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Electronic Health Records

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An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration.

Cancer medicine
BACKGROUND: The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM.

Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning.

Sensors (Basel, Switzerland)
The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchai...

Aci-bench: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation.

Scientific data
Recent immense breakthroughs in generative models such as in GPT4 have precipitated re-imagined ubiquitous usage of these models in all applications. One area that can benefit by improvements in artificial intelligence (AI) is healthcare. The note ge...

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

Artificial intelligence in medicine
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 learni...

Application of Natural Language Processing in Total Joint Arthroplasty: Opportunities and Challenges.

The Journal of arthroplasty
Total joint arthroplasty is becoming one of the most common surgeries within the United States, creating an abundance of analyzable data to improve patient experience and outcomes. Unfortunately, a large majority of this data is concealed in electron...

Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data.

The Journal of arthroplasty
Electronic health records have facilitated the extraction and analysis of a vast amount of data with many variables for clinical care and research. Conventional regression-based statistical methods may not capture all the complexities in high-dimensi...

Identifying the most important data for research in the field of infectious diseases: thinking on the basis of artificial intelligence.

Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia
OBJECTIVE: Clinical data on which artificial intelligence (AI) algorithms are trained and tested provide the basis to improve diagnosis or treatment of infectious diseases (ID). We aimed to identify important data for ID research to prioritise effort...

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

Neural networks : the official journal of the International Neural Network Society
Accurate estimation of in-hospital mortality based on patients' physiological time series data improves the performance of the clinical decision support systems and assists hospital providers in allocating resources. In practice, the data quality iss...

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

Child maltreatment
Survivors of child sex trafficking (SCST) experience high rates of adverse health outcomes. Amidst the duration of their victimization, survivors regularly seek healthcare yet fail to be identified. This study sought to utilize artificial intelligenc...