Vik: A Chatbot to Support Patients with Chronic Diseases

happiness and reduce negative symptoms. Laranjo, et al. [8] conducted a systematic review of conversational agents in healthcare and showed that the use of conversational agents with unconstrained natural language input capabilities for health-related purposes was an emerging field of research. But, the authors noted that the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Bibault, et al. [9] made the same observation, if chatbots are shown to be effective and safe, they could be prescribed like any drug to improve patient information, monitoring, or treatment adherence. Chaix, et al. [2] showed that a chatbot designed to help patient with breast cancer could improve medication adherence. In 2019, we conducted a prospective study comparing the quality of medical information delivered by a chatbot to that delivered by a multidisciplinary committee of physicians for breast cancer patients. 140 patients were randomized blindly between information provided by the chatbot Vik and information provided by doctors. In this phase III, non-inferiority, randomized, blind, controlled trial, the EORTC INFO25 scores from the chatbot were Chatbots are easy to use and simulate a human conversation through text or voice via smartphones or computers. In the field of health, chatbots can improve patient information, monitoring, or treatment adherence. The objective of this article is to describe how a chatbot dedicated to disease monitoring and support of patients can interact with them and how data are exploited to be safe.

found to be non-inferior to the scores of the group of physicians Bibault,, et al. [10].

Proposed Work
The objective of this paper is to describe how a chatbot like Vik can interact with patient with text and emojis and how data are exploited to be safe. We will present how we built our virtual assistant and then how it interacts with the patient. are also a black-box with no or few personalizations. Those tools are not interesting to create a virtual assistant because we need to understand the NLP algorithm decision. We cannot let a black box decide without being able to add a border or to custom the algorithm. Secondly, because we can't send personal and health data to external actors. Finally, we aim to propose to the users a new chatbot experience that simulates a true chat with a human.

Chatbots
We can't stick to static scenarios.

Natural Language Processing
For a chatbot to be fully developed, both machine learning algorithms and Natural Language Processing are required. To build a chatbot, we have two crucial components to be supervised; Intent Classification, and Entity Recognition... Intent detection is a classic supervised classification problem. For a given question, the objective is to identify among a predefined list of intents which one corresponds to the user's request. We use machine learning algorithms to address this problem. These algorithms rely on a learning base to build models that will then allow the Vik chatbot to These questions are also used to enrich the learning base. In this way, the models are constantly evolving to ensure that Vik's level of understanding is constantly improving. Entity detection allows the extraction of relevant information from a question asked by a user. This information is used by Vik's language comprehension system to add level of personalization to the answer given by Vik by adapting it to the user's profile. For example, a user identified as a patient and a user identified as a caregiver will not always receive the same answer if they ask the same question. Entity detection is also based on machine learning algorithms that operate in a similar way to those used for intensity detection. the quality of life with information about sport, fertility, sexuality, and diet. More practical information, like reimbursement and patients rights, are also available. The goal is to increase compliance, educate the patient and improve the quality of life of patients with chronic diseases. We build several Vik for each disease. Each Vik has its contents, its own NLP model and interacts its way with the patient. So we could first think that it is better to build each Vik   The user sends a message to Vik. Vik first creates a new profile if it is a new user, we ask them to accept our TOS and to read the information about data treatment. If it is an old user, we retrieve all its data, and all the data of the Vik he is speaking with. Then the NLP will find intents and entities of the message. The NLP as a minimum threshold it must reach to accept the answer. If the threshold is not reached, then it will send to the user a proposition of the 3 most probable contents and a notification shows on our interface to be handled by a human. Then the feature linked to the intent is launched. It sends back an id of content. If there is an error or if nothing is sent, a generic message is sent and a notification shows on our interface to be handled by a human. The right content is found and personalized according to the user profile. After setting up a conversation, the user will be shown the application interface where an acknowledgment message will be shown which is followed by waiting for the user's response.

Future Developments
In the future, we will need to address multiple fields in one Vik to help patients that have multiple pathologies. Vik will have to find the right answer among the contents of all the pathologies. To do it, we will develop a module that is called before everything else. This module will decide which Vik will answer. This decision will be based on the previous question sent by the user, on the topic of the question (some topic are only related to one pathology), and on a global analysis of all the conversation between the Viks and the patients. However this solution does not allow us to answer a question related to multiple pathologies, it just allows us to answer distinct questions on distinct pathologies. To do so, we will have to create an answer to global questions.

Conclusion
A healthcare chatbot like Vik allows patients with chronic diseases to have a way to find support and answers to their concerns during their disease. Conversational agents may help patients with minor health concerns without seeing a real physician. If the quality of these softwares is not thoroughly assessed, they could be dangerous. If chatbots are effective and safe, they could be prescribed like a drug to improve patient information, monitoring, or treatment adherence. New functionalities are planned to confirm Vik as an intermediary between the patient and the medical team to provide relevant information to the physicians and enable real-time monitoring.