The secondary use of electronic health records for data mining: Data characteristics and challenges
The primary objective of implementing Electronic Health Records (EHRs) is to improve the
management of patients' health-related information. However, these records have also been …
management of patients' health-related information. However, these records have also been …
Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review
Objective To determine the effects of using unstructured clinical text in machine learning
(ML) for prediction, early detection, and identification of sepsis. Materials and methods …
(ML) for prediction, early detection, and identification of sepsis. Materials and methods …
Deid-gpt: Zero-shot medical text de-identification by gpt-4
The digitization of healthcare has facilitated the sharing and re-using of medical data but has
also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability …
also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability …
Anomaly detection framework for wearables data: A perspective review on data concepts, data analysis algorithms and prospects
Wearable devices use sensors to evaluate physiological parameters, such as the heart rate,
pulse rate, number of steps taken, body fat and diet. The continuous monitoring of …
pulse rate, number of steps taken, body fat and diet. The continuous monitoring of …
Machine-learning-based adverse drug event prediction from observational health data: a review
Adverse drug events (ADEs) are responsible for a significant number of hospital admissions
and fatalities. Machine learning models have been developed to assess individual patient …
and fatalities. Machine learning models have been developed to assess individual patient …
[HTML][HTML] Consolidated reporting guidelines for prognostic and diagnostic machine learning modeling studies: development and validation
Background The reporting of machine learning (ML) prognostic and diagnostic modeling
studies is often inadequate, making it difficult to understand and replicate such studies. To …
studies is often inadequate, making it difficult to understand and replicate such studies. To …
Artificial intelligence and machine learning approaches to facilitate therapeutic drug management and model-informed precision dosing
EA Poweleit, AA Vinks, T Mizuno - Therapeutic drug monitoring, 2023 - journals.lww.com
Background: Therapeutic drug monitoring (TDM) and model-informed precision dosing
(MIPD) have greatly benefitted from computational and mathematical advances over the …
(MIPD) have greatly benefitted from computational and mathematical advances over the …
Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions
VS Naresh, M Thamarai - Wiley Interdisciplinary Reviews: Data …, 2023 - Wiley Online Library
Data mining (DM) and machine learning (ML) applications in medical diagnostic systems
are budding. Data privacy is essential in these systems as healthcare data are highly …
are budding. Data privacy is essential in these systems as healthcare data are highly …
Assessment of electronic health record for cancer research and patient care through a scoping review of cancer natural language processing
PURPOSE The advancement of natural language processing (NLP) has promoted the use
of detailed textual data in electronic health records (EHRs) to support cancer research and …
of detailed textual data in electronic health records (EHRs) to support cancer research and …
Artificial intelligence-based medical data mining
A Zia, M Aziz, I Popa, SA Khan, AF Hamedani… - Journal of Personalized …, 2022 - mdpi.com
Understanding published unstructured textual data using traditional text mining approaches
and tools is becoming a challenging issue due to the rapid increase in electronic open …
and tools is becoming a challenging issue due to the rapid increase in electronic open …