Information Technology

A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

BACKGROUND: Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting ...

Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning

The Internet of Things (IoT) has been introduced as a breakthrough technology that integrates intelligence into everyday objects, enabling high levels of connectivity between them. As the IoT networks grow and expand, they become more susceptible to cybersecurity attacks. A significant challenge in current intrusion detection systems for IoT includes handling imbalanced datasets where labeled da...

The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization

Background: Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison;...

Integrating and Adopting AI in the Radiology Workflow: A Primer for Standards and Integrating the Healthcare Enterprise (IHE) Profiles.

The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom ...

[An overview of investments in the development of the Russian Medtech market and the prospects for the impact of digitalization in medicine on the economic performance of companies until 2030].

Today, the topic of digitalization, the introduction of innovations based on Big Data, the complexity of technologies due to the introduction of artif...

Comments on Contemporary Uses of Machine Learning for Electronic Health Records.

Various decisions concerning the management, display, and diagnostic use of electronic health records (EHR) data can be automated using machine learni...

A Health Care Clinical Data Platform for Rapid Deployment of Artificial Intelligence and Machine Learning Algorithms for Cancer Care and Oncology Clinical Trials.

The xCures platform aggregates, organizes, structures, and normalizes clinical EMR data across care sites, utilizing advanced technologies for near re...

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health rec...

[The ethics of IA in medicine must be based on the practical ethics of the healthcare relationship].

THE ETHICS OF IA IN MEDICINE MUST BE BASED ON THE PRACTICAL ETHICS OF THE HEALTHCARE RELATIONSHIP. Artificial intelligence (AI) offers more and more a...

Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.

OBJECTIVES: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve hea...

Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction.

With the widespread adoption of electronic health records, the amount of stored medical data has been increasing. Clinical data, often in the form of ...

Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.

OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarbox...

Technology in Medicine: Improving Clinical Documentation.

The association between electronic health record (EHR) documentation and physician burnout is well-known. A combination of insufficient time to comple...

Topic evolution before fall incidents in new fallers through natural language processing of general practitioners' clinical notes.

BACKGROUND: Falls involve dynamic risk factors that change over time, but most studies on fall-risk factors are cross-sectional and do not capture thi...

Semisupervised transfer learning for evaluation of model classification performance.

In many modern machine learning applications, changes in covariate distributions and difficulty in acquiring outcome information have posed challenges...

Making Digital Health Equitable.

Most agree that the current healthcare system is broken. Fortunately, technology is increasing at an exponential rate and provides a solution for the ...

Elucidating Discrepancy in Explanations of Predictive Models Developed Using EMR.

The lack of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms. While explainable artificial intelligen...

Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.

OBJECTIVE: Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and pos...

The DO-KB Knowledgebase: a 20-year journey developing the disease open science ecosystem.

In 2003, the Human Disease Ontology (DO, https://disease-ontology.org/) was established at Northwestern University. In the intervening 20 years, the D...

Plant Reactome Knowledgebase: empowering plant pathway exploration and OMICS data analysis.

Plant Reactome (https://plantreactome.gramene.org) is a freely accessible, comprehensive plant pathway knowledgebase. It provides curated reference pa...