AIMC Topic: Electronic Health Records

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Rare disease knowledge enrichment through a data-driven approach.

BMC medical informatics and decision making
BACKGROUND: Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize t...

Discovering the Type 2 Diabetes in Electronic Health Records Using the Sparse Balanced Support Vector Machine.

IEEE journal of biomedical and health informatics
The diagnosis of type 2 diabetes (T2D) at an early stage has a key role for an adequate T2D integrated management system and patient's follow-up. Recent years have witnessed an increasing amount of available electronic health record (EHR) data and ma...

Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence.

Nature medicine
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive e...

Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.

PloS one
Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity...

Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death.

Depression and anxiety
BACKGROUND: Identification of individuals at increased risk for suicide is an important public health priority, but the extent to which considering clinical phenomenology improves prediction of longer term outcomes remains understudied. Hospital disc...

Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks.

BMC bioinformatics
BACKGROUND: Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural networks. The adven...

An automated data verification approach for improving data quality in a clinical registry.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The quality of data is crucial for clinical registry studies as it impacts credibility. In the regular practice of most such studies, a vulnerability arises from researchers recording data on paper-based case report forms (C...

Temporal indexing of medical entity in Chinese clinical notes.

BMC medical informatics and decision making
BACKGROUND: The goal of temporal indexing is to select an occurred time or time interval for each medical entity in clinical notes, so that all medical entities can be indexed on a united timeline, which could assist the understanding of clinical not...

Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text.

BMC medical informatics and decision making
BACKGROUND: Extracting relations between important clinical entities is critical but very challenging for natural language processing (NLP) in the medical domain. Researchers have applied deep learning-based approaches to clinical relation extraction...

Natural Language Processing-Identified Problem Opioid Use and Its Associated Health Care Costs.

Journal of pain & palliative care pharmacotherapy
Use of prescription opioids and problems of abuse and addiction have increased over the past decade. Claims-based studies have documented substantial economic burden of opioid abuse. This study utilized electronic health record (EHR) data to identify...