## 1 Introduction  
Immune checkpoint inhibitors (ICIs) are standard-of-care treatments in several malignancies, both in adjuvant and advanced settings [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. However, treatment response assessment of the ICIs differs from traditional cancer therapies, with unique tumor response patterns such as pseudo- and hyperprogression [13]. Furthermore, the temporal association of radiological response to treatment may sometimes be obscure. While only a subset of patients responds to ICIs, novel tools to assess the treatment response are needed when aiming to improve patient care and the clinical value of ICIs.  
Artificial intelligence (AI)-based analytics have gained growing interest in the field of cancer care. Machine learning models have been shown to predict responses to a variety of standard-of-care chemotherapy regimens from gene expression profiles of individual patients with high accuracy [14; 15]. Furthermore, deep learning systems have shown promising results, especially in cancer diagnostics [16]. AI-based methods can be used to analyze vast data pools to create predictive and prognostic analytics for generating value-based healthcare assets. In addition, recent data show that machine learning (ML) algorithms could identify patients with cancer who are at risk of short-term mortality [17]. Tumor immunology is a very complex entity, and it is clear that none of the single factors known so far can predict benefit for ICI therapy with high accuracy. Therefore, it is likely that using multiple inputs would result in prediction models with higher sensitivity and specificity.  
A comprehensive and timely assessment of patients' symptoms is feasible via electronic (e) patient-reported outcomes (PROs) [18; 19; 20]. ePROs have been shown to improve quality of life (QoL) and survival and decrease emergency clinic visits in cancer patients receiving chemotherapy and in lung cancer follow-up [21; 22]. Numerous studies have linked ICI treatment benefit to the presence of physician-assessed immune-related adverse events (irAEs), but the prognostic role of ePROs is an uninvestigated area [23; 24; 25]. While ML-based methodology can comprise numerous variable data sources to generate prediction models [26], the association of irAEs to ICI treatment benefit together with the complexity of tumor immunology generates an interesting landscape to investigate ML-based models.  
We have previously shown that the real-world symptom data collected with the Kaiku Health ePRO tool from cancer patients receiving ICI therapy align with the data from clinical trials and that correlations between different symptoms occur, which might reflect therapeutic efficiency, side effects, or tumor progression [27; 28]. We first explored the possibilities of ML-based prediction models on ePROs to create prediction models of symptom continuity of cancer patients receiving ICIs and showed that it is feasible [29]. Based on our previous work on ML modeling and the ePRO symptom correlations, we speculated that if symptoms can predict irAEs, symptoms could work as a surrogate to irAEs. That hypothesis was confirmed in our latest research showing that ML-based prediction models using ePRO and electronic health care record (EHR) data as an input can predict the presence and onset of irAEs with a high accuracy [30].  
The aim of this study was to investigate whether it is possible to predict objective response rate (ORR) in patients undergoing ICIs for advanced cancers. Thus, pseudonymized and aggregated ePRO symptom data collected with the Kaiku Health ePRO tool, laboratory values, and demographics, in addition to prospectively collected clinician-assessed treatment responses and irAE data, were used to train and tune a prediction model built using an open-source Python library XBoost (extreme gradient boosting algorithm) to assess clinical response to ICI treatment.