Information Technology

Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination.

BACKGROUND: Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)-integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the c...

Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.

BACKGROUND: Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at ensuring perfect PHI removal. As an alternative to relying on de-identification systems, we propose the following solutions: (1) Mapping the corpus of ...

Harmonized representation learning on dynamic EHR graphs.

With the rise of deep learning, several recent studies on deep learning-based methods for electronic health records (EHR) successfully address real-wo...

Predicting Inpatient Medication Orders From Electronic Health Record Data.

In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EH...

Automated ICD coding via unsupervised knowledge integration (UNITE).

OBJECTIVE: Accurate coding is critical for medical billing and electronic medical record (EMR)-based research. Recent research has been focused on dev...

Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study.

BACKGROUND: Electronic medical record (EMR) systems capture large amounts of data per patient and present that data to physicians with little prioriti...

Data science and machine learning in anesthesiology.

Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods that are largely inference-based, ML is geared mor...

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

BACKGROUND: Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. ...

Diabetic retinopathy and ultrawide field imaging.

The introduction of ultrawide field imaging has allowed the visualization of approximately 82% of the total retinal area compared to only 30% using 7-...

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

The medical and machine learning communities are relying on the promise of artificial intelligence (AI) to transform medicine through enabling more ac...

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 eligible trial participants is resource-intensive when r...

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 Distress Syndrome(ARDS) prediction models and ARDS ...

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 are not systematically documented in electronic heal...

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 questions by utilizing Fast Healthcare Interoperability R...

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 deaths have risen at alarming rates. Combating op...

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 interoperability and to support research across organizations. N...

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 underreporting as current methodology relies heavily on m...

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 outcomes and quality of life. Early detection of c...

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

Patient perceptions of wait time during outpatient office visits can affect patient satisfaction. Providing accurate information about wait times coul...

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 highly heterogeneous phenotypes, phenotyping of ischem...