AIMC Topic: Electronic Health Records
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Prediction of severe chest injury using natural language processing from the electronic health record.
Injury
Oct 25, 2020
INTRODUCTION: Trauma injury severity scores are currently calculated retrospectively from the electronic health record (EHR) using manual annotation by certified trauma coders. Natural language processing (NLP) of clinical documents in the EHR may en...
Cross domains adversarial learning for Chinese named entity recognition for online medical consultation.
Journal of biomedical informatics
Oct 23, 2020
Deep learning methods have been applied to Chinese named entity recognition for the online medical consultation. They require a large number of marked samples. However, no such database is available at present. This paper begins with constructing a l...
Prediction of obstetrical and fetal complications using automated electronic health record data.
American journal of obstetrics and gynecology
Oct 22, 2020
An increasing number of delivering women experience major morbidity and mortality. Limited work has been done on automated predictive models that could be used for prevention. Using only routinely collected obstetrical data, this study aimed to devel...
Routinely Collected Health Data
Uterine Rupture
Obstetric Labor Complications
Stillbirth
Clinical Decision Rules
Pregnancy in Obesity
Pregnancy
Maternal Age
Female
Postpartum Hemorrhage
Humans
Premature Birth
Hypoxia-Ischemia, Brain
Parity
Time Factors
Machine Learning
Blood Pressure
Reproducibility of Results
Retrospective Studies
Pre-Eclampsia
Adult
Electronic Health Records
A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study.
JMIR public health and surveillance
Oct 22, 2020
BACKGROUND: Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities throu...
Automatic classification of scanned electronic health record documents.
International journal of medical informatics
Oct 17, 2020
OBJECTIVES: Electronic Health Records (EHRs) contain scanned documents from a variety of sources such as identification cards, radiology reports, clinical correspondence, and many other document types. We describe the distribution of scanned document...
Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation.
Journal of medical Internet research
Oct 16, 2020
BACKGROUND: Fractures as a result of osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Comm...
Extracting medication information from unstructured public health data: a demonstration on data from population-based and tertiary-based samples.
BMC medical research methodology
Oct 15, 2020
BACKGROUND: Unstructured data from clinical epidemiological studies can be valuable and easy to obtain. However, it requires further extraction and processing for data analysis. Doing this manually is labor-intensive, slow and subject to error. In th...
Accelerating Surgical Site Infection Abstraction With a Semi-automated Machine-learning Approach.
Annals of surgery
Oct 14, 2020
OBJECTIVE: To demonstrate that a semi-automated approach to health data abstraction provides significant efficiencies and high accuracy.
Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing.
Methods of information in medicine
Oct 14, 2020
BACKGROUND: Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the ext...
Development of a Portable Tool to Identify Patients With Atrial Fibrillation Using Clinical Notes From the Electronic Medical Record.
Circulation. Cardiovascular quality and outcomes
Oct 14, 2020
BACKGROUND: The electronic medical record contains a wealth of information buried in free text. We created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone.
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