Background SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)–based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. Objective The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. Methods A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. Results The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients’ radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. Conclusions In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.

Background: Direct oral anticoagulants (DOACs) have been found to be similar or superior to warfarin in reducing ischemic stroke and intracranial hemorrhage (ICH) in patients with atrial fibrillation (AF). We sought to examine the anticoagulation prescription patterns in community since the advent of DOACs and also evaluate the outcomes in terms of gastrointestinal (GI) bleeding, ischemic stroke, and ICH in real-world patients with AF receiving anticoagulation. Methods: This is a retrospective study comprising patients who were newly diagnosed with nonvalvular AF and were prescribed anticoagulants for stroke prevention. Prescription pattern of the anticoagulants based on CHA2DS2Vasc score was studied. Clinical outcomes of GI bleeding, ischemic stroke, and ICH were analyzed using a multivariate logistic regression model. Results: Of the 2362 patients with AF on anticoagulation, 44.7% were prescribed DOACs. Patients with CHA2DS2VASc score of ≥3 received a prescription for warfarin more often than DOACs ( P < .001). Multivariate logistic regression analysis revealed that the incidence of GI bleed (odds ratio [OR]: 0.91, 95% confidence interval [CI]: 0.62-1.35, P = .66) and stroke (OR: 0.77, 95% CI: 0.57-1.05, P = .10) was similar between warfarin and DOAC users. However, there was a trend toward lower ICH in the DOAC group (OR: 0.60, 95% CI: 0.36-1.01, P = .06). Conclusions: Prescription rate of DOACs for nonvalvular AF has increased significantly, with apixaban being the most commonly used agent. Patients with higher CHA2DS2-VASc score (≥3) are prescribed DOACs less often than warfarin. The reason for this discrepancy is unclear. Given the favorable risk–benefit profile of DOACs, further studies are needed to identify factors that determine anticoagulant selection in patients with AF with high thromboembolic risk.

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Objectives: The time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesize that understanding longitudinal disease characteristics can lead to more timely intervention and improve clinical outcomes. As a first step, we developed an efficient and easy-to-install toolkit, the Time-based Elixhauser Comorbidity Index (TECI), which pre-calculates time-based Elixhauser comorbidities and can be extended to common data models (CDMs). Methods: A Structured Query Language (SQL)-based toolkit, TECI, was built to pre-calculate time-specific Elixhauser comorbidity indices using data from a clinical data repository (CDR). Then it was extended to the Informatics for Integrating Biology and the Bedside (I2B2) and Observational Medical Outcomes Partnership (OMOP) CDMs. Results: At the University of Arkansas for Medical Sciences (UAMS), the TECI toolkit was successfully installed to compute the indices from CDR data, and the scores were integrated into the I2B2 and OMOP CDMs. Comorbidity scores calculated by TECI were validated against: scores available in the 2015 quarter 1-3 Nationwide Readmissions Database (NRD) and scores calculated using the comorbidities using a previously validated algorithm on the 2015 quarter 4 NRD. Furthermore, TECI identified 18,846 UAMS patients that had changes in comorbidity scores over time (year 2013 to 2019). Comorbidities for a random sample of patients were independently reviewed, and in all cases, the results were found to be 100% accurate. Conclusions: TECI facilitates the study of comorbidities within a time-dependent context, allowing better understanding of disease associations and trajectories, which has the potential to improve clinical outcomes.