Fig 1.
A. An example Bayesian network with parent and child nodes connected by arcs. B. The general structure of our network components. Training data (*may be updated over time) and patient features allow for improving inference (i.e. probabilities) and structure over time making these networks dynamic. (EHR = Electronic Health Record).
Table 1.
Demographic features of the cohort.
Table 2.
Top 20 network features ranked by weight.
Fig 2.
Network construction, training and validation scheme. Here, 100 patients (50 PI and 50 Control) were used as training data and 150 (75 each cohort) were used as validation data.
Fig 3.
Validity testing of our BN for individual patients from the PI and Control cohorts. A. Mean risk scores between the two populations were significantly different (53% vs. 7%; p <0.000001). B. Network performance as calculated by AUROC (Area under Receiver Operator Characteristic Curve) where an AUROC of 1.0 represents the ability of a model to discriminate between classes 100% of the time.
Table 3.
Model performance comparisons.
Fig 4.
Cohort features & network outcomes.
A. Validation cohort disorder spectrum. The IUIS groupings are clustered according to color (i.e. Blue = T/CID; Red = PAD; Yellow = PIRD; Purple = PD; Green = ID; Orange = AID and Pink = CD. B. BN performance for classifying each IUIS category and overall outcome. The legend displays category number and accuracy for our BN prediction. NOTE- 3 patients were not included here since insufficient input data were available and a class outcome could not be defined by the model. (Abbreviations: CHARGE-coloboma, heart disease, atresia of choanae, restricted growth, genital and ear abnormalities; WAS-Wiskott-Aldrich Syndrome; CVID-Common Variable Immunodeficiency; APDS-Activated Pi3K Delta Syndrome; XLA-X-linked Agammaglobulinemia; CGD-Chronic Granulomatous Disease; STAT1 GOF- Signal Transducer Activator of Transcription 1 Gain of Function; POMP-Proteasome Maturation Protein; NOMID-Neonatal Onset Multisystem Inflammatory Disease).
Fig 5.
The proposed workflow for our model. Here, an end-user or EHR data feed can provide inputs via clinical impressions or diagnostic codes. The BN calculates a risk score which can subsequently be acted upon. It is important to note that it is the risk score and clinical impression should be taken together, which guide subsequent evaluation and management.