AIMC Topic: Medical Informatics Computing

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Replacing non-biomedical concepts improves embedding of biomedical concepts.

PloS one May 5, 2025
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...
Algorithms Semantics Medical Informatics Computing Machine Learning
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Data drift in medical machine learning: implications and potential remedies.

The British journal of radiology Mar 27, 2023
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...
Machine Learning Medical Informatics Computing
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Incorporating medical code descriptions for diagnosis prediction in healthcare.

BMC medical informatics and decision making Dec 19, 2019
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...
Clinical Coding Medical Informatics Computing Forecasting Heart Failure Prognosis Datasets as Topic Deep Learning Models, Statistical Electronic Health Records Humans Neural Networks, Computer
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Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes.

Thoracic cancer Aug 19, 2019
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.
Neoplasm Staging Humans Neoplasm Grading Tomography, X-Ray Computed Image Processing, Computer-Assisted Adenocarcinoma of Lung Area Under Curve Medical Informatics Computing Deep Learning ROC Curve
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Learning statistical models of phenotypes using noisy labeled training data.

Journal of the American Medical Informatics Association : JAMIA May 12, 2016
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...
Phenotype Electronic Health Records Machine Learning Medical Informatics Computing Algorithms Models, Statistical Humans Diabetes Mellitus, Type 2 Myocardial Infarction Logistic Models Vocabulary, Controlled
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Hierarchical classification of large-scale patient records for automatic treatment stratification.

IEEE journal of biomedical and health informatics Mar 19, 2015
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...
Electronic Health Records Support Vector Machine Medical Informatics Computing Humans Decision Trees
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The Unexpected Harms of Artificial Intelligence in Healthcare: Reflections on Four Real-World Cases.

Studies in health technology and informatics May 2, 2025
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.
Humans Medical Informatics Computing Artificial Intelligence
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[Automatic keyword retrieval from clinical texts: an application of natural language processing to massive data of Chilean suspected diagnosis].

Revista medica de Chile Oct 1, 2019
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...
Time Factors Natural Language Processing Chile Medicine Reproducibility of Results Information Storage and Retrieval Electronic Data Processing Medical Records Data Mining Referral and Consultation Humans Medical Informatics Computing Diagnostic Techniques and Procedures
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Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
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...
Humans Medical Informatics Medical Informatics Computing Diagnosis, Computer-Assisted Neural Networks, Computer Medical Informatics Applications Deep Learning Machine Learning Computational Biology
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HTP-NLP: A New NLP System for High Throughput Phenotyping.

Studies in health technology and informatics Jan 1, 2017
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...
Natural Language Processing Information Storage and Retrieval Medical Informatics Computing Semantics Phenotype Electronic Health Records Humans
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