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Dataset of anonymized clinical events and notes from obstetric electronic health records for NLP.

Interoperability remains a global challenge in healthcare systems. Addressing the need for standardized health data to ensure compatibility … scale observational research. In this work, we present a Spanish-language dataset comprising 23,879 electronic health records (EHRs), each corresponding to a unique patient treated in a gynecology and obstetrics setting at the Clínica Universitaria Bolivariana, a national reference institution in Colombia. The dataset was standardized according to the Observational Medical …

Characterizing Autism Phenotype Onset in the Electronic Health Records to Enable Age-Specific Screening.

Autism exhibits substantial phenotypic heterogeneity, yet age-dependent variation in phenotypic profiles and onset timing remains poorly understood. We aimed to characterize stage-specific phenotypes and temporal onset patterns of autism using longitudinal electronic health records (EHRs). We analyzed EHR data from Columbia University Irving Medical Center. Autism cases were stratified by age at diagnosis. Phenotype enrichment analyses were performed using chi-square and Fisher's exact tests to identify phenotypes overrepresented in autism cases compared with matched controls, and temporal pat…

Natural Language Processing to Identify Substance Use in Electronic Health Records: A Scoping Review.

BACKGROUND: This scoping review aimed to characterize natural language processing (NLP) techniques deployed for identifying substance use in electronic health records (EHRs) and to compare the performance of these techniques by substance type. METHODS: We conducted a systematic search of PubMed, the Cochrane Library, Embase, Web of Science, ACM Digital Library, IEEE Xplore, and Scopus for peer-reviewed original research published in English. Studies were eligible if they applied NLP to identify non-prescription or problematic substance use in EHRs, provided full-text access, and reported quan…

Post-treatment infection prediction in CLL using domain adaptation of lymphoma electronic health records.

Background and purposeInfections are the leading cause of morbidity and mortality in patients with chronic lymphocytic leukemia (CLL) and occur during and after treatment. When deciding on the type of CLL treatment, the risk of infections is typically assessed based only on age and comorbidities; therefore, there is a need to develop a predictive model that incorporates information from multiple data sources. However, training an effective machine learning model requires a large sample size. Patient/material and methods: In this study, we developed a machine learning approach using domain adap

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… Although DA and transfer learning are common in other fields, their use in healthcare has largely been limited to medical imaging [ 40 ] due to challenges in integrating diverse electronic health records [ 41 ]. … A review of recent work in transfer learning and domain adaptation for natural language processing of electronic health records . Yearb Med Inform . 2021 ; 30 ( 1 ): 239 . 10.1055/S-0041-1726522 34479396 PMC8416218 [42] Crassini KR , Best OG , Mulligan SP . …

Explainable AI machine learning framework for chronic kidney disease prediction utilizing electronic health records.

BACKGROUND: Chronic Kidney Disease (CKD) is one of the significant health issues in the world which is usually unnoticeable and afterwards causes greater morbidity and mortality. Timely intervention is important in early diagnosis that will enhance patient outcomes. METHODS: This study proposes an explainable artificial intelligence driven machine learning (ML) framework for CKD prediction using electronic health records. A dataset of 398 patients (241 CKD and 157 non-CKD) from Pakistan Kidney Center, Abbottabad, was analyzed using multiple models, including logistic regression, decision tree…

Using Electronic Health Records to Enhance Lyme Disease Surveillance: Protocol for the SubLyme Network.

BACKGROUND: Lyme disease is the most common vector-borne illness in the United States. The limitations of traditional surveillance strategies for Lyme disease affect the ability to reliably track its burden and evaluate interventions. The US Centers for Disease Control and Prevention (CDC) established the Surveillance Based Lyme Disease Network (SubLyme) in September 2023 to strengthen Lyme disease surveillance and research using electronic health record (EHR) data. OBJECTIVE: SubLyme has three primary objectives: (1) to establish and evaluate criteria for identifying Lyme disease cases in EH

Zero-shot medical event prediction using a generative pretrained transformer on electronic health records.

ObjectivesLongitudinal data in electronic health records (EHRs) represent an individual's clinical history through a sequence of codified concepts, including diagnoses, procedures, medications, and laboratory tests. Generative pretrained transformers (GPT) can leverage this data to predict future events. While fine-tuning of these models can enhance task-specific performance, it becomes costly when applied to many clinical prediction tasks. In contrast, a pretrained foundation model can be used in zero-shot forecasting setting, offering a scalable alternative to fine-tuning separate models for…

Evaluating the completeness of electronic health records in dental education: a big data study.

OBJECTIVES: The BigMouth Dental Data Repository is an oral health database developed from de-identified electronic health record (EHR) data from eleven dental schools within the United States. To better understand how this database can be used for further research, the repository must be analyzed for data quality, such as accuracy, consistency, and completeness. This study determined the completeness of all patient health records between 2017 and 2019, including demographic, dental, behavioral, and health history variables at the students, faculty, and resident level. METHODS: This study anal

Leveraging electronic health records for detection of acute kidney injury in critical care units.

PURPOSE: To assess the incidence of acute kidney injury (AKI) and identify associated risk factors using Kidney Disease Improving Global Outcomes (KDIGO) criteria through the analysis of electronic health record (EHR) data in ICUs. METHODS: A retrospective observational study was conducted using EHR data from 563 adult ICU patients admitted to a large public hospital in Jordan between January and December 2019, analyzing demographic, clinical, and medication variables through univariate and multivariate logistic regression. RESULTS: The incidence of AKI was 24.5%, with significant risk facto

Do electronic health records used by primary care practices support recommended alcohol-related care?

OBJECTIVE: The quality of alcohol-related prevention and treatment in US primary care is poor. The purpose of this study was to describe the extent to which Electronic Health Records (EHRs) used by 167 primary care practices across 7 states currently include the necessary prompts, clinical support, and performance reporting essential for improving alcohol-related prevention and treatment in primary care. MATERIALS AND METHODS: Experts from five regional quality improvement programs identified basic EHR features needed to support evidence-based alcohol-related prevention (ie, screening and bri…

Clinician Note Documentation of Parental Justice Involvement: Preliminary Evidence from Pediatric Electronic Health Records.

Little is known about clinical documentation for youth exposed to parental justiceinvolvement (e.g., parole, probation, jail, prison). We reviewed the electronic health records of 100 youth with at least one mention of parental incarceration between 2011-2019 from a large Midwestern pediatric hospital-based institution to describe clinical documentation and health characteristics. Within the sample, youth more commonly experienced incarceration of a father-identified figure (68%) as opposed to a mother-identified figure (32%). Seventeen percent (17%) of the youth were between zero and four yea…

EHR-BERT: A BERT-based model for effective anomaly detection in electronic health records.

OBJECTIVE: Physicians and clinicians rely on data contained in electronic health records (EHRs), as recorded by health information technology (HIT), to make informed decisions about their patients. The reliability of HIT systems in this regard is critical to patient safety. Consequently, better tools are needed to monitor the performance of HIT systems for potential hazards that could compromise the collected EHRs, which in turn could affect patient safety. In this paper, we propose a new framework for detecting anomalies in EHRs using sequence of clinical events. This new framework, EHR-Bidir…

Predicting maintenance lithium response for bipolar disorder from electronic health records-a retrospective study.

BACKGROUND: Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited. METHOD: We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monothera…

Postpartum hemorrhage in electronic health records: risk factors at admission and in-hospital occurrence.

OBJECTIVE: Postpartum hemorrhage (PPH) is the leading cause of maternal death globally. Therefore, prevention strategies have been created. The study aimed to evaluate the occurrence of PPH and its risk factors after implementing a risk stratification at admission in a teaching hospital. METHODS: A retrospective cohort involving a database of SISMATER® electronic medical record. Classification in low, medium, or high risk for PPH was performed through data filled out by the obstetrician-assistant. PPH frequency was calculated, compared among these groups and associated with the risk factors.

Development and validation of a self-updating gout register from electronic health records data.

OBJECTIVE: To develop an automatic gout register from electronic health records (EHRs) data. METHODS: We analysed the EHR of all patients >18 years old from a tertiary academic hospital (2013-2022) based on six criteria: International Classification of Diseases 10 gout diagnosis, urate-lowering therapy prescription, monosodium urate crystals in joint aspiration and gout-related terms in problem lists, clinical or imaging reports. We assessed the positive and negative predictive value (PPV and NPV) of the query by chart reviews. RESULTS: Of 2 110 902 outpatients and inpatients, 10 289 had at …

Factors Associated With Treatment Escalation for Psoriasis: An Analysis of Electronic Health Records Data.

BACKGROUND: Electronic health records (EHRs) offer the possibility of using data entry templates to simultaneously document routine clinical care and capture disease-specific measures as discrete data elements that can be used for health services research (HSR). The objective of this study was to determine factors associated with meaningful treatment escalation (MTE) of psoriasis as a pilot study for future real-world HSR studies. METHODS: We conducted a retrospective, observational cohort study of psoriasis patients by using data collected during routine clinical care from an EHR using EpiCa…

Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records.

IMPORTANCE: Half of the people who die by suicide make a health care visit within 1 month of their death. However, clinicians lack the tools … S: Suicide attempts 1 and 6 months after presentation to the ED were defined by combining data from electronic health records (EHRs) with patient 1-month (n = 1102) and 6-month (n = 1220) follow-up surveys. Ensemble machine learning was used to develop predictive models and a risk score for suicide. RESULTS: A total of 1818 patients participated in this study (1016 men [5 …

Pediatric sepsis phenotypes for enhanced therapeutics: An application of clustering to electronic health records.

OBJECTIVE: The heterogeneity of pediatric sepsis patients suggests the potential benefits of clustering analytics to derive phenotypes with distinct host response patterns that may help guide personalized therapeutics. We evaluate the relative performance of latent class analysis (LCA) and K-means, 2 commonly used clustering methods toward the derivation of clinically useful pediatric sepsis phenotypes. METHODS: Data were extracted from anonymized medical records of 6446 pediatric patients that presented to 1 of 6 emergency departments (EDs) between 2013 and 2018 and were thereafter admitted.

Characterization of genetic and phenotypic heterogeneity of obstructive sleep apnea using electronic health records.

BACKGROUND: Obstructive sleep apnea (OSA) is defined by frequent episodes of reduced or complete cessation of airflow during sleep and is linked to negative health outcomes. Understanding the genetic factors influencing expression of OSA may lead to new treatment strategies. Electronic health records (EHRs) can be leveraged to both validate previously reported OSA-associated genomic variation and detect novel relationships between these variants and comorbidities. METHODS: We identified candidate single nucleotide polymorphisms (SNPs) via systematic literature review of existing research. Usi…

A genome-wide association study of polycystic ovary syndrome identified from electronic health records.

BACKGROUND: Polycystic ovary syndrome is the most common endocrine disorder affecting women of reproductive age. A number of criteria have b … ed polycystic ovary syndrome cases and unaffected controls based on the Rotterdam criteria from the electronic health records and performed a discovery-validation genome-wide association study. STUDY DESIGN: We developed a polycystic ovary syndrome phenotyping algorithm on the basis of the Rotterdam criteria and applied it to 3 electronic health record-linked biobanks to …