Information Technology

Machine learning for administrative health records: A systematic review of techniques and applications.

Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of...

Smartphone Eye Examination: Artificial Intelligence and Telemedicine.

The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special interest for primary health care services. Obtaining fundus imaging with this technique has improved and democratized the teaching of fundoscopy, but in p...

A novel neural network for improved in-hospital mortality prediction with irregular and incomplete multivariate data.

Accurate estimation of in-hospital mortality based on patients' physiological time series data improves the performance of the clinical decision suppo...

Revolutionising Impacts of Artificial Intelligence on Health Care System and Its Related Medical In-Transparencies.

The application of artificial intelligence (AI) in the field of medicine has revolutionised various sectors of the health care system, including robot...

Identification of Child Survivors of Sex Trafficking From Electronic Health Records: An Artificial Intelligence Guided Approach.

Survivors of child sex trafficking (SCST) experience high rates of adverse health outcomes. Amidst the duration of their victimization, survivors regu...

Research on the digital application of telemedicine based on internet big data in the era of artificial intelligence.

Toward a stable and low-resource PLM-based medical diagnostic system via prompt tuning and MoE structure.

Machine learning (ML) has been extensively involved in assistant disease diagnosis and prediction systems to emancipate the serious dependence on medi...

Emerging Applications for Computer Vision and Artificial Intelligence in Management of the Cardiovascular Patient.

Artificial intelligence and telemedicine promise to reshape patient care to an unprecedented extent, leading to a safer and more sustainable work envi...

Artificial intelligence and urology: ethical considerations for urologists and patients.

The use of artificial intelligence (AI) in medicine and in urology specifically has increased over the past few years, during which time it has enable...

Application of Artificial Intelligence in the Early Detection of Retinopathy of Prematurity: Review of the Literature.

Retinopathy of prematurity (ROP) is a potentially blinding disease in premature neonates that requires a skilled workforce for diagnosis, monitoring, ...

Improving Diagnostics with Deep Forest Applied to Electronic Health Records.

An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this d...

RegEMR: a natural language processing system to automatically identify premature ovarian decline from Chinese electronic medical records.

BACKGROUND: The ovarian reserve is a reservoir for reproductive potential. In clinical practice, early detection and treatment of premature ovarian de...

Gaps and future of human-centered artificial intelligence in ophthalmology: Future Vision Forum consensus statement.

PURPOSE OF REVIEW: The Future Vision Forum discussed the current state of Human Centered Computing and the future of data collection, curation, and co...

Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus.

PURPOSE OF REVIEW: The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important rol...

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulat...

SCOPE: predicting future diagnoses in office visits using electronic health records.

We propose an interpretable and scalable model to predict likely diagnoses at an encounter based on past diagnoses and lab results. This model is inte...

Can the Electronic Health Record Predict Risk of Falls in Hospitalized Patients by Using Artificial Intelligence? A Meta-analysis.

Because of an aging population worldwide, the increasing prevalence of falls and their consequent injuries are becoming a safety, health, and social-c...

Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics.

UNLABELLED: Artificial intelligence (AI) and machine learning (ML) are becoming critical in developing and deploying personalized medicine and targete...

Utility of ChatGPT in Clinical Practice.

ChatGPT is receiving increasing attention and has a variety of application scenarios in clinical practice. In clinical decision support, ChatGPT has b...

Deep learning prediction models based on EHR trajectories: A systematic review.

BACKGROUND: Electronic health records (EHRs) are generated at an ever-increasing rate. EHR trajectories, the temporal aspect of health records, facili...