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Using Natural Language Processing to Classify Serious Illness Communication with Oncology Patients

Davoudi, A.; Tissot, H.; Doucette, A.; Gabriel, P. E.; Parikh, R. B.; Mowery, D. L.; Miranda, S.

2021-08-22 health informatics
10.1101/2021.08.20.21262082 medRxiv
Show abstract

One core measure of healthcare quality set forth by the Institute of Medicine is whether care decisions match patient goals. High-quality "serious illness communication" about patient goals and prognosis is required to support patient-centered decision-making, however current methods are not sensitive enough to measure the quality of this communication or determine whether care delivered matches patient priorities. Natural language processing (NLP) offers an efficient method for identification and evaluation of documented serious illness communication, which could serve as the basis for future quality metrics in oncology and other forms of serious illness. In this study, we trained NLP algorithms to identify and characterize serious illness communication with oncology patients.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Journal of the American Medical Informatics Association
◐ 71 papers in training set
Top 0.1%
22.1%
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2
JCO Clinical Cancer Informatics
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18.3%
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3
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Top 0.2%
9.5%
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4
JAMIA Open
◐ 42 papers in training set
Top 0.2%
6.6%
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50% of probability mass above
5
Journal of Medical Internet Research
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6.2%
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6
BMC Medical Informatics and Decision Making
○ 43 papers in training set
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5.5%
7
BMJ Health & Care Informatics
● 15 papers in training set
Top 0.2%
5.4%
8
npj Digital Medicine
○ 118 papers in training set
Top 1%
5.4%
9
International Journal of Medical Informatics
○ 26 papers in training set
Top 0.2%
4.3%
10
JMIR Medical Informatics
◐ 18 papers in training set
Top 0.4%
1.7%
11
Artificial Intelligence in Medicine
○ 17 papers in training set
Top 0.4%
1.7%
12
PLOS Digital Health
● 106 papers in training set
Top 3%
1.3%
13
Scientific Reports
○ 3612 papers in training set
Top 60%
1.3%
14
Cancer Medicine
○ 26 papers in training set
Top 0.8%
1.1%
15
eBioMedicine
○ 183 papers in training set
Top 7%
0.8%
16
Frontiers in Digital Health
○ 24 papers in training set
Top 1%
0.6%
17
iScience
○ 1154 papers in training set
Top 42%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.