Faculty Bibliography

INTRODUCTION AND AIMS/OBJECTIVE:This article examines the feasibility of leveraging Twitter to detect posts authored by people who use opioids (PWUO) or content related to opioid use disorder (OUD), and manually develop a multidimensional taxonomy of relevant tweets. DESIGN AND METHODS/METHODS:Twitter messages were collected between June and October 2017 (n = 23 827) and evaluated using an inductive coding approach. Content was then manually classified into two axes (n = 17 420): (i) user experience regarding accessing, using, or recovery from illicit opioids; and (ii) content categories (e.g. policies, medical information, jokes/sarcasm). RESULTS:The most prevalent categories consisted of jokes or sarcastic comments pertaining to OUD, PWUOs or hypothetically using illicit opioids (63%), informational content about treatments for OUD, overdose prevention or accessing self-help groups (20%), and commentary about government opioid policy or news related to opioids (17%). Posts by PWUOs centered on identifying illicit sources for procuring opioids (i.e. online, drug dealers; 49%), symptoms and/or strategies to quell opioid withdrawal symptoms (21%), and combining illicit opioid use with other substances, such as cocaine or benzodiazepines (17%). State and public health experts infrequently posted content pertaining to OUD (1%). DISCUSSION AND CONCLUSIONS/CONCLUSIONS:Twitter offers a feasible approach to identify PWUO. Further research is needed to evaluate the efficacy of Twitter to disseminate evidence-based content and facilitate linkage to treatment and harm reduction services.

The trend of e-cigarette use among teens is ever increasing. Here we show the dysbiotic oral microbial ecology in e-cigarette users influencing the local host immune environment compared with non-smoker controls and cigarette smokers. Using 16S rRNA high-throughput sequencing, we evaluated 119 human participants, 40 in each of the three cohorts, and found significantly altered beta-diversity in e-cigarette users (p = 0.006) when compared with never smokers or tobacco cigarette smokers. The abundance of Porphyromonas and Veillonella (p = 0.008) was higher among vapers. Interleukin (IL)-6 and IL-1β were highly elevated in e-cigarette users when compared with non-users. Epithelial cell-exposed e-cigarette aerosols were more susceptible for infection. In vitro infection model of premalignant Leuk-1 and malignant cell lines exposed to e-cigarette aerosol and challenged by Porphyromonas gingivalis and Fusobacterium nucleatum resulted in elevated inflammatory response. Our findings for the first time demonstrate that e-cigarette users are more prone to infection.

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4-88.7] and 90.8% [90.8-90.8]) and discrimination (95.1% [95.1-95.2] and 86.8% [86.8-86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.

[This corrects the article DOI: 10.1371/journal.pone.0215571.].

BACKGROUND:Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. The ability to predict obesity before age five could be a useful tool, allowing prevention strategies to focus on high risk children. The few existing prediction models for obesity in childhood have primarily employed data from longitudinal cohort studies, relying on difficult to collect data that are not readily available to all practitioners. Instead, we utilized real-world unaugmented electronic health record (EHR) data from the first two years of life to predict obesity status at age five, an approach not yet taken in pediatric obesity research. METHODS AND FINDINGS/RESULTS:We trained a variety of machine learning algorithms to perform both binary classification and regression. Following previous studies demonstrating different obesity determinants for boys and girls, we similarly developed separate models for both groups. In each of the separate models for boys and girls we found that weight for length z-score, BMI between 19 and 24 months, and the last BMI measure recorded before age two were the most important features for prediction. The best performing models were able to predict obesity with an Area Under the Receiver Operator Characteristic Curve (AUC) of 81.7% for girls and 76.1% for boys. CONCLUSIONS:We were able to predict obesity at age five using EHR data with an AUC comparable to cohort-based studies, reducing the need for investment in additional data collection. Our results suggest that machine learning approaches for predicting future childhood obesity using EHR data could improve the ability of clinicians and researchers to drive future policy, intervention design, and the decision-making process in a clinical setting.

OBJECTIVES/OBJECTIVE:Black race has been associated with increased 30-day morbidity and mortality following surgery for endometrial cancer. Black women are also less likely to undergo laparoscopy when compared to white women. With the development of improved laparoscopic techniques and equipment, including the robotic platform, we sought to evaluate whether there has been a change in surgical approach for black women, and in turn, improvement in perioperative outcomes. METHODS:Using the American College of Surgeons' National Surgical Quality Improvement Project's database, patients who underwent hysterectomy for endometrial cancer from 2010 to 2015 were identified. Comparative analyses stratified by race and hysterectomy approach were performed to assess the relationship between race and perioperative outcomes. RESULTS:A total of 17,692 patients were identified: of these, 13,720 (77.5%) were white and 1553 (8.8%) were black. Black women were less likely to undergo laparoscopic hysterectomy compared to white women (49.3% vs 71.3%, p<0.0001). Rates of laparoscopy in both races increased over the 6-year period; however these consistently remained lower in black women each year. Black women had higher 30-day postoperative complication rates compared to white women (22.5% vs 13.6%, p<0.0001). When laparoscopic hysterectomies were isolated, there was no difference in postoperative complication rates between black and white women (9.2% vs 7.5%, p=0.1). CONCLUSIONS:Overall black women incur more postoperative complications compared to white women undergoing hysterectomy for endometrial cancer. However, laparoscopy may mitigate this disparity. Efforts should be made to maximize the utilization of minimally invasive surgery for the surgical management of endometrial cancer.

Conventional text classification models make a bag-of-words assumption reducing text into word occurrence counts per document. Recent algorithms such as word2vec are capable of learning semantic meaning and similarity between words in an entirely unsupervised manner using a contextual window and doing so much faster than previous methods. Each word is projected into vector space such that similar meaning words such as "strong" and "powerful" are projected into the same general Euclidean space. Open questions about these embeddings include their utility across classification tasks and the optimal properties and source of documents to construct broadly functional embeddings. In this work, we demonstrate the usefulness of pre-trained embeddings for classification in our task and demonstrate that custom word embeddings, built in the domain and for the tasks, can improve performance over word embeddings learnt on more general data including news articles or Wikipedia.