1. Scalable and accurate deep learning with electronic health
NEMO English Seminar
Scalable and accurate deep learning with electronic health
Alvin Rajkomar et al.
Google Inc., Mountain View, CA, USA; University of California, San Francisco, San Francisco, CA,USA
npj Digital Medicine, 1(1):1-18, 2018.
Speaker JUNGJU PARK
Date 4th July 2018

2. Introduction Key words Limited traditional predictive modeling
Disease prediction
Electronic health record (EHR)
Deep learning
Limited traditional predictive modeling
Required custom data set with specific variables
Thousands of potential predictors
Variable selections may produce imprecise prediction
Contribution
Reporting a generic data processing pipeline
Demonstration in a wide variety of predictive problems

3. Unstructured data (raw)

4. Structured data (input)
Fast Healthcare Interoperability Resources (FHIR)

5. Descriptive statistics

6. Results: 216,221 hospitalizations
Deep learning
Logistic regression

7. Improving Palliative Care with Deep Learning
NEMO English Seminar
Improving Palliative Care with Deep Learning
Anand Avati et al.
Department of Computer Science, Stanford University
Bioinformatics and Biomedicine (BIBM), 2017 IEEE International Conference
pp IEEE
Speaker JUNGJU PARK
Date 4th July 2018

8. Introduction Key words Difficulties in real world Goal Palliative care
Deep learning
Difficulties in real world
Limited resource
Physicians may not refer patients
Shortage of palliative care professionals
Goal
Identification of the patients who needs palliative cares
To predict the mortality of a given patient within 12 months

9. Literature review Prognostic tools in Palliative Care
Palliative Prognostic Score (multiple regression analysis)
Prognostic tools in the Intensive Care Unit
APACHE-II, -III, SAPS II, etc.
Prognostic tools for Early Identification
CriSTAL, CARING, etc.,
Prognostic in the age of Big-Data
Machine Learning-based approaches
Small data set, few variables, naïve model,

10. Procedures Dataset construction Feature extraction Training Evaluation
Deep Neural Network
13,654 dimensions
18 hidden layers
Evaluation
With imbalanced data
Average Precession (AP) score

11. Results Brier score of 0.042 AP score of 0.69
Recall of 0.34 at 0.9 precision
AUROC of 0.93