Extracting social determinants of health from inpatient electronic medical records using natural language processing.

Background: Social determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we developed methods to extract them from inpatient electronic medical record (EMR) data using techniques compatible with current EMR systems.

Methods: Four social determinants were targeted: patient language barriers, employment status, education, and whether the patient lives alone.

Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review.

Background: Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders.

Pediatric-Like Brain Tumors in Adults.

Pediatric brain tumors are different to those found in adults in pathological type, anatomical site, molecular signature, and probable tumor drivers. Although these tumors usually occur in childhood, they also rarely present in adult patients, either as a de novo diagnosis or as a delayed recurrence of a pediatric tumor in the setting of a patient that has transitioned into adult services.Due to the rarity of pediatric-like tumors in adults, the literature on these tumor types in adults is often limited to small case series, and treatment decisions are often based on the management plans taken from pediatric studies.

BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study.

Background: Inpatient falls are a substantial concern for health care providers and are associated with negative outcomes for patients. Automated detection of falls using machine learning (ML) algorithms may aid in improving patient safety and reducing the occurrence of falls.

Objective: This study aims to develop and evaluate an ML algorithm for inpatient fall detection using multidisciplinary progress record notes and a pretrained Bidirectional Encoder Representation from Transformers (BERT) language model.

Automated extraction of weight, height, and obesity in electronic medical records are highly valid.

Objective: Coding of obesity using the International Classification of Diseases (ICD) in healthcare administrative databases is under-reported and thus unreliable for measuring prevalence or incidence. This study aimed to develop and test a rule-based algorithm for automating the detection and severity of obesity using height and weight collected in several sections of the Electronic Medical Records (EMRs).

Methods: In this cross-sectional study, 1904 inpatient charts randomly selected in three hospitals in Calgary, Canada between January and June 2015 were reviewed and linked with AllScripts Sunrise Clinical Manager EMRs.

Exploring the reliability of inpatient EMR algorithms for diabetes identification.

Introduction: Accurate identification of medical conditions within a real-time inpatient setting is crucial for health systems. Current inpatient comorbidity algorithms rely on integrating various sources of administrative data, but at times, there is a considerable lag in obtaining and linking these data. Our study objective was to develop electronic medical records (EMR) data-based inpatient diabetes phenotyping algorithms.

A feedback loop between heterochromatin and the nucleopore complex controls germ-cell-to-oocyte transition during Drosophila oogenesis.

Germ cells differentiate into oocytes that launch the next generation upon fertilization. How the highly specialized oocyte acquires this distinct cell fate is poorly understood. During Drosophila oogenesis, H3K9me3 histone methyltransferase SETDB1 translocates from the cytoplasm to the nucleus of germ cells concurrently with oocyte specification.

Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing.

Background: Abstracting cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patient outcomes. Existing methods rely on coders' abstraction, which has time delays and under-coding issues. This study sought to develop an NLP-based method to detect CeVD using EMR clinical notes.

Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study.

Background: Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection.

Methods: This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020.

H/ACA snRNP-dependent ribosome biogenesis regulates translation of polyglutamine proteins.

Stem cells in many systems, including germline stem cells (GSCs), increase ribosome biogenesis and translation during terminal differentiation. Here, we show that the H/ACA small nuclear ribonucleoprotein (snRNP) complex that promotes pseudouridylation of ribosomal RNA (rRNA) and ribosome biogenesis is required for oocyte specification. Reducing ribosome levels during differentiation decreased the translation of a subset of messenger RNAs that are enriched for CAG trinucleotide repeats and encode polyglutamine-containing proteins, including differentiation factors such as RNA-binding Fox protein 1.

Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models.

Background: Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs.

Objective: This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes.

Hypertension identification using inpatient clinical notes from electronic medical records: an explainable, data-driven algorithm study.

Background: Case identification is important for health services research, measuring health system performance and risk adjustment, but existing methods based on manual chart review or diagnosis codes can be expensive, time consuming or of limited validity. We aimed to develop a hypertension case definition in electronic medical records (EMRs) for inpatient clinical notes using machine learning.

Methods: A cohort of patients 18 years of age or older who were discharged from 1 of 3 Calgary acute care facilities (1 academic hospital and 2 community hospitals) between Jan.

Oo-site: A dashboard to visualize gene expression during Drosophila oogenesis suggests meiotic entry is regulated post-transcriptionally.

Determining how stem cell differentiation is controlled has important implications for understanding the etiology of degenerative disease and designing regenerative therapies. In vivo analyses of stem cell model systems have revealed regulatory paradigms for stem cell self-renewal and differentiation. The germarium of the female Drosophila gonad, which houses both germline and somatic stem cells, is one such model system.

A translation control module coordinates germline stem cell differentiation with ribosome biogenesis during Drosophila oogenesis.

Ribosomal defects perturb stem cell differentiation, and this is the cause of ribosomopathies. How ribosome levels control stem cell differentiation is not fully known. Here, we discover that three DExD/H-box proteins govern ribosome biogenesis (RiBi) and Drosophila oogenesis.

Macrophage mitochondrial bioenergetics and tissue invasion are boosted by an Atossa-Porthos axis in Drosophila.

Cellular metabolism must adapt to changing demands to enable homeostasis. During immune responses or cancer metastasis, cells leading migration into challenging environments require an energy boost, but what controls this capacity is unclear. Here, we study a previously uncharacterized nuclear protein, Atossa (encoded by CG9005), which supports macrophage invasion into the germband of Drosophila by controlling cellular metabolism.

Msl3 promotes germline stem cell differentiation in female Drosophila.

Gamete formation from germline stem cells (GSCs) is essential for sexual reproduction. However, the regulation of GSC differentiation is incompletely understood. Set2, which deposits H3K36me3 modifications, is required for GSC differentiation during Drosophila oogenesis.

CREATE: A New Data Resource to Support Cardiac Precision Health.

Background: The initiatives of precision medicine and learning health systems require databases with rich and accurately captured data on patient characteristics. We introduce the linical gistry, dminisrative Data and lectronic Medical Records (CREATE) database, which includes linked data from 4 population databases: lberta rovincial oject for utcome ssessment in oronary eart Disease (APPROACH; a national clinical registry), Sunrise Clinical Manager (SCM) electronic medical record (city-wide), the Discharge Abstract Database (DAD), and the National Ambulatory Care Reporting System (NACRS). The intent of this work is to introduce a cardiovascular-specific database for pursuing precision health activities using big data analytics.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.

Background: Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective: This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Post-transcriptional gene regulation regulates germline stem cell to oocyte transition during Drosophila oogenesis.

During oogenesis, several developmental processes must be traversed to ensure effective completion of gametogenesis including, stem cell maintenance and asymmetric division, differentiation, mitosis and meiosis, and production of maternally contributed mRNAs, making the germline a salient model for understanding how cell fate transitions are mediated. Due to silencing of the genome during meiotic divisions, there is little instructive transcription, barring a few examples, to mediate these critical transitions. In Drosophila, several layers of post-transcriptional regulation ensure that the mRNAs required for these processes are expressed in a timely manner and as needed during germline differentiation.

Ribosomal protein RACK1 enhances translation of poliovirus and other viral IRESs.

Viruses have evolved strategies to ensure efficient translation using host cell ribosomes and translation factors. In addition to cleaving translation initiation factors required for host cell translation, poliovirus (PV) uses an internal ribosome entry site (IRES). Recent studies suggest that viruses exploit specific ribosomal proteins to enhance translation of their viral proteins.

Enhancing ICD-Code-Based Case Definition for Heart Failure Using Electronic Medical Record Data.

Background: Surveillance and outcome studies for heart failure (HF) require accurate identification of patients with HF. Algorithms based on International Classification of Diseases (ICD) codes to identify HF from administrative data are inadequate owing to their relatively low sensitivity. Detailed clinical information from electronic medical records (EMRs) is potentially useful for improving ICD algorithms.

Sequential Regulation of Maternal mRNAs through a Conserved cis-Acting Element in Their 3' UTRs.

Maternal mRNAs synthesized during oogenesis initiate the development of future generations. Some maternal mRNAs are either somatic or germline determinants and must be translationally repressed until embryogenesis. However, the translational repressors themselves are temporally regulated.

Tip60 complex promotes expression of a differentiation factor to regulate germline differentiation in female Drosophila.

Germline stem cells (GSCs) self-renew and differentiate to sustain a continuous production of gametes. In the female Drosophila germ line, two differentiation factors, bag of marbles ( bam) and benign gonial cell neoplasm ( bgcn), work in concert in the stem cell daughter to promote the generation of eggs. In GSCs, bam transcription is repressed by signaling from the niche and is activated in stem cell daughters.

Risk stratification of high-risk metastatic neuroblastoma: A report from the HR-NBL-1/SIOPEN study.

Background: Risk stratification is crucial to treatment decision-making in neuroblastoma. This study aimed to explore factors present at diagnosis affecting outcome in patients aged ≥18 months with metastatic neuroblastoma and to develop a simple risk score for prognostication.

Procedure: Data were derived from the European high-risk neuroblastoma 1 (HR-NBL1)/International Society for Paediatric Oncology European Neuroblastoma (SIOPEN) trial with analysis restricted to patients aged ≥18 months with metastatic disease and treated prior to the introduction of immunotherapy.