Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table

Generative models improve fairness of medical classifiers under distribution shifts

A ubiquitous challenge in machine learning is the problem of domain generalisation. This can exacerbate bias against groups or labels that are underrepresented in the datasets used for model development. Model bias can lead to unintended harms, especially in safety-critical applications like healthcare. Furthermore, the challenge is compounded by the difficulty of obtaining labelled data due to high cost or lack of readily available domain expertise. In our work, we show that learning realistic augmentations automatically from data is possible in a label-efficient manner using generative models. In particular, we leverage the higher abundance of unlabelled data to capture the underlying data distribution of different conditions and subgroups for an imaging modality. By conditioning generative models on appropriate labels, we can steer the distribution of synthetic examples according to specific requirements. We demonstrate that these learned augmentations can surpass heuristic ones by making models more robust and statistically fair in- and out-of-distribution. To evaluate the generality of our approach, we study 3 distinct medical imaging contexts of varying difficulty: (i) histopathology images from a publicly available generalisation benchmark, (ii) chest X-rays from publicly available clinical datasets, and (iii) dermatology images characterised by complex shifts and imaging conditions. Complementing real training samples with synthetic ones improves the robustness of models in all three medical tasks and increases fairness by improving the accuracy of diagnosis within underrepresented groups. This approach leads to stark improvements OOD across modalities: 7.7% prediction accuracy improvement in histopathology, 5.2% in chest radiology with 44.6% lower fairness gap and a striking 63.5% improvement in high-risk sensitivity for dermatology with a 7.5x reduction in fairness gap

Towards Generalist Biomedical AI

Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly encode, integrate, and interpret this data at scale can potentially enable impactful applications ranging from scientific discovery to care delivery. To enable the development of these models, we first curate MultiMedBench, a new multimodal biomedical benchmark. MultiMedBench encompasses 14 diverse tasks such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling. We then introduce Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system. Med-PaLM M is a large multimodal generative model that flexibly encodes and interprets biomedical data including clinical language, imaging, and genomics with the same set of model weights. Med-PaLM M reaches performance competitive with or exceeding the state of the art on all MultiMedBench tasks, often surpassing specialist models by a wide margin. We also report examples of zero-shot generalization to novel medical concepts and tasks, positive transfer learning across tasks, and emergent zero-shot medical reasoning. To further probe the capabilities and limitations of Med-PaLM M, we conduct a radiologist evaluation of model-generated (and human) chest X-ray reports and observe encouraging performance across model scales. In a side-by-side ranking on 246 retrospective chest X-rays, clinicians express a pairwise preference for Med-PaLM M reports over those produced by radiologists in up to 40.50% of cases, suggesting potential clinical utility. While considerable work is needed to validate these models in real-world use cases, our results represent a milestone towards the development of generalist biomedical AI systems

Computational analysis and modelling of graph-structured neuroimaging data

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. This thesis is focusing on the analysis of neuroimaging data that can be intuitively modelled as graphs. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. They have contributed to gaining novel insights into the brain's organisation and the mechanisms underlying brain development and disease, which were previously unknown. At an individual level, the underlying parcellation used to construct the connectome, the comprehensive map of neural connections in the brain, and its resolution impact traditional network measures and network-based tasks. This thesis explores how these factors affect topological measures of brain networks and uses the latter to inform predictive models of patient outcome in diseased populations. Additionally, a graph theoretical approach is proposed to establish correspondences between graph elements, when subject-level data-driven parcellation methods are adopted, which is an essential step to perform any further population-level analysis. The present work also employs concepts from the field of signal processing on graphs and geometric deep learning to address significant problems in disease prediction. More specifically, graph convolutions are adopted for the evaluation of similarity between brain connectivity networks in a manner that accounts for the graph structure and is tailored for classification tasks. At the same time, exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of simultaneously representing individual features and data associations between subjects from potentially large populations. The latter can be particularly beneficial in large-scale studies and graphs provide a natural framework for such tasks. This work uses geometric deep learning to perform convolutions on a population graph incorporating both imaging and non-imaging information and demonstrates their importance for semi-supervised classification tasks. The proposed framework allows to infer subject-specific properties from their imaging features and interactions within a population.Open Acces

Effective scheduling of hospital personnel needs through forecasting daily emergency admissions

151 σ.Ο βασικός κοινωνικός στόχος του Παγκόσμιου Οργανισμού Υγείας και των κρατών-μελών του κατά τα τέλη του προηγούμενου αιώνα ήταν η εξασφάλιση για όλους τους ανθρώπους του κόσμου ενός επιπέδου υγείας μέχρι το 2000, που θα τους επέτρεπε να έχουν μια κοινωνικά και οικονομικά παραγωγική ζωή. Παρόλα αυτά, ακόμα και σήμερα, μία δεκαετία αργότερα, οι ανισότητες στον κλάδο της Υγείας είναι τόσο μεγάλες που, προκειμένου να επιτευχθεί αυτός ο στόχος, πρέπει να πραγματοποιηθεί μια σημαντική ανακατανομή των ανθρώπινων δυνάμεων, αλλά και να αλλάξει ριζικά ο τρόπος με τον οποίο οι ανθρώπινοι πόροι χρησιμοποιούνται για τη βελτίωση της υγείας. Το ανθρώπινο δυναμικό είναι ο πυλώνας του συστήματος υγείας κάθε χώρας, καθώς όλες οι μορφές της υγειονομικής περίθαλψης βασίζονται σε ένα καλά εκπαιδευμένο υγειονομικό προσωπικό. Σε πολλές χώρες έχει δοθεί ελάχιστη σημασία στο σχεδιασμό του ανθρώπινου δυναμικού, ενώ, αρκετές φορές, τα σχέδια που αναπτύχθηκαν κατέληξαν σε αποτυχία. Έτσι, λοιπόν, στη σημερινή εποχή, οι μάνατζερ του τομέα της υγείας έρχονται αντιμέτωποι με όλες τις σημαντικές προκλήσεις του 21ου αιώνα αλλά και με τους Στόχους Ανάπτυξης της Χιλιετίας. Πολλές από αυτές τις προκλήσεις προκύπτουν από τη δυσκολία εξασφάλισης μιας επαρκούς και κατάλληλης κατανομής του προσωπικού της υγείας, παράλληλα με τις αυξανόμενες οικονομικές πιέσεις που δέχεται ο δημόσιος τομέας για περιορισμό των δαπανών του. Η μέθοδος Δεικτών Φόρτου Εργασίας για τις Ανάγκες σε Προσωπικό (WISN) είναι μία αυστηρή μέθοδος που σαν στόχο έχει τον προσδιορισμό του πλήθους των εργαζομένων που απαιτούνται στις υγειονομικές εγκαταστάσεις. Οι δυνατότητες του κλάδου των προβλέψεων μπορούν να χρησιμοποιηθούν για την ολοκλήρωση και τη βελτίωση της ακρίβειας αυτής της μεθόδου, προκειμένου οι μάνατζερ ανθρώπινου δυναμικού να έχουν πλέον στα χέρια τους ένα ισχυρό και αποτελεσματικό εργαλείο για τη διαχείριση του προσωπικού που θα οδηγήσει στην καλύτερη προσφορά των υγειονομικών υπηρεσιών, την ισότητα στην πρόσβαση στις υπηρεσίες αυτές, ακόμα και στη μείωση των δαπανών για την Υγεία.The main social target of the World Health Organization and of its Member States at the end of the last century was to secure for all people of the world by the year 2000 a level of health that would allow them to lead a socially and economically productive life. However, such is the present inequality in the health status of the world’s people that, in order to reach this goal, there should be a substantial redistribution of health manpower and also a radical change of the way in which human resources are used to improve health. Health workforce is the cornerstone of every health system, since all forms of health care are based on a well-trained health personnel. In many countries, too little attention has been paid to health manpower planning, and sometimes, when plans have been developed, they proved to be inadequate and led to failure. As a result, current managers of the health sector are confronted with all these significant challenges of the 21st century as well as with the Millennium Development Goals. Many of these challenges arise from the difficulty of ensuring an adequate and appropriate distribution of health services, along with increasing financial pressures in the public sector to reduce its expenditure. The Workload Indicators of Staffing Need (WISN) method is a rigorous method which aims to determine the number of health workers required in health facilities. The potential of the field of forecasting can be employed in order to integrate this method and improve its accuracy, so as to create a powerful and effective tool for human resource managers. This tool can be used for personnel management in order to achieve better quality of health care services, equal access to these services and even reduced cost for the health sector.Σοφία-Ήρα Σ. Κτεν