This notebook fosters the reproducibility of the following paper:
If you want to compute the same metrics with you own data, we redirect you towards our generic content analysis GitHub https://github.com/vincentpmartin/generic_content_analysis
Welcome to this online coding environment ! You are currently running a Jupyter notebook that we hope to be usefull for content analysis of questionnaires.
Each cell can be ran individually (pressing the "Play" ▶ button or pressing CTRL+ENTER in the each cell). In order to make everything run smoothly, it is strongly recommended to run them in order.
RECOMMANDED: You can also run all the cells at the same time : "Run" → "Run All Cells".
At the end of the execution, you will have the opportunity to save all yours results in an HTML file, executing "File" → "Save and Export Notebook as" → "HTML"
If you have chosen to run every cell individually, please begin by running the following cell that imports all the required package for our code to work correctly ↓
In a first step, we analyse the frequency of the symptoms.
The figure has been save in the online folder (📁 symbol on the left) under the name figure1_histogram_1.pdf.
You can change the name and the format of the file changing the name in the fig.write_image() function.
⚠️ If you need it, save the figure on your local computer : these online file will be deleted as soon as you quit this page!
The figure has been save in the online folder (📁 symbol on the left) under the name figure2_histogram_2.pdf.
You can change the name and the format of the file changing the name in the fig.write_image() function.
⚠️ If you need it, save the figure on your local computer : these online file will be deleted as soon as you quit this page!
| Specific symptoms | Compound symptoms | Total | |
|---|---|---|---|
| ASA | 9 | 4 | 13 |
| SA-SDQ | 10 | 1 | 11 |
| STOP-Bang | 7 | 2 | 9 |
| Berlin | 8 | 1 | 9 |
| Haraldsson | 3 | 4 | 7 |
| NoSAS | 5 | 0 | 5 |
| STOP | 3 | 2 | 5 |
| GOAL | 4 | 0 | 4 |
| OSA 50 | 4 | 0 | 4 |
| AS | 2 | 2 | 4 |
| Wisconsin Q | 3 | 0 | 3 |
The table has been save in the online folder (📁 symbol on the left) under the name table1_symptoms_per_questionnaire.xlsx.
You can change the name and the format of the file changing the name in the sympt_per_questionnaire.to_excel() function.
⚠️ If you need it, save the excel file on your local computer : these online file will be deleted as soon as you quit this page!
References list is empty
C:\Users\Vincent\AppData\Local\Temp\ipykernel_6396\3312803415.py:5: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)` C:\Users\Vincent\AppData\Local\Temp\ipykernel_6396\3312803415.py:5: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)` C:\Users\Vincent\AppData\Local\Temp\ipykernel_6396\3312803415.py:5: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
| ASA | SA-SDQ | STOP-Bang | Berlin | Haraldsson | NoSAS | STOP | GOAL | OSA 50 | AS | Wisconsin Q | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| OSA symptoms | 4 | 6 | 2 | 4 | 2 | 1 | 2 | 1 | 2 | 3 | 3 |
| Sleep-related symptoms | 4 | 1 | 2 | 3 | 5 | 0 | 2 | 0 | 0 | 0 | 0 |
| Clinical characteristics | 5 | 4 | 5 | 2 | 0 | 4 | 1 | 3 | 2 | 1 | 0 |
The table has been save in the online folder (📁 symbol on the left) under the name table2_categorie_per_questionnaire.xlsx.
You can change the name and the format of the file changing the name in the cat_per_questionnaire.T.to_excel() function.
⚠️ If you need it, save the excel file on your local computer : these online file will be deleted as soon as you quit this page!
(i.e. same thing as before, but normalized by questionnaire (sum across lines equals 1)).
Figure 3 has been save in the online folder (📁 symbol on the left) under the name figure3_heatmap.pdf. You can change the name and the format of the file changing the name in the fig.write_image() function.
⚠️ If you need it, save the figure on your local computer : these online file will be deleted as soon as you quit this page!
The figure has been save in the online folder (📁 symbol on the left) under the name figure4_radial.pdf. You can change the name and the format of the file changing the name in the fig.write_image() function.
⚠️ If you need it, save the figure on your local computer : these online file will be deleted as soon as you quit this page!
In order to estimate the overlap between the symptoms measured by the questionnes, calculate the Jaccard index, which is defined as the number of symmtoms that are measured by both questionnaires, divided by the number of unique symptoms measured both questionnaires.
First, we compute the Jaccard index for each pair of questionnaires and plot it using a heatmap.
| ASA | SA-SDQ | STOP-Bang | Berlin | Haraldsson | NoSAS | STOP | GOAL | OSA 50 | AS | Wisconsin Q | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ASA | 1.000000 | 0.263158 | 0.375000 | 0.375000 | 0.333333 | 0.200000 | 0.285714 | 0.133333 | 0.062500 | 0.307692 | 0.230769 |
| SA-SDQ | 0.263158 | 1.000000 | 0.333333 | 0.250000 | 0.125000 | 0.142857 | 0.230769 | 0.250000 | 0.153846 | 0.153846 | 0.166667 |
| STOP-Bang | 0.375000 | 0.333333 | 1.000000 | 0.384615 | 0.230769 | 0.400000 | 0.555556 | 0.444444 | 0.181818 | 0.181818 | 0.200000 |
| Berlin | 0.375000 | 0.250000 | 0.384615 | 1.000000 | 0.230769 | 0.166667 | 0.400000 | 0.181818 | 0.181818 | 0.300000 | 0.333333 |
| Haraldsson | 0.333333 | 0.125000 | 0.230769 | 0.230769 | 1.000000 | 0.090909 | 0.333333 | 0.000000 | 0.100000 | 0.222222 | 0.250000 |
| NoSAS | 0.200000 | 0.142857 | 0.400000 | 0.166667 | 0.090909 | 1.000000 | 0.000000 | 0.500000 | 0.125000 | 0.125000 | 0.142857 |
| STOP | 0.285714 | 0.230769 | 0.555556 | 0.400000 | 0.333333 | 0.000000 | 1.000000 | 0.125000 | 0.125000 | 0.285714 | 0.333333 |
| GOAL | 0.133333 | 0.250000 | 0.444444 | 0.181818 | 0.000000 | 0.500000 | 0.125000 | 1.000000 | 0.142857 | 0.142857 | 0.166667 |
| OSA 50 | 0.062500 | 0.153846 | 0.181818 | 0.181818 | 0.100000 | 0.125000 | 0.125000 | 0.142857 | 1.000000 | 0.142857 | 0.166667 |
| AS | 0.307692 | 0.153846 | 0.181818 | 0.300000 | 0.222222 | 0.125000 | 0.285714 | 0.142857 | 0.142857 | 1.000000 | 0.750000 |
| Wisconsin Q | 0.230769 | 0.166667 | 0.200000 | 0.333333 | 0.250000 | 0.142857 | 0.333333 | 0.166667 | 0.166667 | 0.750000 | 1.000000 |
Table 3 has been save in the online folder (📁 symbol on the left) under the name table3_jaccard_pairs.xlsx.
You can change the name and the format of the file changing the name in the jaccard_table.to_excel() function.
⚠️ If you need it, save the excel file on your local computer : these online file will be deleted as soon as you quit this page!
The figure has been save in the online folder (📁 symbol on the left) under the name figure5_heatmap_jaccard.pdf.
You can change the name and the format of the file changing the name in the fig.write_image() function.
⚠️ If you want it, save the figure on your local computer : these online file will be deleted as soon as you quit this page!
Then, we compute the average of Jaccard index for each questionnaire with other questionnaires (excluding the references).
| Avg. Jaccard Index | |
|---|---|
| ASA | 0.256650 |
| SA-SDQ | 0.206948 |
| STOP-Bang | 0.328735 |
| Berlin | 0.280402 |
| Haraldsson | 0.191634 |
| NoSAS | 0.189329 |
| STOP | 0.267442 |
| GOAL | 0.208698 |
| OSA 50 | 0.138236 |
| AS | 0.261201 |
| Wisconsin Q | 0.274029 |
Average Jaccard index (wo references): 0.23666 (sd: 0.0542)
Table 4 has been save in the online folder (📁 symbol on the left) under the name table4_jaccard_average_questionnaires.xlsx.
You can change the name and the format of the file changing the name in the jaccard.to_excel() function.
⚠️ If you need it, save the excel file on your local computer : these online file will be deleted as soon as you quit this page!
| Avg. Jaccard Index | Specific symptoms | Compound symptoms | Total | |
|---|---|---|---|---|
| ASA | 0.256650 | 9 | 4 | 13 |
| SA-SDQ | 0.206948 | 10 | 1 | 11 |
| STOP-Bang | 0.328735 | 7 | 2 | 9 |
| Berlin | 0.280402 | 8 | 1 | 9 |
| Haraldsson | 0.191634 | 3 | 4 | 7 |
| NoSAS | 0.189329 | 5 | 0 | 5 |
| STOP | 0.267442 | 3 | 2 | 5 |
| GOAL | 0.208698 | 4 | 0 | 4 |
| OSA 50 | 0.138236 | 4 | 0 | 4 |
| AS | 0.261201 | 2 | 2 | 4 |
| Wisconsin Q | 0.274029 | 3 | 0 | 3 |
Correlation between Jaccard Index and number of specific symptoms: SignificanceResult(statistic=0.0, pvalue=1.0) Correlation between Jaccard Index and number of compound symptoms: SignificanceResult(statistic=0.24545784641604232, pvalue=0.46691618386154077) Correlation between Jaccard Index and total number of symptoms: SignificanceResult(statistic=0.11521838449545894, pvalue=0.7358609458354772)
Computing the same metric (average of average) for each category of questionnaires.
| Avg. Jaccard Index | |
|---|---|
| OSA symptoms | 0.393983 |
| Sleep-related symptoms | 0.319524 |
| Clinical characteristics | 0.213062 |
Table 5 has been save in the online folder (📁 symbol on the left) under the name table5_jaccard_categories.xlsx.
You can change the name and the format of the file changing the name in the res.to_excel() function.
⚠️ If you need it, save the excel file on your local computer : these online file will be deleted as soon as you quit this page!
Computing the same metric (average of average) for each subcategory.
C:\Users\Vincent\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\numpy\core\fromnumeric.py:3464: RuntimeWarning: Mean of empty slice. C:\Users\Vincent\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\numpy\core\_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide C:\Users\Vincent\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\numpy\core\fromnumeric.py:3464: RuntimeWarning: Mean of empty slice. C:\Users\Vincent\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\numpy\core\_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide
| Avg. Jaccard Index | |
|---|---|
| Snoring | 0.555556 |
| Breath abnormalities-related complaints | 0.250000 |
| Breath abnormalities observations | 1.000000 |
| Context | NaN |
| Sleepiness | 0.625000 |
| Awakening | 1.000000 |
| Insomnia | NaN |
| Sociodemographic | 0.700000 |
| Anthropometric | 0.500000 |
| Comorbidity | 0.750000 |
| Anatomical airway | 0.333333 |
The figure has been save in the online folder (📁 symbol on the left) under the name figure6_sunburst_plot.pdf.
You can change the name and the format of the file changing the name in the fig.write_image() function.
⚠️ If you want it, save the figure on your local computer : these online file will be deleted as soon as you quit this page!
You have reached the end of this notebook. If you want to save the whole page, you can download it to html with dynamic figures:
- "File" → "Save and Export Notebook as" → "HTML"