AIMC Topic: Electronic Health Records
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An Accurate and Automated Method for Adenoma Detection Rate and Report Card Generation Utilizing Common Electronic Health Records.
Journal of clinical gastroenterology
Aug 1, 2024
GOALS: To develop an automated method for Adenoma Detection Rate (ADR) calculation and report card generation using common electronic health records (EHRs).
Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.
Translational psychiatry
Jul 31, 2024
Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transportability, namely the performance of a model trained on one dataset and applied to different data, is ...
Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.
Computer methods and programs in biomedicine
Jul 29, 2024
BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combinin...
Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease.
BMJ health & care informatics
Jul 29, 2024
BACKGROUND: Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hos...
Evaluating the effectiveness of a sliding window technique in machine learning models for mortality prediction in ICU cardiac arrest patients.
International journal of medical informatics
Jul 27, 2024
Extensive research has been devoted to predicting ICU mortality, to assist clinical teams managing critical patients. Electronic health records (EHR) contain both static and dynamic medical data, with the latter accumulating during ICU stays. Existin...
Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.
Psychiatry research
Jul 27, 2024
Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic Topic Modeling, a natural language processing method that evaluates topic changes over time, to anal...
Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques.
Computers in biology and medicine
Jul 25, 2024
Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increased. Artificial Intelligence has proved to be instrumental in development of various such techniques...
Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician.
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Jul 25, 2024
Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language, enabling computers to understand, generate, and derive meaning from human language. NLP's potential appli...
Machine learning computational model to predict lung cancer using electronic medical records.
Cancer epidemiology
Jul 24, 2024
BACKGROUND: Lung cancer (LC) screening using low-dose computed tomography (CT) is recommended according to standard risk criteria or personalized risk calculators. Machine learning (ML) models that can predict disease risk are an emerging method in m...
Deep representation learning from electronic medical records identifies distinct symptom based subtypes and progression patterns for COVID-19 prognosis.
International journal of medical informatics
Jul 20, 2024
OBJECTIVE: Symptoms are significant kind of phenotypes for managing and controlling of the burst of acute infectious diseases, such as COVID-19. Although patterns of symptom clusters and time series have been considered the high potential prediction ...
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