AIMC Topic: Electronic Health Records

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Detecting Opioid-Related Aberrant Behavior using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The United States is in the midst of a prescription opioid epidemic, with the number of yearly opioid-related overdose deaths increasing almost fourfold since 2000. To more effectively prevent unintentional opioid overdoses, the medical profession re...

Representation of Social History Factors Across Age Groups: A Topic Analysis of Free-Text Social Documentation.

AMIA ... Annual Symposium proceedings. AMIA Symposium
As individuals age, there is potential for dramatic changes in the social and behavioral determinants that affect health status and outcomes. The importance of these determinants has been increasingly recognized in clinical decision-making. We sought...

A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In this paper, we propose a novel neural network architecture for clinical text mining. We formulate this hybrid neural network model (HNN), composed of recurrent neural network and deep residual network, to jointly predict the presence and period as...

Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.

AMIA ... Annual Symposium proceedings. AMIA Symposium
De-identification of clinical notes is a special case of named entity recognition. Supervised machine-learning (ML) algorithms have achieved promising results for this task. However, ML-based de-identification systems often require annotating a large...

Exploiting Unlabeled Texts with Clustering-based Instance Selection for Medical Relation Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Classifying relations between pairs of medical concepts in clinical texts is a crucial task to acquire empirical evidence relevant to patient care. Due to limited labeled data and extremely unbalanced class distributions, medical relation classificat...

The Dependence of Machine Learning on Electronic Medical Record Quality.

AMIA ... Annual Symposium proceedings. AMIA Symposium
There is growing interest in applying machine learning methods to Electronic Medical Records (EMR). Across different institutions, however, EMR quality can vary widely. This work investigated the impact of this disparity on the performance of three a...

Mining Electronic Health Records to Extract Patient-Centered Outcomes Following Prostate Cancer Treatment.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The clinical, granular data in electronic health record (EHR) systems provide opportunities to improve patient care using informatics retrieval methods. However, it is well known that many methodological obstacles exist in accessing data within EHRs....

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
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...

Predicting Inpatient Acute Kidney Injury over Different Time Horizons: How Early and Accurate?

AMIA ... Annual Symposium proceedings. AMIA Symposium
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...

Deep Learning Solutions for Classifying Patients on Opioid Use.

AMIA ... Annual Symposium proceedings. AMIA Symposium
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...