Improving Keyword-Based Topic Classification in Cancer Patient Forums with Multilingual Transformers.

Online forums play an important role in connecting people who have crossed paths with cancer. These communities create networks of mutual support that cover different cancer-related topics, containing an extensive amount of heterogeneous information that can be mined to get useful insights. This work presents a case study where users' posts from an Italian cancer patient community have been classified combining both count-based and prediction-based representations to identify discussion topics, with the aim of improving message reviewing and filtering. We demonstrate that pairing simple bag-of-words representations based on keywords matching with pre-trained contextual embeddings significantly improves the overall quality of the predictions and allows the model to handle ambiguities and misspellings. By using non-English real-world data, we also investigated the reusability of pretrained multilingual models like BERT in lower data regimes like many local medical institutions.

A review of AI and Data Science support for cancer management.

INTRODUCTION: Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home. OBJECTIVE: Our main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt. METHODS: We designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient's state from it and deliver coaching/behavior change interventions. RESULTS: Starting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching. CONCLUSION: Development of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

Patient similarity for precision medicine: A systematic review.

Evidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical practice guidelines typically define a set of recommendations together with eligibility criteria that restrict their applicability to a specific group of patients. The ever-growing size and availability of health-related data is currently challenging the broad definitions of guideline-defined patient groups. Precision medicine leverages on genetic, phenotypic, or psychosocial characteristics to provide precise identification of patient subsets for treatment targeting. Defining a patient similarity measure is thus an essential step to allow stratification of patients into clinically-meaningful subgroups. The present review investigates the use of patient similarity as a tool to enable precision medicine. 279 articles were analyzed along four dimensions: data types considered, clinical domains of application, data analysis methods, and translational stage of findings. Cancer-related research employing molecular profiling and standard data analysis techniques such as clustering constitute the majority of the retrieved studies. Chronic and psychiatric diseases follow as the second most represented clinical domains. Interestingly, almost one quarter of the studies analyzed presented a novel methodology, with the most advanced employing data integration strategies and being portable to different clinical domains. Integration of such techniques into decision support systems constitutes and interesting trend for future research.

Trusting telemedicine: A discussion on risks, safety, legal implications and liability of involved stakeholders.

OBJECTIVES: The main purpose of the article is to raise awareness among all the involved stakeholders about the risks and legal implications connected to the development and use of modern telemedicine systems. Particular focus is given to the class of "active" telemedicine systems, that imply a real-world, non-mediated, interaction with the final user. A secondary objective is to give an overview of the European legal framework that applies to these systems, in the effort to avoid defensive medicine practices and fears, which might be a barrier to their broader adoption. METHODS: We leverage on the experience gained during two international telemedicine projects, namely MobiGuide (pilot studies conducted in Spain and Italy) and AP@home (clinical trials enrolled patients in Italy, France, the Netherlands, United Kingdom, Austria and Germany), whose development our group has significantly contributed to in the last 4 years, to create a map of the potential criticalities of active telemedicine systems and comment upon the legal framework that applies to them. Two workshops have been organized in December 2015 and March 2016 where the topic has been discussed in round tables with system developers, researchers, physicians, nurses, legal experts, healthcare economists and administrators. RESULTS: We identified 8 features that generate relevant risks from our example use cases. These features generalize to a broad set of telemedicine applications, and suggest insights on possible risk mitigation strategies. We also discuss the relevant European legal framework that regulate this class of systems, providing pointers to specific norms and highlighting possible liability profiles for involved stakeholders. CONCLUSIONS: Patients are more and more willing to adopt telemedicine systems to improve home care and day-by-day self-management. An essential step towards a broader adoption of these systems consists in increasing their compliance with existing regulations and better defining responsibilities for all the involved stakeholders.

UceWeb: a web-based collaborative tool for collecting and sharing quality of life data.

OBJECTIVES: This work aims at building a platform where quality-of-life data, namely utility coefficients, can be elicited not only for immediate use, but also systematically stored together with patient profiles to build a public repository to be further exploited in studies on specific target populations (e.g. cost/utility analyses). METHODS: We capitalized on utility theory and previous experience to define a set of desirable features such a tool should show to facilitate sound elicitation of quality of life. A set of visualization tools and algorithms has been developed to this purpose. To make it easily accessible for potential users, the software has been designed as a web application. A pilot validation study has been performed on 20 atrial fibrillation patients. RESULTS: A collaborative platform, UceWeb, has been developed and tested. It implements the standard gamble, time trade-off and rating-scale utility elicitation methods. It allows doctors and patients to choose the mode of interaction to maximize patients' comfort in answering difficult questions. Every utility elicitation may contribute to the growth of the repository. CONCLUSION: UceWeb can become a unique source of data allowing researchers both to perform more reliable comparisons among healthcare interventions and build statistical models to gain deeper insight into quality of life data.