Information Technology

[Actively promoting the research and development of artificial intelligence diagnosis and treatment of orbital disease].

Medical artificial intelligence (AI) promotes technological revolution and industrial transformation in the medical field, and the medical level of orbital disease will also be improved with the in-depth development of AI diagnosis and treatment. The problems should be solved in the orbital disease AI research at the initial stage include: the complex knowledge system of orbital disease requires c...

5th-Generation Mobile Communication: Data Highway for Surgery 4.0.

INTRODUCTION: 5th generation cellular mobile communications (5G) is one of the main requirements for the digital future. The new standard will offer high bandwidths (10GB/s), low latency (<1ms), and a high quality of service. It is not yet known whether 5G performance is sufficient for demanding eHealth applications (e.g., telemedicine).

Putting the "why" in "EHR": capturing and coding clinical cognition.

Complaints about electronic health records, including information overload, note bloat, and alert fatigue, are frequent topics of discussion. Despite ...

Transparent Reporting on Research Using Unstructured Electronic Health Record Data to Generate 'Real World' Evidence of Comparative Effectiveness and Safety.

Research that makes secondary use of administrative and clinical healthcare databases is increasingly influential for regulatory, reimbursement, and o...

Convolutional neural networks for wound detection: the role of artificial intelligence in wound care.

OBJECTIVE: Telemedicine is an essential support system for clinical settings outside the hospital. Recently, the importance of the model for assessmen...

Text Classification to Inform Suicide Risk Assessment in Electronic Health Records.

Assessing a patient's risk of an impending suicide attempt has been hampered by limited information about dynamic factors that change rapidly in the d...

Identifying Suicidal Adolescents from Mental Health Records Using Natural Language Processing.

Suicidal ideation is a risk factor for self-harm, completed suicide and can be indicative of mental health issues. Adolescents are a particularly vuln...

Identifying Patients with Significant Problems Related to Social Determinants of Health with Natural Language Processing.

Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosoci...

Do You Need Embeddings Trained on a Massive Specialized Corpus for Your Clinical Natural Language Processing Task?

We explore the impact of data source on word representations for different NLP tasks in the clinical domain in French (natural language understanding ...

Evaluation of the Fast Healthcare Interoperability Resources (FHIR) Standard for Representation of Knowledge Bases Encoded in the Arden Syntax.

CONTEXT: Arden Syntax is a standard that encodes knowledge as Medical Logic Modules (MLMs) but lacks a standard query data model and terminology.

Relationship Between Very Cold Outside Weather and Surgical Outcome: Integrating Shallow and Deep Artificial Neural Nets.

Patients' hospital length of stay (LOS) as a surgical outcome is important indicator of quality of care. We used EMR data to build artificial neural n...

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-i...

Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records.

OBJECTIVE: Geriatric syndromes such as functional disability and lack of social support are often not encoded in electronic health records (EHRs), thu...

Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation.

OBJECTIVE: We aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic...

Open Source Infrastructure for Health Care Data Integration and Machine Learning Analyses.

PURPOSE: We have created a cloud-based machine learning system (CLOBNET) that is an open-source, lean infrastructure for electronic health record (EHR...

An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models.

OBJECTIVE: We aim to evaluate the effectiveness of advanced deep learning models (eg, capsule network [CapNet], adversarial training [ADV]) for single...

Outlier Detection in Health Record Free-Text using Deep Learning.

In recent years, machine learning approaches have been successfully applied to analysis of patient symptom data in the context of disease diagnosis, a...

Predicting Gastrointestinal Bleeding Events from Multimodal In-Hospital Electronic Health Records Using Deep Fusion Networks.

Applying machine learning (ML) methods on electronic health records (EHRs) that accurately predict the occurrence of a variety of diseases or complica...

Sparse Embedding for Interpretable Hospital Admission Prediction.

This paper introduces a sparse embedding for electronic health record (EHR) data in order to predict hospital admission. We use a k-sparse autoencoder...

A sustainable HL7 FHIR based ontology for PHR data.

One of the most widely acknowledged standards in health informatics is HL7 (Health Level 7 International). HL7 FHIR® (Fast Healthcare Interoperability...