Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review
Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …
insights from Electronic Health Records (EHRs). However, the lack of annotated data …
Social determinants of health in electronic health records and their impact on analysis and risk prediction: a systematic review
Objective This integrative review identifies and analyzes the extant literature to examine the
integration of social determinants of health (SDoH) domains into electronic health records …
integration of social determinants of health (SDoH) domains into electronic health records …
Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study
JL Marcus, LB Hurley, DS Krakower, S Alexeeff… - The lancet HIV, 2019 - thelancet.com
Background The limitations of existing HIV risk prediction tools are a barrier to
implementation of pre-exposure prophylaxis (PrEP). We developed and validated an HIV …
implementation of pre-exposure prophylaxis (PrEP). We developed and validated an HIV …
Development and validation of an automated HIV prediction algorithm to identify candidates for pre-exposure prophylaxis: a modelling study
DS Krakower, S Gruber, K Hsu, JT Menchaca… - The Lancet …, 2019 - thelancet.com
Background HIV pre-exposure prophylaxis (PrEP) is effective but underused, in part
because clinicians do not have the tools to identify PrEP candidates. We developed and …
because clinicians do not have the tools to identify PrEP candidates. We developed and …
Artificial intelligence for quantitative modeling in drug discovery and development: An innovation and quality consortium perspective on use cases and best practices
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered
in a new era of possibilities across various scientific domains. One area where these …
in a new era of possibilities across various scientific domains. One area where these …
Artificial intelligence and machine learning for HIV prevention: emerging approaches to ending the epidemic
Abstract Purpose of Review We review applications of artificial intelligence (AI), including
machine learning (ML), in the field of HIV prevention. Recent Findings ML approaches have …
machine learning (ML), in the field of HIV prevention. Recent Findings ML approaches have …
Cohort selection for clinical trials: n2c2 2018 shared task track 1
Abstract Objective Track 1 of the 2018 National NLP Clinical Challenges shared tasks
focused on identifying which patients in a corpus of longitudinal medical records meet and …
focused on identifying which patients in a corpus of longitudinal medical records meet and …
[HTML][HTML] Web-based risk prediction tool for an individual's risk of HIV and sexually transmitted infections using machine learning algorithms: development and external …
Background HIV and sexually transmitted infections (STIs) are major global public health
concerns. Over 1 million curable STIs occur every day among people aged 15 years to 49 …
concerns. Over 1 million curable STIs occur every day among people aged 15 years to 49 …
Machine learning to identify persons at high-risk of human immunodeficiency virus acquisition in rural Kenya and Uganda
Background In generalized epidemic settings, strategies are needed to prioritize individuals
at higher risk of human immunodeficiency virus (HIV) acquisition for prevention services. We …
at higher risk of human immunodeficiency virus (HIV) acquisition for prevention services. We …
Artificial intelligence and the future of life sciences
ML Leite, LS de Loiola Costa, VA Cunha, V Kreniski… - Drug discovery today, 2021 - Elsevier
Over the past few decades, the number of health and 'omics-related data'generated and
stored has grown exponentially. Patient information can be collected in real time and …
stored has grown exponentially. Patient information can be collected in real time and …