Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.

Recommender systems aim to increase user actions such as clicks and purchases. Typical evaluations of recommenders regard the purchase of a recommended item as a success. However, the item may have been purchased even without the recommendation. An uplift is defned as an increase in user actions caused by recommendations. Situations with and without a recommendation cannot both be observed for a specifc user-item pair at a given time instance, making uplift-based evaluation and optimization challenging. This paper proposes new evaluation metrics and optimization methods for the uplift in a recommender system. We apply a causal inference framework to estimate the average uplift for the ofine evaluation of recommenders. Our evaluation protocol leverages both purchase and recommendation logs under a currently deployed recommender system, to simulate the cases both with and without recommendations. This enables the ofine evaluation of the uplift for newly generated recommendation lists. For optimization, we need to defne positive and negative samples that are specifc to an uplift-based approach. For this purpose, we deduce four classes of items by observing purchase and recommendation logs. We derive the relative priorities among these four classes in terms of the uplift and use them to construct both pointwise and pairwise sampling methods for uplift optimization. Through dedicated experiments with three public datasets, we demonstrate the efectiveness of our optimization methods in improving the uplift. CCS CONCEPTS • Information systems → Recommender systems; • Computing methodologies → Learning from implicit feedback.

Increasing users' positive interactions, such as purchases or clicks, is an important objective of recommender systems. Recommenders typically aim to select items that users will interact with. If the recommended items are purchased, an increase in sales is expected. However, the items could have been purchased even without recommendation. Thus, we want to recommend items that results in purchases caused by recommendation. This can be formulated as a ranking problem in terms of the causal effect. Despite its importance, this problem has not been well explored in the related research. It is challenging because the ground truth of causal effect is unobservable, and estimating the causal effect is prone to the bias arising from currently deployed recommenders. This paper proposes an unbiased learning framework for the causal effect of recommendation. Based on the inverse propensity scoring technique, the proposed framework first constructs unbiased estimators for ranking metrics. Then, it conducts empirical risk minimization on the estimators with propensity capping, which reduces variance under finite training samples. Based on the framework, we develop an unbiased learning method for the causal effect extension of a ranking metric. We theoretically analyze the unbiasedness of the proposed method and empirically demonstrate that the proposed method outperforms other biased learning methods in various settings. CCS Concepts: • Information systems → Recommender systems; • Computing methodologies → Learning from implicit feedback.

Aims: Physicians have to routinely make crucial decisions about patients' health in the ICU. Sepsis affects about 35% of ICU patients, killing approximately 25% of the afflicted. In this paper, we aim to predict the occurrence of sepsis early by studying the missingness of physiological variables and using it with the overall trends in data. Methods: We chose XGBoost as our base model and tried several variations by changing hyperparameters, window sizes and imputation methods. To further improve the model, we used masking vectors to represent the missingness of features in the dataset. Additional modifications include shifting the Sepsis Label to earlier time steps and tuning the classification probability threshold to further improve the model's performance.Results: The XGBoost model with a sliding window of size 5, no imputation, utilizing informative missingness of all temporal variables and trained on labels shifted by 3 hours before t optimal , achieved a Utility Score of 0.337 on the full test set. We identified as "CTL-Team" in the challenge and were officially ranked 5 th on the basis of this score.

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