Opportunities and obstacles for deep learning in biology and medicine
T Ching, DS Himmelstein… - Journal of the …, 2018 - royalsocietypublishing.org
Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …
combining raw inputs into layers of intermediate features. These algorithms have recently …
[HTML][HTML] Natural language processing of clinical notes on chronic diseases: systematic review
Background: Novel approaches that complement and go beyond evidence-based medicine
are required in the domain of chronic diseases, given the growing incidence of such …
are required in the domain of chronic diseases, given the growing incidence of such …
A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop
Imaging research laboratories are rapidly creating machine learning systems that achieve
expert human performance using open-source methods and tools. These artificial …
expert human performance using open-source methods and tools. These artificial …
Biological phenoty** in sepsis and acute respiratory distress syndrome
P Sinha, NJ Meyer, CS Calfee - Annual review of medicine, 2023 - annualreviews.org
Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly
being recognized as one of the principal barriers to finding efficacious targeted therapies …
being recognized as one of the principal barriers to finding efficacious targeted therapies …
Big data analytics to improve cardiovascular care: promise and challenges
The potential for big data analytics to improve cardiovascular quality of care and patient
outcomes is tremendous. However, the application of big data in health care is at a nascent …
outcomes is tremendous. However, the application of big data in health care is at a nascent …
A machine learning-based framework to identify type 2 diabetes through electronic health records
Objective To discover diverse genotype-phenotype associations affiliated with Type 2
Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide …
Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide …
Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research
B Birnbaum, N Nussbaum, K Seidl-Rathkopf… - arxiv preprint arxiv …, 2020 - arxiv.org
Objective Electronic health records (EHRs) are a promising source of data for health
outcomes research in oncology. A challenge in using EHR data is that selecting cohorts of …
outcomes research in oncology. A challenge in using EHR data is that selecting cohorts of …
The GA4GH Phenopacket schema defines a computable representation of clinical data
TG is a shareholder of Westlake Omics Inc. TI is a cofounder of Data4Cure, is on the
Scientific Advisory Board and has an equity interest. TI is on the Scientific Advisory Board of …
Scientific Advisory Board and has an equity interest. TI is on the Scientific Advisory Board of …
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability
JC Kirby, P Speltz, LV Rasmussen… - Journal of the …, 2016 - academic.oup.com
Objective Health care generated data have become an important source for clinical and
genomic research. Often, investigators create and iteratively refine phenotype algorithms to …
genomic research. Often, investigators create and iteratively refine phenotype algorithms to …
Mining electronic health records (EHRs) A survey
The continuously increasing cost of the US healthcare system has received significant
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …