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Information Technology

Latest AI and machine learning research in information technology for healthcare professionals.

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Data science and machine learning in anesthesiology.

Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods ...

Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation.

BACKGROUND: Scalable and accurate health outcome prediction using electronic health record (EHR) dat...

Diabetic retinopathy and ultrawide field imaging.

The introduction of ultrawide field imaging has allowed the visualization of approximately 82% of th...

Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN).

The medical and machine learning communities are relying on the promise of artificial intelligence (...

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial.

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligib...

Using FHIR to Construct a Corpus of Clinical Questions Annotated with Logical Forms and Answers.

This paper describes a novel technique for annotating logical forms and answers for clinical questio...

Predicting Wait Times in Pediatric Ophthalmology Outpatient Clinic Using Machine Learning.

Patient perceptions of wait time during outpatient office visits can affect patient satisfaction. Pr...

Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods.

Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment ...

Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underre...

Regional Variations in Documentation of Sexual Trauma Concepts in Electronic Medical Records in the United States Veterans Health Administration.

Experiences of sexual trauma are associated with adverse patient and health system outcomes, but ar...

Machine Learned Mapping of Local EHR Flowsheet Data to Standard Information Models using Topic Model Filtering.

Electronic health record (EHR) data must be mapped to standard information models for interoperabili...

Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.

Opioid addiction in the United States has come to national attention as opioid overdose (OD) related...

Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.

In this work, we utilize a combination of free-text and structured data to build Acute Respiratory D...

EMR-Based Phenotyping of Ischemic Stroke Using Supervised Machine Learning and Text Mining Techniques.

Ischemic stroke is a major cause of death and disability in adulthood worldwide. Because it has high...

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive am...

The application of unsupervised deep learning in predictive models using electronic health records.

BACKGROUND: The main goal of this study is to explore the use of features representing patient-level...

Learning Personalized Treatment Rules from Electronic Health Records Using Topic Modeling Feature Extraction.

To address substantial heterogeneity in patient response to treatment of chronic disorders and achie...

Economic Evaluation of Robot-Based Telemedicine Consultation Services.

Through information and communication technology, telemedicine can deliver medical care without tim...

Assessing stroke severity using electronic health record data: a machine learning approach.

BACKGROUND: Stroke severity is an important predictor of patient outcomes and is commonly measured w...

Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.

BACKGROUND: Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. E...

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