Comments (2)
Summary
This a pretty straightforward ML paper. With the aim of predicting chronological age from blood chemistry, they built a bunch of DNNs, optimized them, and reported the results, as well as combining the best performing ones into an ensemble. They also identified important features.
This can clearly be grouped with other EHR papers, probably as a cited example for deep learning's use in a hospital context. I can't imagine anything beyond that, though.
Problem
Research has shown that biomarkers of age-associated pathology may reflect senescence modifications and hence may act as aging clocks, but most of these biomarkers are not important in inferring health status and are therefore not frequently collected.
Here, they tried to use readily collected data (blood chemistry) to predict patient chronological age.
Methods
Data
62,414 patient records containing age, sex, and 41 common blood markers such as glucose, cholesterol, etc. They omitted WBC data because of how variable it is across the general population. All biomarkers were normalized to 0-1.
Design
They treated this as a regression problem -- not a classification problem -- primarily so they could associate patient age with biomarkers that are correlated.
4 metrics were evaluated using 90/10 training/testing split: Pearson's correlation, R-squared, MAE, and prediction accuracy coded 0 or 1 where 1 is given if the predicted value falls withing a predefined neighborhood of the true value (+/- 10 years).
They ranked important features in a fashion similar to RFs: they randomly shuffled features and calculated the drop in performance, in terms of R-squared.
40 DNNs were fit with various hyperparameters, and they selected 21 DNNs that performed the best to be combined into an ensemble via stacked generalization (elastic net turned out to be the stacking best model). They selected these 21 DNNs by iteratively adding each DNN prediction vector into the ensemble in an order based on either (1) decreasing R-squared or (2) decreasing correlation. Both methods identified a 21 net ensemble.
They used feedfoward NNs with at least 4 layers. The best performing DNN in their ensemble had 5 hidden layers with 2000, 1500, 1000, 500, and 1 node each, used PReLU, AdaGrad, L2 regularization, and dropout (0.2)
Results
That best performing DNN had an R-squared of 0.8 and 82% prediction accuracy and outperformed KNNs, SVMs, RFs, among others.
The ensemble had a 0.82 R-squared and a 83.5% prediction accuracy.
They compared their performance to studies that predicted age with transcriptomic biomarkers (R-squared = 0.6) and epigenetic biomarkers (R-squared = 0.93 and R-squared = 0.89).
The feature importance procedure ranked albumin, glucose, ALP, urea and RBCs at the top.
Notes
The DNN is available here: www.aging.ai
from deep-review.
DOI link: http://doi.org/10.18632/aging.100968
from deep-review.
Related Issues (20)
- Potential Reorganization HOT 7
- Deep Learning for the Life Sciences
- Deepprune: Learning Efficient and Interpretable Convolutional Networks Through Weight Pruning for Predicting DNA-Protein Binding
- Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets
- In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening
- Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening
- Update subsection: Structure-based prediction of bioactivity HOT 2
- Update 'Imaging applications in healthcare'
- Applications in sleep research HOT 2
- LISA: Towards Learned DNA Sequence Search HOT 1
- DeepArk: modeling cis-regulatory codes of model species with deep learning
- Human Protein Atlas (HPA) Cell Type Prediction using Deep Learning and estimating uncertainty HOT 2
- How can one cite this dynamic review? HOT 3
- Update for 2020? HOT 3
- Add a Systematic Text-Mining Component to the Review? HOT 3
- Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs HOT 3
- Alpha Fold 2 HOT 1
- feature and labels HOT 1
- Update in Proteomics mass spectrometry HOT 2
- Consider change to reference 346 HOT 9
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from deep-review.