Author Contributions
Conceptualization, T.O. and I.P.S.; methodology, T.O. and T.V.N.; formal analysis, T.O., I.P.S., S.A.S. and T.V.N.; resources, T.O., I.P.S. and T.V.N.; writing—original draft preparation, T.O., I.P.S. and T.V.N.; writing—review, T.O. and T.V.N. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
Conflicts of Interest
The authors declare no conflicts of interest.
References
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Figure 1.
Classes for the multi-output model. This figure illustrates how medication recommendations may evolve for a patient with multiple concurrent conditions (DCCs) based on their changing concerns. A patient whose primary concern is weight management is prescribed to take Victoza and Hydroxychloroquine. However, if they add cost to list of their concerns, their prescribed medication combination shifts to Empagliflozin and Aspirin.
Figure 2.
Correlation matrix showing how features related to one another. The intensity of the color represents the strength of the correlation between the variables, with darker colors indicating a stronger correlation and lighter colors indicating a weaker correlation. See figure heatmap, Medicine 2 has a high correlation with Arthritis, and Interaction has a high correlation with Weight.
Figure 3.
Random forest decision tree. The first decision point “Cost <= 0.5” exhibits a Gini impurity of 0.749 and consists of 200 samples with values [72.265, 82.875, 76.455, 72.8]. The second layer, the left subtree, has a threshold of “0.72”, 57 samples, and values [36.706, 17.672, 20.851, 36.4], while the right subtree is determined with “Interaction <= 0.5,” and has a Gini impurity of 0.733 and encompasses 143 samples with values [35.559, 65.203, 55.604, 36.4]. Further, the third layer shows the right subtree, evaluated by “Weight <= 0.5,” with a Gini impurity of 0.733 and 99 samples returning values [28.676, 43.266, 36.297, 20.8].
Figure 4.
Algorithm performances and their accuracy.
Table 1.
The Summary of study participants.
Category | Attribute | Number of Participant |
---|
Experience | Knowledgeable | 162 |
| Very knowledgeable | 64 |
Diseases Treated | Type 2 diabetes (T2D) | 226 |
| T2D & one other disease | 62 |
| T2D & two other diseases | 109 |
| T2D & more than two diseases | 55 |
Primary Specialization | Family medicine | 112 |
| Endocrinologist | 14 |
| General internal medicine | 63 |
| General pediatrics | 32 |
| Gynecology | 8 |
| Nephrologist | 12 |
| Other | 23 |
Table 2.
Data with single outputs.
Diabetes | Arthritis | Depression | Cost | Weight | Interaction | Medicine |
---|
1 | 0 | 0 | 1 | 1 | 0 | Metformin |
1 | 0 | 0 | 1 | 1 | 0 | Victoza |
1 | 0 | 0 | 1 | 1 | 0 | Metformin |
1 | 0 | 0 | 1 | 1 | 0 | Victoza |
1 | 0 | 0 | 1 | 1 | 0 | Victoza |
1 | 0 | 0 | 1 | 1 | 0 | Metformin |
1 | 0 | 0 | 1 | 0 | 1 | Victoza |
1 | 0 | 0 | 1 | 0 | 1 | Metformin |
1 | 0 | 0 | 1 | 0 | 1 | Victoza |
1 | 0 | 0 | 1 | 0 | 1 | Metformin |
1 | 0 | 0 | 1 | 0 | 1 | Metformin |
1 | 0 | 0 | 1 | 0 | 1 | Metformin |
Table 3.
The data population from survey response.
Diabetes | Arthritis | Depression | Cost | Weight | Interaction | Medicine |
---|
1 | 0 | 0 | 1 | 1 | 0 | Metformin |
1 | 0 | 0 | 1 | 1 | 0 | Victoza |
1 | 0 | 0 | 1 | 1 | 0 | Metformin |
1 | 0 | 0 | 1 | 1 | 0 | Victoza |
1 | 0 | 0 | 1 | 1 | 0 | Victoza |
1 | 0 | 0 | 1 | 1 | 0 | Metformin |
- | - | - | - | - | - | - |
- | - | - | - | - | - | - |
1 | 1 | 1 | 1 | 1 | 1 | V + H + Es |
1 | 1 | 1 | 1 | 1 | 1 | M + H + Es |
1 | 1 | 1 | 1 | 1 | 1 | Em + I + S |
1 | 1 | 1 | 1 | 1 | 1 | Em + H + Es |
1 | 1 | 1 | 1 | 1 | 1 | M + I + Es |
Table 4.
The number and dimensions of targets according to classification type.
Classification | Number of Targets | Targets Cardinality |
---|
Multiclass | 1 | >2 |
Multi-label | >1 | 2 (0 or 1) |
Multiclass and multi-output | >1 | >2 |
Table 5.
Data with multiple outputs.
Diabetes | Arthritis | Depression | Cost | Weight | Interaction | Med1 | Med2 | Med3 |
---|
1 | 0 | 0 | 1 | 1 | 0 | Metformin | None | None |
1 | 0 | 0 | 1 | 1 | 0 | Victoza | None | None |
1 | 0 | 0 | 1 | 1 | 0 | Metformin | None | None |
1 | 0 | 0 | 1 | 1 | 0 | Victoza | None | None |
1 | 0 | 0 | 1 | 1 | 0 | Victoza | None | None |
1 | 0 | 0 | 1 | 1 | 0 | Metformin | None | None |
1 | 1 | 1 | 1 | 1 | 1 | V | H | Es |
1 | 1 | 1 | 1 | 1 | 1 | M | H | Es |
1 | 1 | 1 | 1 | 1 | 1 | Em | I | S |
1 | 1 | 1 | 1 | 1 | 1 | Em | H | Es |
1 | 1 | 1 | 1 | 1 | 1 | M | I | Es |
Table 6.
Priority of medications/treatments for each concern.
Algorithm/Model | Iteration 1 | Iteration 2 | Iteration 3 |
---|
Random Forest | 65.5% | 83.5% | 93.3% |
KNN | 60.2% | 67.4% | 78.5% |
AdaBoost | 35.8% | 36.4% | 67.3% |
XGBoost | 53.8% | 62.4% | 76.4% |
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