Studies in health technology and informatics
40326654
INTRODUCTION: Rapid advances in Artificial Intelligence (AI), especially with large language models, present both opportunities and challenges in healthcare. This article analyzes real-world AI-related harms in healthcare.
IEEE journal of biomedical and health informatics
25807574
In this paper, a hierarchical learning algorithm is developed for classifying large-scale patient records, e.g., categorizing large-scale patient records into large numbers of known patient categories (i.e., thousands of known patient categories) for...
Journal of the American Medical Informatics Association : JAMIA
27174893
OBJECTIVE: Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled traini...
Studies in health technology and informatics
28423797
Secondary use of clinical data for research requires a method to quickly process the data so that researchers can quickly extract cohorts. We present two advances in the High Throughput Phenotyping NLP system which support the aim of truly high throu...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
30864310
The proliferation of healthcare data has brought the opportunities of applying data-driven approaches, such as machine learning methods, to assist diagnosis. Recently, many deep learning methods have been shown with impressive successes in predicting...
BACKGROUND: The aim of this study was to investigate the influence of convolution kernel and iterative reconstruction on the diagnostic performance of radiomics and deep learning (DL) in lung adenocarcinomas.
BACKGROUND: Free-text imposes a challenge in health data analysis since the lack of structure makes the extraction and integration of information difficult, particularly in the case of massive data. An appropriate machine-interpretation of electronic...
BMC medical informatics and decision making
31856806
BACKGROUND: Diagnosis aims to predict the future health status of patients according to their historical electronic health records (EHR), which is an important yet challenging task in healthcare informatics. Existing diagnosis prediction approaches m...
Data drift refers to differences between the data used in training a machine learning (ML) model and that applied to the model in real-world operation. Medical ML systems can be exposed to various forms of data drift, including differences between th...
Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. Traditional biomedical embedding techniques often replace all synonymous words representing biologic...