Patient “no-shows” are missed appointments resulting in clinical inefficiencies, revenue loss, and discontinuity of care. Using secondary electronic health record (EHR) data, we used machine learning to predict patient no-shows in follow-up and new patient visits in pediatric ophthalmology and to evaluate features for importance. The best model, XGBoost, had an area under the receiver operating characteristics curve (AUC) score of 0.90 for predicting no-shows in follow-up visits. The key findings from this study are (1) secondary use of EHR data can be used to build datasets for predictive modeling and successfully predict patient no-shows in pediatric ophthalmology, (2) models predicting no-shows for follow-up visits are more accurate than those for new patient visits, and (3) the performance of predictive models is more robust in predicting no-shows compared to individual important features. We hope these models will be used for more effective interventions to mitigate the impact ofpatient no-shows.Rapidly increasing costs have been a major threat to our clinical research enterprise. Improvement in appointment scheduling is a crucial means to boost efficiency and save cost in clinical research and has been well studied in the outpatient setting. This study reviews nearly 5 years of usage data of an integrated scheduling system implemented at Columbia University/New York Presbyterian (CUIMC/NYP) called IMPACT and provides original insights into the challenges faced by a clinical research facility. Briefly, the IMPACT data shows that high rates of room and resource changes correlate with rescheduled appointments and that rescheduled visits are more likely to be attended than non-rescheduled visits. We highlight the differing roles of schedulers, coordinators, and investigators, and propose a highly accurate predictive model of participant no-shows in a research setting. This study sheds light on ways to reduce overall cost and improve the care we offer to clinical research participants.Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electronic health record (EHR) data are being explored to better identify patients, a process called EHR phenotyping. Most STB phenotyping studies have used structured EHR data, but some are beginning to incorporate unstructured clinical text. In this study, we used a publicly-accessible natural language processing (NLP) program for biomedical text (MetaMap) and iterative elastic net regression to extract and select predictive text features from the discharge summaries of 810 inpatient admissions of interest. Initial sets of 5,866 and 2,709 text features were reduced to 18 and 11, respectively. The two models fit with these features obtained an area under the receiver operating characteristic curve of 0.866-0.895 and an area under the precision-recall curve of 0.800-0.838, demonstrating the approach’s potential to identify textual features to incorporate in phenotyping models.Identification of comorbidity subgroups linked with Autism Spectrum Disorder (ASD) could provide promising insight into learning more about this disorder. This study sought to use the Rhode Island All-Payer Claims Database to examine mental health conditions linked to ASD. Medical claims data for ASD patients and one or more mental health conditions were analyzed using descriptive statistics, association rule mining (ARM), and sequential pattern mining (SPM). The results indicated that patients with ASD have a higher proportion of mental health diagnoses than the general pediatric population. ARM and SPM methods identified patterns of comorbidities commonly seen among ASD patients. Based on the observed patterns and temporal sequences, suicidal ideation, mood disorders, anxiety, and conduct disorders may need focused attention prospectively. Understanding more about groupings of ASD patients and their comorbidity burden can help bridge gaps in knowledge and make strides toward improved outcomes for patients with ASD.Due to the fast pace at which randomized controlled trials are published in the health domain, researchers, consultants and policymakers would benefit from more automatic ways to process them by both extracting relevant information and automating the meta-analysis processes. In this paper, we present a novel methodology based on natural language processing and reasoning models to 1) extract relevant information from RCTs and 2) predict potential outcome values on novel scenarios, given the extracted knowledge, in the domain of behavior change for smoking cessation.Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, causing delays or even early termination. Using electronic health records to find eligible patients who meet clinical trial eligibility criteria has been shown as a promising way to assess recruitment feasibility and accelerate the recruitment process. In this study, we analyzed the eligibility criteria of 100 randomly selected DS clinical trials and identified both computable and non-computable criteria. We mapped annotated entities to OMOP Common Data Model (CDM) with novel entities (e.g., DS). We also evaluated a deep learning model (Bi-LSTM-CRF) for extracting these entities on CLAMP platform, with an average F1 measure of 0.601. This study shows the feasibility of automatic parsing of the eligibility criteria following OMOP CDM for future cohort identification.Opioid use disorder (OUD) represents a global public health crisis that challenges classic clinical decision making. As existing hospital screening methods are resource-intensive, patients with OUD are significantly under-detected. An automated and accurate approach is needed to improve OUD identification so that appropriate care can be provided to these patients in a timely fashion. In this study, we used a large-scale clinical database from Mass General Brigham (MGB; formerly Partners HealthCare) to develop an OUD patient identification algorithm, using multiple machine learning methods. Working closely with an addiction psychiatrist, we developed a set of hand-crafted rules for identifying information suggestive of OUD from free-text clinical notes. We implemented a natural language processing (NLP)-based classification algorithm within the Medical Text Extraction, Reasoning and Mapping System (MTERMS) tool suite to automatically label patients as positive or negative for OUD based on these rules. Proteinase K nmr We further used the NLP output as features to build multiple machine learning and a neural classifier.