offers a holistic view of the patient’s clinical status, enhancing predictive accuracy while providing clinically relevant insights.

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• Let’s talk about non-verbal communication: using AI and Machine learning for the investigation of interpersonal psychotherapeutic interactions

  2025. Stephan Hau (et al.). Scandinavian Psychoanalytic Review, 1-12

  Article

  The transmissions in the interpersonal communication in psychotherapy is very complex as both parties are simultaneous senders and recipients of non-verbal and verbal messages. To learn more about these transmissions, it is important in psychotherapy research to link the non-verbal communication to the verbal content and to explore the communication patterns that arise over time. Non-verbal communication is multimodal and includes, for example, body/head position and movements, facial expressions, eye movements or emotional prosody. These non-verbal signals also form part of the complex interactional synchronization patterns that include reflections of emotional expression. First results will be presented of an interdisciplinary project, in which the fields of psychology and data science are brought together. The aim of the study is to analyze all sessions of video-recorded psychodynamic psychotherapy with the help of AI-based methods such as machine learning and time series analyses to investigate what is transmitted non-verbally and which nonverbal interactive behavioral patterns develop between patient and therapist.

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Show all publications by Franco Rugolon at Stockholm University