AIMC Topic: Electronic Health Records
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Nursing documentation of symptoms is associated with higher risk of emergency department visits and hospitalizations in homecare patients.
Nursing outlook
Dec 29, 2020
BACKGROUND: Nurses often document patient symptoms in narrative notes.
Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.
European journal of cancer (Oxford, England : 1990)
Dec 26, 2020
BACKGROUND: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient ...
Humans
Machine Learning
Prognosis
Retrospective Studies
Middle Aged
Disease Progression
Antineoplastic Combined Chemotherapy Protocols
Young Adult
Follow-Up Studies
Adult
Electronic Health Records
Survival Rate
Female
Natural Language Processing
Breast Neoplasms
Aged
Receptor, ErbB-2
Aged, 80 and over
Receptors, Estrogen
Receptors, Progesterone
A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling.
International journal of medical informatics
Dec 24, 2020
BACKGROUND: Secondary use of Electronic Health Records (EHRs) has mostly focused on health conditions (diseases and drugs). Function is an important health indicator in addition to morbidity and mortality. Nevertheless, function has been overlooked i...
Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing.
Journal of emergency nursing
Dec 24, 2020
INTRODUCTION: Triage is critical to mitigating the effect of increased volume by determining patient acuity, need for resources, and establishing acuity-based patient prioritization. The purpose of this retrospective study was to determine whether hi...
Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure.
Annals of emergency medicine
Dec 24, 2020
STUDY OBJECTIVE: We use variables from a recently derived acute heart failure risk-stratification rule (STRATIFY) as a basis to develop and optimize risk prediction using additional patient clinical data from electronic health records and machine-lea...
Prediction of Incident Heart Failure in TTR Val122Ile Carriers One Year Ahead of Diagnosis in a Multiethnic Biobank.
The American journal of cardiology
Dec 18, 2020
Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection.
Scientific reports
Dec 17, 2020
Recent advancements in deep learning have led to a resurgence of medical imaging and Electronic Medical Record (EMR) models for a variety of applications, including clinical decision support, automated workflow triage, clinical prediction and more. H...
Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning.
Journal of women's health (2002)
Dec 16, 2020
The impact of sex and gender in the incidence and severity of coronavirus disease 2019 (COVID-19) remains controversial. Here, we aim to describe the characteristics of COVID-19 patients at disease onset, with special focus on the diagnosis and mana...
Using multivariate long short-term memory neural network to detect aberrant signals in health data for quality assurance.
International journal of medical informatics
Dec 16, 2020
BACKGROUND: The data quality of electronic health records (EHR) has been a topic of increasing interest to clinical and health services researchers. One indicator of possible errors in data is a large change in the frequency of observations in chroni...
Treatment effect prediction with adversarial deep learning using electronic health records.
BMC medical informatics and decision making
Dec 14, 2020
BACKGROUND: Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized cl...
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