Information Technology

The practical implementation of artificial intelligence technologies in medicine.

The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability acro...

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.

Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common geneti...

Artificial intelligence-based decision-making for age-related macular degeneration.

Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care q...

Quantitative analysis of manual annotation of clinical text samples.

BACKGROUND: Semantic interoperability of eHealth services within and across countries has been the main topic in several research projects. It is a ke...

Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy.

PURPOSE: The purpose of this study was to compare the effectiveness of ensemble methods (e.g., random forests) and single-model methods (e.g., logisti...

EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.

BACKGROUND: There has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Record...

Ascertaining Depression Severity by Extracting Patient Health Questionnaire-9 (PHQ-9) Scores from Clinical Notes.

The Patient Health Questionnaire-9 (PHQ-9) is a validated instrument for assessing depression severity. While some electronic health record (EHR) syst...

Generalized Extraction and Classification of Span-Level Clinical Phrases.

Much of the critical information in a patient's electronic health record (EHR) is hidden in unstructured text. As such, there is an increasing role fo...

Optimizing Corpus Creation for Training Word Embedding in Low Resource Domains: A Case Study in Autism Spectrum Disorder (ASD).

Automating the extraction of behavioral criteria indicative of Autism Spectrum Disorder (ASD) in electronic health records (EHRs) can contribute signi...

Standardizing Heterogeneous Annotation Corpora Using HL7 FHIR for Facilitating their Reuse and Integration in Clinical NLP.

Manually annotated clinical corpora are commonly used as the gold standards for the training and evaluation of clinical natural language processing (N...

Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity.

Accurate image-based medical diagnosis relies upon adequate image quality and clarity. This has important implications for clinical diagnosis, and for...

Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records.

Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans i...

Using Machine Learning to Predict the Information Seeking Behavior of Clinicians Using an Electronic Medical Record System.

Poor electronic medical record (EMR) usability is detrimental to both clinicians and patients. A better EMR would provide concise, context sensitive p...

Identifying Cases of Metastatic Prostate Cancer Using Machine Learning on Electronic Health Records.

Cancer stage is rarely captured in structured form in the electronic health record (EHR). We evaluate the performance of a classifier, trained on stru...

Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.

BACKGROUND: Pythia is an automated, clinically curated surgical data pipeline and repository housing all surgical patient electronic health record (EH...

Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals.

BACKGROUND: The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorit...

Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial int...

Artificial intelligence and deep learning in ophthalmology.

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image r...

QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments.

BACKGROUND: Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and ...

Using neural attention networks to detect adverse medical events from electronic health records.

The detection of Adverse Medical Events (AMEs) plays an important role in disease management in ensuring efficient treatment delivery and quality impr...