1. Haokai Sheng, Yun Xiong, David W. Bates, Li Zhou
A Temporal Visualization of Chronic Obstructive Pulmonary Disease Progression Using Deep Learning and Free-Text Clinical Reports
Meihan Wan, Chunlei Tang, Joseph M. Plasek, Haohan Zhang, Min Jeoung Kang,
Haokai Sheng, Yun Xiong, David W. Bates, Li Zhou

2. Learning Objectives
After participating in this oral presentation, the learner should be better able to:
• Handle the time dimension in the EHRs;
• Use the irregular time-lapse segments to demonstrate disease progression
DGIM Research Day 2019

3. Chronic Obstructive Pulmonary Disease
COPD is the third leading chronic disease in the United States, which can take well over ten years to evolve from mild to very severe. The global initiative for chronic obstructive lung disease provides the GOLD standard guidelines which physicians use in managing COPD.
DGIM Research Day 2019

4. Free-Text Clinical Document & Its Time
Irregular Visits: The temporal granularity of a patient’s record may vary significantly over different time periods.
Incomplete Records: Clinical data may not be available for the entire progression of COPD.
Disease Progression Heterogeneity: There is no natural alignment between different patients as progression rates vary.
Discrete Observations: Although the disease progression is a continuous-time process, the patient is only observed at discrete time points with varied intervals
• 1 day for an office visit
• a few days for a hospitalization
DGIM Research Day 2019

5. Yet, constant time segments cannot show the temporal
autocorrelation from the dynamics arising in the data.
DGIM Research Day 2019

6. A Four-Layer Deep Learning Model
We used a flatten layer to facilitate the unfolding process followed by a dense layer to combine the time segments into a fully-connected network.
DGIM Research Day 2019

7. Capturing of Time Lapse Segments
We used a sigmoid activation function to output a {0,1}-sequence, in which we set two or more consecutive zeros or ones as a time segment.
DGIM Research Day 2019

8. Dataset {P, R, C, M}
Dataset P a PHYSICIAN INTERPRETATION section of 78,489 pulmonary notes for 2,431unique patients
Dataset R two main sections: FINDINGS and IMPRESSION of 1,893,498 chest X- ray radiology reports for 13,414 unique patients
Dataset C an ABNORMAL ECG section of 1,029,363 cardiology reports for 13,918 patients
Dataset M merged Datasets P, R, and C using a heuristic merger that inserts a note into the appropriate chronological place in a corpus that was initialized to represent the most prevalent domain.
DGIM Research Day 2019

9. Results on Dataset M
High Prediction Accuracy Our proposed model achieved a prediction accuracy of 80% on average on our corpus.
DGIM Research Day 2019

10. Results on Dataset P
Regular time segment (pre-set time window + delta window)
LSTM irregular time segment
Days before death
COPD Stage documented by the experts 1
[0,70] IV
[0,65] 2
[71,140]
III
[55,150] 3
[141,210]
II-2
[145,270] 4
[211,280]
II-1 5
[281,350]
[262,360] 6
[351,420]
[337,484] 7
[421,490]
[450,552] 8
[491,560] 9
[560,630] I
[449,630]
DGIM Research Day 2019

11. DGIM Research Day 2019