Information Technology

Simulator, machine learning, and artificial intelligence: Time has come to assist prenatal ultrasound diagnosis.

In this Commentary authors investigated and extended the role of simulator in assisting obstetric sonographers in training program. The interconnection of different digitalized technologies such as digital data, artificial neuronal and convolutional networks, machine and deep learning, telemedicine, and output are discussed and contribute to the generation of artificial intelligence.

GPT Technology to Help Address Longstanding Barriers to Care in Free Medical Clinics.

The implementation of technology in healthcare has revolutionized patient-centered decision making by providing contextualized information about a patient's healthcare journey, leading to increased efficiency (Keyworth et al. in BMC Med Inform Decis Mak 18:93, 2018, https://doi.org/10.1186/s12911-018-0661-3 ). Artificial intelligence has been integrated within Electronic Health Records (EHR) to pr...

Developing an Automated Registry (Autoregistry) of Spine Surgery Using Natural Language Processing and Health System Scale Databases.

BACKGROUND AND OBJECTIVES: Clinical registries are critical for modern surgery and underpin outcomes research, device monitoring, and trial developmen...

A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets.

The missing data mechanism is a relevant problem in Machine Learning (ML) and biomedical informatics communities. Real-world Electronic Health Record ...

Leveraging Natural Language Processing to Improve Electronic Health Record Suicide Risk Prediction for Veterans Health Administration Users.

Suicide risk prediction models frequently rely on structured electronic health record (EHR) data, including patient demographics and health care usag...

Representation of time-varying and time-invariant EMR data and its application in modeling outcome prediction for heart failure patients.

OBJECTIVE: To represent a patient record with both time-invariant and time-varying features as a single vector using an end-to-end deep learning model...

Unlock the algorithms: regulation of adaptive algorithms in reproduction.

In the USA, the Food and Drug Administration plans to regulate artificial intelligence and machine learning software systems as medical devices to imp...

Machine learning functional impairment classification with electronic health record data.

BACKGROUND: Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the ...

Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-Based Intrusion Detection for Cybersecurity in CPS Environment.

A Cyber-Physical System (CPS) is a network of cyber and physical elements that interact with each other. In recent years, there has been a drastic inc...

Letter to the Editor - Update from Ukraine: Development of the Cloud-based Platform for Patient-centered Telerehabilitation of Oncology Patients with Mathematical-related Modeling.

This Letter to the Editor provides an update on the research from the Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine...

Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records.

BACKGROUND: An artificial-intelligence (AI) model for predicting the prognosis or mortality of coronavirus disease 2019 (COVID-19) patients will allow...

Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.

A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling ap...

Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges.

The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent i...

Artificial Intelligence-Based Ethical Hacking for Health Information Systems: Simulation Study.

BACKGROUND: Health information systems (HISs) are continuously targeted by hackers, who aim to bring down critical health infrastructure. This study w...

RIMD: A novel method for clinical prediction.

Electronic health records (EHR) are sparse, noisy, and private, with variable vital measurements and stay lengths. Deep learning models are the curren...

Radiologist Worklist Reprioritization Using Artificial Intelligence: Impact on Report Turnaround Times for CTPA Examinations Positive for Acute Pulmonary Embolism.

In patients with acute pulmonary embolism (PE), timely intervention (e.g., initiation of anticoagulation) is critical for optimizing clinical outcome...

A Multidatabase ExTRaction PipEline (METRE) for facile cross validation in critical care research.

Transforming raw EHR data into machine learning model-ready inputs requires considerable effort. One widely used EHR database is Medical Information M...

Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated.

Predictive analytics based on artificial intelligence (AI) offer clinicians the opportunity to leverage big data available in electronic health record...

Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch.

Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional tel...

Centering Patients' Voices in Artificial Intelligence‒Based Telemedicine.