AIMC Topic: Electronic Health Records

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Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System.

Diabetes care May 15, 2020
OBJECTIVE: To determine if natural language processing (NLP) improves detection of nonsevere hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of fu...
Electronic Health Records Female Aged Aged, 80 and over Humans Algorithms Clinical Decision Rules United States Young Adult Middle Aged Predictive Value of Tests Adult Severity of Illness Index Natural Language Processing Diabetes Mellitus, Type 2 Male International Classification of Diseases Community Health Planning Hypoglycemia Information Storage and Retrieval
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On using electronic health records to improve optimal treatment rules in randomized trials.

Biometrics May 14, 2020
Individualized treatment rules (ITRs) tailor medical treatments according to patient-specific characteristics in order to optimize patient outcomes. Data from randomized controlled trials (RCTs) are used to infer valid ITRs using statistical and mach...
Algorithms Research Design Electronic Health Records Humans Machine Learning Randomized Controlled Trials as Topic
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Cohort selection for clinical trials using multiple instance learning.

Journal of biomedical informatics May 1, 2020
Identifying patients eligible for clinical trials using electronic health records (EHRs) is a challenging task usually requiring a comprehensive analysis of information stored in multiple EHRs of a patient. The goal of this study is to investigate di...
Electronic Health Records Cohort Studies Patient Selection Motivation Humans Machine Learning
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Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records.

BMC medical informatics and decision making Apr 30, 2020
BACKGROUND: Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and models are limited in biomedical...
Electronic Health Records Language Deep Learning Humans PubMed Machine Learning Data Mining Information Storage and Retrieval
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Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records.

Diabetes care Apr 29, 2020
OBJECTIVE: We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms.
Area Under Curve Hypoglycemia United Kingdom Medical History Taking Models, Theoretical Inpatients Predictive Value of Tests Middle Aged Blood Glucose Cohort Studies Prognosis Electronic Health Records Female Humans Aged, 80 and over Hospitalization Machine Learning Male Aged Algorithms
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Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.

BMC medical informatics and decision making Apr 29, 2020
BACKGROUND: Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at e...
Natural Language Processing Opioid-Related Disorders Unified Medical Language System Electronic Health Records Adult Inpatients Medical Records Humans Machine Learning
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Chinese clinical named entity recognition with variant neural structures based on BERT methods.

Journal of biomedical informatics Apr 28, 2020
Clinical Named Entity Recognition (CNER) is a critical task which aims to identify and classify clinical terms in electronic medical records. In recent years, deep neural networks have achieved significant success in CNER. However, these methods requ...
Text Messaging Neural Networks, Computer Electronic Health Records China
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Harmonized representation learning on dynamic EHR graphs.

Journal of biomedical informatics Apr 25, 2020
With the rise of deep learning, several recent studies on deep learning-based methods for electronic health records (EHR) successfully address real-world clinical challenges by utilizing effective representations of medical entities. However, existin...
Humans Machine Learning Electronic Health Records
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BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.

PLoS computational biology Apr 23, 2020
A massive number of biological entities, such as genes and mutations, are mentioned in the biomedical literature. The capturing of the semantic relatedness of biological entities is vital to many biological applications, such as protein-protein inter...
Semantics Humans Protein Interaction Mapping Algorithms Electronic Health Records Publications Drug Interactions Deep Learning PubMed Computational Biology Data Mining Databases, Protein
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Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.

The Journal of clinical psychiatry Apr 21, 2020
OBJECTIVE: Machine learning algorithms in electronic medical records can classify patients by suicide risk, but no research has explored clinicians' perceptions of suicide risk flags generated by these algorithms, which may affect algorithm implement...
Physicians Electronic Health Records Female Risk Assessment Attitude of Health Personnel Adult Male Machine Learning Algorithms Health Personnel Humans Suicide Prevention False Negative Reactions False Positive Reactions
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