Patient-reported outcomes (PROs) and their use in the clinical workflow can improve cancer survivors’ outcomes and quality of life. However, there are several challenges regarding efficient collection of the patient-reported outcomes and their integration into the clinical workflow. Patient adherence and interoperability are recognized as main barriers. This work implements a cancer-related study which interconnects artificial intelligence (spoken language algorithms, conversational intelligence) and natural sciences (embodied conversational agents) to create an omni-comprehensive system enabling symmetric computer-mediated interaction. Its goal is to collect patient information and integrate it into clinical routine as digital patient resources (the Fast Healthcare Interoperability Resources). To further increase convenience and simplicity of the data collection, a multimodal sensing network is delivered. In this paper, we introduce the main components of the system, including the mHealth application, the Open Health Connect platform, and algorithms to deliver speech enabled 3D embodied conversational agent to interact with the cancer survivors in five different languages. The system integrates cancer patients’ reported information as patient gathered health data into their digital clinical record. The value and impact of the integration will be further evaluated in the clinical study.

Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.

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With spoken language interfaces, chatbots, and enablers, the conversational intelligence became an emerging field of research in man-machine interfaces in several target domains. In this paper, we introduce the multilingual conversational chatbot platform that integrates Open Health Connect platform and mHealth application together with multimodal services in order to deliver advanced 3D embodied conversational agents. The platform enables novel human-machine interaction with the cancer survivors in six different languages. The platform also integrates patients’ reported information as patients gather health data into digital clinical records. Further, the conversational agents have the potential to play a significant role in healthcare, from assistants during clinical consultations, to supporting positive behavior changes, or as assistants in living environments helping with daily tasks and activities.