Table 1  A description of the proposed new methodology of influence analysis concerning machine learning that can be applied to measure the patient’s “need for help” ratings of expression statements in respect to groupings based on the answer values of background questions, and further to evaluate the applicability of training and validation of a machine learning model to learn the groupings concerning the ratings

Step 1. Gathering questionnaire answers from persons representing various health and demographic backgrounds.

- Each person gives the “need for help” ratings for a set of common expression statements that describe imagined scenarios.

- The rating answers given by the person form his/her “need for help” rating profile.

(Described in the chapter “Gathering ratings about expression statements from persons representing various background features”)

Step 2. Identifying statistically significant and non-significant rating differences for expression statements in respect to groupings based on the answer values of background questions (for example groupings relying on the person’s answer about his/her estimated health condition).

(Described in the chapter “Identifying statistically significant rating differences for expression statements in respect to background questions”)

Step 3. Training and validation of a machine learning model (with a supervised learning approach) to learn the groupings concerning the “need for help” ratings. This step uses the same groupings of respondents that have been used in the step 2.

(Described in the chapter “Training and validation of a machine learning model to learn groupings concerning the ratings”)

Step 4. Comparing the validation accuracies of the machine learning model with the probabilities of pure chance of classifying the rating profiles correctly (averaged from at least 100 separate training and validation sequences).

(Described in the chapter “Comparing the validation accuracies of the machine learning model with the probabilities of pure chance”)

Step 5. Contrasting the validation accuracies of the machine learning model with the occurrence of statistically significant and non-significant rating differences for expression statements in respect to groupings based on the answer values of background questions (averaged from at least 100 separate training and validation sequences).

(Described in the chapter “Contrasting the validation accuracies of the machine learning model with the statistically significant rating differences in respect to groupings”)

Step 6. Drawing conclusions about the applicability of the current machine learning model in this knowledge context. Based on the conclusions further fitting of the model and iteratively repeating the steps 2-6.

(Described in the chapter “Drawing conclusions about the applicability of the current machine learning model”)