AIMC Topic: Electronic Health Records

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Detection of Suicidality in Adolescents with Autism Spectrum Disorders: Developing a Natural Language Processing Approach for Use in Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Over 15% of young people with autism spectrum disorders (ASD) will contemplate or attempt suicide during adolescence. Yet, there is limited evidence concerning risk factors for suicidality in childhood ASD. Electronic health records (EHRs) can be use...
Risk Factors Adolescent Suicidal Ideation Natural Language Processing Datasets as Topic Information Storage and Retrieval Data Analysis Autism Spectrum Disorder Electronic Health Records Humans London
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Predicting Inpatient Acute Kidney Injury over Different Time Horizons: How Early and Accurate?

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Incidence of Acute Kidney Injury (AKI) has increased dramatically over the past two decades due to rising prevalence of comorbidities and broadening repertoire of nephrotoxic medications. Hospitalized patients with AKI are at higher risk for complica...
Models, Theoretical Risk Factors Acute Kidney Injury Academic Medical Centers Electronic Health Records Hospitalization Humans Female Young Adult Adult Machine Learning Male Middle Aged
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Deep Learning Solutions for Classifying Patients on Opioid Use.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Opioid analgesics, as commonly prescribed medications used for relieving pain in patients, are especially prevalent in US these years. However, an increasing amount of opioid misuse and abuse have caused lots of consequences. Researchers and clinicia...
Datasets as Topic Practice Patterns, Physicians' Drug Prescriptions Analgesics, Opioid Opioid-Related Disorders Young Adult Electronic Health Records Adult Male Middle Aged Female Deep Learning Aged Adolescent Humans Neural Networks, Computer
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Detecting Evidence of Intra-abdominal Surgical Site Infections from Radiology Reports Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Free-text reports in electronic health records (EHRs) contain medically significant information - signs, symptoms, findings, diagnoses - recorded by clinicians during patient encounters. These reports contain rich clinical information which can be le...
Machine Learning Vocabulary, Controlled Electronic Health Records Abdomen Humans Datasets as Topic Reference Standards Radiology Information Systems Natural Language Processing Radiography Surgical Wound Infection
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Improving the 'Fitness for Purpose' of Common Data Models through Realism Based Ontology.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Common data models are designed and built based on requirements that are aimed towards fitness for purpose. But when common data models are used as lenses through which reality is observed from the perspective according to which they are built, then ...
Databases, Factual Electronic Health Records Big Data Information Storage and Retrieval Biological Ontologies Common Data Elements
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Intelligent Word Embeddings of Free-Text Radiology Reports.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to th...
Natural Language Processing Datasets as Topic Semantics Humans Machine Learning Electronic Health Records Intracranial Hemorrhages Radiology Information Systems
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Reimagining Clinical Documentation With Artificial Intelligence.

Mayo Clinic proceedings Apr 7, 2018
Humans Artificial Intelligence Natural Language Processing Patient-Centered Care Practice Patterns, Physicians' Electronic Health Records Attitude of Health Personnel United States
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Leveraging text skeleton for de-identification of electronic medical records.

BMC medical informatics and decision making Mar 22, 2018
BACKGROUND: De-identification is the first step to use these records for data processing or further medical investigations in electronic medical records. Consequently, a reliable automated de-identification system would be of high value.
Humans Data Anonymization Neural Networks, Computer Electronic Health Records
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Causal risk factor discovery for severe acute kidney injury using electronic health records.

BMC medical informatics and decision making Mar 22, 2018
BACKGROUND: Acute kidney injury (AKI), characterized by abrupt deterioration of renal function, is a common clinical event among hospitalized patients and it is associated with high morbidity and mortality. AKI is defined in three stages with stage-3...
Knowledge Discovery Electronic Health Records Risk Factors Natural Language Processing Humans Acute Kidney Injury
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Automated ICD-9 Coding via A Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics Mar 20, 2018
ICD-9 (the Ninth Revision of International Classification of Diseases) is widely used to describe a patient's diagnosis. Accurate automated ICD-9 coding is important because manual coding is expensive, time-consuming, and inefficient. Inspired by the...
Neural Networks, Computer Algorithms Electronic Health Records Humans Medical Informatics International Classification of Diseases Clinical Coding Reproducibility of Results Deep Learning Biomimetics
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