VARIABLES NECESARIAS PARA LA COMPARACIÓN DE RESULTADOS

As noted in section 4.2.2, there are several aspects of the explanation design that require further investigation in discussions with target users. To facilitate these discussions, I proposed preliminary explanation designs for the pediatric ICU in-hospital mortality risk model. As per the insights from section 4.2, I focused on suggesting explanation designs for model-agnostic, instance-level explanations based on feature influence methods to better understand the potential utility of these types of explanations within the healthcare domain.

Two popular and publicly-available model-agnostic, instance-level explanation algorithms have been previously applied to predictive modeling problems in the healthcare domain—the local interpretable model-agnostic explanations (LIME) algorithm64,123 and the Shapley additive explanations (SHAP) algorithm.124,125 _{The LIME algorithm generates an explanation for a} prediction by learning an interpretable model (e.g., sparse linear regression) that fits the local decision boundary near the instance of interest. The SHAP algorithm is based on concepts from game theory and is theoretically guaranteed to be faithful to the underlying predictive model. It unifies several alternative instance-level explanation algorithms into a single approach, including the LIME algorithm. A detailed description of both algorithms is available in Appendix A. To generate model-agnostic, instance-level explanations of feature influence for the pediatric ICU in- hospital mortality risk model, the SHAP algorithm was used after a series of experiments comparing the two algorithms revealed that the LIME algorithm did not guarantee local fidelity and required more computation time. The experiments are described in Appendix A.

Based on insights from section 4.2, I mocked-up five explanation designs for the SHAP explanations to solicit critical care provider feedback on the following explanation design options: 1.) Unit of explanation—individual features (low granularity) vs. feature groupings by lab

test/vital sign (high granularity)

2.) Organization of explanation units—no groupings, grouping by influence on risk (i.e. whether the unit increases/decreases risk), grouping by assessment (e.g., laboratory test features, physical assessment test features, demographic/healthcare utilization features) which was used as an approximation of the controllability of features

3.) Dimensionality—static vs. modifiable explanation (i.e., interactive options to control explanation size and granularity of explanation unit)

4.) Risk representation—probability vs. odds

5.) Explanation display format—tornado plot vs. force plot

Each mock-up included the predicted risk of mortality from the pediatric ICU in-hospital mortality risk model, an explanation for the predicted risk from the SHAP algorithm, and supporting information to assist in interpreting the risk and explanation. Mock-ups varied in explanation design options and were organized into two sets based on the different design options. The mock- up sets are summarized in Table 6 and the explanations for each mock-up are shown in Figures 10-14. By default, mock-ups with feature groups for the unit of explanation included groupings by influence within the explanation plot (i.e., each feature group had factors that increased or decreased the risk). Mock-ups with feature groups and tornado plots also included an interactive hover-box option to view the individual level features within each group (i.e., modifiable granularity of explanation unit). For mock-ups with modifiable explanation size, an interactive option to scroll down the explanation plot to view additional features was included.

Table 6. Explanation design options used for each mock-up

Set 1 Set 2

1-1 1-2 1-3 2-1 2-2

Unit of explanation Individual features X X

Feature groups X X X Organization of explanation units None X Influence groups X X X X Assessment groups X Dimensionality Size Static X Modifiable X X X X Granularity of explanation unit Static X X X Modifiable X X

Risk representation Probability X X X X

Odds X

Explanation display format

Force plot X

Supporting information for each mock-up included demographic information (e.g., age, length of stay), a list of current diagnoses, a table of the raw values of the features used in the model (i.e., undiscretized feature values), and an interactive plot where the raw values of time series data from laboratory tests and vital signs could be viewed. An example of the supporting information included in each mock-up is shown in Figure 15. SHAP explanations were generated using the Python package shap version 0.27.0126 _{and mock-ups were generated as interactive} HTML pages using the Python package bokeh version 1.0.4.127

Figure 10. Mock-up 1-1 prediction and explanation. This mock-up depicts the following design options:1) unit of

explanation—indiviudal features, 2) organization of explanation units—no grouping, 3) dimensionality—modifiable explanation size and static granularity of explanation unit, 4) risk representation—odds, and 5) explanation display format—tornado plot.

Figure 11. Mock-up 1-2 prediction and explanation. This mock-up depicts the following design options:1) unit of explanation—feature groups, 2) organization of explanation units—influence groups, 3) dimensionality—modifiable explanation size and modifiable granularity of explanation unit, 4) risk representation—probability, and 5) explanation display format—tornado plot.

Figure 12. Mock-up 1-3 prediction and explanation. This mock-up depicts the following design options:1) unit of

explanation—feature groups, 2) organization of explanation units—influence groups, 3) dimensionality—static explanation size and static granularity of explanation unit, 4) risk representation—probability, and 5) explanation display format—force plot.

Figure 13. Mock-up 2-1 prediction and explanation. This mock-up depicts the following design options:1) unit of explanation—individual features, 2) organization of explanation units—influence groups, 3) dimensionality— modifiable explanation size and static granularity of explanation unit, 4) risk repsentation—probability, and 5) explanation display format—tornado plot.

Figure 14. Mock-up 2-2 prediction and explanation. This mock-up depicts the following design options:1) unit of

explanation—feature groups, 2) organization of explanation units—influence groups and assessment groups, 3) dimensionality—modifiable explanation size and modifiable granularity of explanation unit, 4) risk repsentation— probability, and 5) explanation display format—tornado plot.

Figure 15. Supporting information provided in each mock-up. Each mock-up included included demographic information (bottom left), a list of current diagnoses (bottom right), a table of the raw values of the features used in the model (middle) and an interactive plot where the raw values of time series data from laboratory tests and vital signs could be viewed (top).