Globally local: Hyper-local modeling for accurate forecast of COVID-19.

IMPORTANCE: Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it. OBJECTIVE: This paper's primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels. DESIGN: We use publicly available confirmed SARS-CoV-2 cases and deaths from January 1st, 2020 to August 3rd, 2020 in the United States at different geo-spatial granularities to conduct our experiments. To understand the impact of this hypothesis, the output of this study was implemented in Tampa General Hospital (TGH) to provide resource demand forecast. RESULTS: The Mean Absolute Percentage Error (MAPE) in the forecast confirmed cases can be 30% higher for modeling at the state-level than aggregating model results at the scale of counties or clusters of counties. Similarly, modeling at a state-level and crafting policy decisions based on them may not be effective - county-level forecasts made by partitioning state-level forecasts are 3x worse for confirmed cases and 20x worse for deaths relative to the same model at the county level. By leveraging these results, TGH was able to accurately allocate clinical resources to tackle COVID-19 cases, continue elective surgical procedures largely uninterrupted and avoid costly construction of overflow capacity in the first two epidemic waves. CONCLUSIONS AND RELEVANCE: Accurate forecasting at the county level requires hyper-local modeling with county resolution. State-level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. Actionable decisions such as deciding whether to cancel planned surgeries or construct overflow capacity require models with local specificity.

A survey on literature based discovery approaches in biomedical domain.

Literature Based Discovery (LBD) refers to the problem of inferring new and interesting knowledge by logically connecting independent fragments of information units through explicit or implicit means. This area of research, which incorporates techniques from Natural Language Processing (NLP), Information Retrieval and Artificial Intelligence, has significant potential to reduce discovery time in biomedical research fields. Formally introduced in 1986, LBD has grown to be a significant and a core task for text mining practitioners in the biomedical domain. Together with its inter-disciplinary nature, this has led researchers across domains to contribute in advancing this field of study. This survey attempts to consolidate and present the evolution of techniques in this area. We cover a variety of techniques and provide a detailed description of the problem setting, the intuition, the advantages and limitations of various influential papers. We also list the current bottlenecks in this field and provide a general direction of research activities for the future. In an effort to be comprehensive and for ease of reference for off-the-shelf users, we also list many publicly available tools for LBD. We hope this survey will act as a guide to both academic and industry (bio)-informaticians, introduce the various methodologies currently employed and also the challenges yet to be tackled.

Towards self-learning based hypotheses generation in biomedical text domain.

Motivation: The overwhelming amount of research articles in the domain of bio-medicine might cause important connections to remain unnoticed. Literature Based Discovery is a sub-field within biomedical text mining that peruses these articles to formulate high confident hypotheses on possible connections between medical concepts. Although many alternate methodologies have been proposed over the last decade, they still suffer from scalability issues. The primary reason, apart from the dense inter-connections between biological concepts, is the absence of information on the factors that lead to the edge-formation. In this work, we formulate this problem as a collaborative filtering task and leverage a relatively new concept of word-vectors to learn and mimic the implicit edge-formation process. Along with single-class classifier, we prune the search-space of redundant and irrelevant hypotheses to increase the efficiency of the system and at the same time maintaining and in some cases even boosting the overall accuracy. Results: We show that our proposed framework is able to prune up to 90% of the hypotheses while still retaining high recall in top-K results. This level of efficiency enables the discovery algorithm to look for higher-order hypotheses, something that was infeasible until now. Furthermore, the generic formulation allows our approach to be agile to perform both open and closed discovery. We also experimentally validate that the core data-structures upon which the system bases its decision has a high concordance with the opinion of the experts.This coupled with the ability to understand the edge formation process provides us with interpretable results without any manual intervention. Availability and implementation: The relevant JAVA codes are available at: https://github.com/vishrawas/Medline-Code_v2. Supplementary information: Supplementary data are available at Bioinformatics online.