4,569
Views
1
CrossRef citations to date
0
Altmetric
Physical Medicine & Rehabilitation

Automated recognition of functioning, activity and participation in COVID-19 from electronic patient records by natural language processing: a proof- of- concept

, , , , , , , , & show all
Pages 235-243 | Received 09 Sep 2021, Accepted 29 Dec 2021, Published online: 18 Jan 2022

Figures & data

Table 1. COVID-19 relevant ICF categories and levels.

Figure 1. Overview of the annotation data divided into non-COVID-19 and COVID-19 notes for each annotated ICF category. Each category is differentiated in levels (0–4 and in the case of Walking & Moving and Exercise Tolerance 0–5).

Figure 1. Overview of the annotation data divided into non-COVID-19 and COVID-19 notes for each annotated ICF category. Each category is differentiated in levels (0–4 and in the case of Walking & Moving and Exercise Tolerance 0–5).

Figure 2. Distribution of level annotations for Walking & Moving (FAC score) across COVID-19 and non-COVID-19 data.

Figure 2. Distribution of level annotations for Walking & Moving (FAC score) across COVID-19 and non-COVID-19 data.

Table 2. Performance of the ICF category classification.

Table 3. Best models regression analysis for each relevant scoring level.

Figure 3. Impact of the size of the training data on performance on COVID-19 data. Per category, grouped bars from top to bottom show the magnitude of precision (P), recall (R) and their harmonic mean (F1) as a function of increasing sample size.

Figure 3. Impact of the size of the training data on performance on COVID-19 data. Per category, grouped bars from top to bottom show the magnitude of precision (P), recall (R) and their harmonic mean (F1) as a function of increasing sample size.

Data availability statement

The data that support the findings of this study are available on request from the corresponding author, CGM. The data are not publicly available due to containing information that could compromise the privacy of research participants.