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Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Patients with venous thromboembolism (VTE) and cancer are at higher risk of recurrent VTE and mortality. Clinical guidelines recommend anticoagulant treatment for these patients. This study assessed trends in outpatient anticoagulant treatment and factors associated with this treatment initiation in outpatient setting among this high-risk patient population., Objective: To study trends and factors associated with anticoagulant treatment initiation among patients with VTE and cancer., Methods: VTE cancer patients age ≥65 were identified from the SEER-Medicare database from 01JAN2014-31DEC2019. Patients were enrolled for ≥6 months prior to their first VTE (i.e. index event) and without evidence of other reasons for anticoagulation (i.e., atrial fibrillation). Patients were also required to be enrolled for ≥30 days after index. Cancer status was identified from SEER or Medicare database in the 6 months pre- through 30 days post-VTE. Patients were classified into treated or untreated cohorts depending on whether they initiated outpatient anticoagulant treatment within 30 days post-index. The trends of treated vs. untreated were evaluated by quarter. Logistic regression was used to identify demographic-, VTE-, cancer- and comorbid-related factors associated with anticoagulant treatment initiation., Results: A total of 28,468 VTE-cancer patients met all study criteria. Of these, ~46 % initiated outpatient anticoagulant treatment within 30 days, and ~54 % did not. The above rates were stable from 2014 to 2019. Factors such as VTE diagnosis in inpatient setting, pulmonary embolism (PE) diagnosis, and pancreatic cancer were associated with increased odds whereas bleeding history and some comorbid factors were associated with decreased odds of initiating anticoagulant treatment., Conclusion: Over half of VTE patients with cancer did not initiate outpatient anticoagulant treatment within the first 30-days after VTE diagnosis. This trend was stable from 2014 to 2019. A range of cancer-, VTE-, and comorbid-related factors were associated with the likelihood of the treatment initiation., Competing Interests: Conflict of interest AD, MF, XL, and JJ are employees of Bristol Myers Squibb, United States who contributed funding to this study. SS, VN and RB are employees of STATinMED which received funding to conduct this study. XL is an employee of Pfizer, United States who also contributed funding to this study., (Copyright © 2023. Published by Elsevier Ltd.)