Information Technology

Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.

BACKGROUND: Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperati...

Electronic Health Record Optimization for Artificial Intelligence.

Laboratory clinical decision support (CDS) typically relies on data from the electronic health record (EHR). The implementation of a sustainable, effective laboratory CDS program requires a commitment to standardization and harmonization of key EHR data elements that are the foundation of laboratory CDS. The direct use of artificial intelligence algorithms in CDS programs will be limited unless ke...

Knowledge Engineering in Chemistry: From Expert Systems to Agents of Creation.

Passing knowledge from human to human is a natural process that has continued since the beginning of humankind. Over the past few decades, we have wit...

Using natural language processing to identify opioid use disorder in electronic health record data.

BACKGROUND: As opioid prescriptions have risen, there has also been an increase in opioid use disorder (OUD) and its adverse outcomes. Accurate and co...

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators.

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implemen...

Validation of a Deep Learning-Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data.

IMPORTANCE: Lung cancer screening with chest computed tomography (CT) prevents lung cancer death; however, fewer than 5% of eligible Americans are scr...

Evaluation of Machine Learning Techniques for Traffic Flow-Based Intrusion Detection.

Cybersecurity is one of the great challenges of today's world. Rapid technological development has allowed society to prosper and improve the quality ...

A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost).

The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life ...

Using model explanations to guide deep learning models towards consistent explanations for EHR data.

It has been shown that identical deep learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that ...

Multiagent-Based Data Presentation Mechanism for Multifaceted Analysis in Network Management Tasks.

Although network management tasks are highly automated using big data and artificial intelligence technologies, when an unforeseen cybersecurity probl...

Intrusion Detection in IoT Using Deep Learning.

Cybersecurity has been widely used in various applications, such as intelligent industrial systems, homes, personal devices, and cars, and has led to ...

Successful implementation of a nurse-navigator-run program using natural language processing identifying patients with an abdominal aortic aneurysm.

BACKGROUND: Abdominal aortic aneurysms (AAA) are often identified incidentally on imaging studies. Patients and/or providers are frequently unaware of...

CATNet: Cross-event attention-based time-aware network for medical event prediction.

Medical event prediction (MEP) is a fundamental task in the healthcare domain, which needs to predict medical events, including medications, diagnosis...

Artificial intelligence-based methods for fusion of electronic health records and imaging data.

Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal...

Getting More Out of Large Databases and EHRs with Natural Language Processing and Artificial Intelligence: The Future Is Here.

Electronic health records (EHRs) have created great opportunities to collect various information from clinical patient encounters. However, most EHR d...

Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports.

BACKGROUND: Cardiac magnetic resonance (CMR) imaging is important for diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM) patients....

DeepMPM: a mortality risk prediction model using longitudinal EHR data.

BACKGROUND: Accurate precision approaches have far not been developed for modeling mortality risk in intensive care unit (ICU) patients. Conventional ...

Developing EMR-based algorithms to Identify hospital adverse events for health system performance evaluation and improvement: Study protocol.

BACKGROUND: Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative da...

Comparing two machine learning approaches in predicting lupus hospitalization using longitudinal data.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease characterized by flares ranging from mild to life-threatening. Severe flares ...

Implementation of machine learning in the clinic: challenges and lessons in prospective deployment from the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) randomized controlled study.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) have resulted in significant enthusiasm for their promise in healthcare. Despite th...