Multimodal machine learning in precision health: A scoping review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
Early prediction of patient outcomes is important for targeting preventive care. This protocol
describes a practical workflow for developing deep-learning risk models that can predict …
describes a practical workflow for developing deep-learning risk models that can predict …
A clinically applicable approach to continuous prediction of future acute kidney injury
The early prediction of deterioration could have an important role in supporting healthcare
professionals, as an estimated 11% of deaths in hospital follow a failure to promptly …
professionals, as an estimated 11% of deaths in hospital follow a failure to promptly …
[HTML][HTML] Deep patient: an unsupervised representation to predict the future of patients from the electronic health records
Secondary use of electronic health records (EHRs) promises to advance clinical research
and better inform clinical decision making. Challenges in summarizing and representing …
and better inform clinical decision making. Challenges in summarizing and representing …
Deep survival analysis
R Ranganath, A Perotte… - Machine Learning for …, 2016 - proceedings.mlr.press
The electronic health record (EHR) provides an unprecedented opportunity to build
actionable tools to support physicians at the point of care. In this paper, we introduce deep …
actionable tools to support physicians at the point of care. In this paper, we introduce deep …
Population-level prediction of type 2 diabetes from claims data and analysis of risk factors
N Razavian, S Blecker, AM Schmidt, A Smith-McLallen… - Big Data, 2015 - liebertpub.com
We present a new approach to population health, in which data-driven predictive models are
learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from …
learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from …
Clinical big data and deep learning: Applications, challenges, and future outlooks
The explosion of digital healthcare data has led to a surge of data-driven medical research
based on machine learning. In recent years, as a powerful technique for big data, deep …
based on machine learning. In recent years, as a powerful technique for big data, deep …
Use of unstructured text in prognostic clinical prediction models: a systematic review
Objective This systematic review aims to assess how information from unstructured text is
used to develop and validate clinical prognostic prediction models. We summarize the …
used to develop and validate clinical prognostic prediction models. We summarize the …
Recommendations for enhancing the usability and understandability of process mining in healthcare
Healthcare organizations are confronted with challenges including the contention between
tightening budgets and increased care needs. In the light of these challenges, they are …
tightening budgets and increased care needs. In the light of these challenges, they are …
Using clinical notes and natural language processing for automated HIV risk assessment
Objective: Universal HIV screening programs are costly, labor intensive, and often fail to
identify high-risk individuals. Automated risk assessment methods that leverage longitudinal …
identify high-risk individuals. Automated risk assessment methods that leverage longitudinal …