cs.AISep 28, 2026
SaveTraining-Free Clinical Reasoning through Medical Ontologies and Cognitive Mapping: A Symbolic-Probabilistic Knowledge Graph Framework
Abstract
Most clinical prediction systems learn patient-variable-outcome associations; we investigate a training-free diagnostic paradigm mapping patient observations to explicit medical knowledge. CKG Reasoner integrates candidate-specific Evidence Feature Nodes, patient-reference matching, a bounded Information Gate, knowledge-weighted evidence accumulation, disease similarity, and decisive clinical rules. Missing-aware normalization and coverage auditing distinguish absent from unavailable evidence. Candidate ranking is separate from outcome-label-independent K-means clustering, which uses four derived evidence coordinates (evidence strength, relative magnitude, directional similarity, and evidence completeness), not raw predictors or targets, to derive cohort-level assignments. Across six retrospective cohorts - four dengue (N = 1000, 1523, 989, 1018), malaria (N = 2190), and influenza (N = 4569) - a uniform, label-free, cohort-fitted K = 2 protocol yielded positive-class F1 scores of 0.996, 0.634, 0.936, 0.917, 0.695, and 0.842, and all-record accuracies of 0.996, 0.558, 0.914, 0.893, 0.707, and 0.906, respectively, with full partition-decision coverage using the frozen package and disease-specific knowledge representations. Neither scoring nor clustering uses outcome labels. Logistic regression provides a supervised baseline. Influenza incorporates confirmatory molecular PCR and is not independent pre-test prediction. Results characterize knowledge-grounded evidence separation, auditability, and sensitivity, not prospective clinical validity or comparative superiority. FOL/LLM-based clinical explanation remains unevaluated.
Figures & tables
| Study | Primary representation | Prob. | Rules | Neg. ev. | Patient-specific | Learned | Intrinsic trace | Main distinction from this work |
|---|---|---|---|---|---|---|---|---|
| [ 1 ] | Hybrid rule/probabilistic experts | Yes | Yes | – | Yes | Yes | Partial | Combines expert outputs through a neural network; no explicit clinical KG evidence semantics. |
| [ 6 ] | Ontology + fuzzy rule system | Fuzzy | Yes | Partial | Yes | Partial | Yes | Semantic similarity and fuzzy inference, but no separate support/contradiction channels or decisive rule override layer. |
| [ 10 ] | Siamese Bayesian networks | Yes | – | Yes | Yes | Yes | Yes | Explicitly addresses negative evidence, but within a learned BN formulation rather than a disease-specific EFN graph. |
| [ 12 ] | KG + deep reinforcement learning | – | – | – | Yes | Yes | Partial | Learns interactive diagnostic policy over the KG. |
| [ 16 ] | KG reasoning paths + dynamic weights | Yes | – | Partial | Yes | Partial | Yes | Weighted paths for TCM syndrome reasoning; no hard pathognomonic/exclusionary override semantics. |
| [ 27 ] | Semantic KG with LR-weighted/conditional edges | Yes | Conditional | Yes | Yes | No/limited | Yes | Very close weighted transparent reasoning; evidence remains primarily edge-weight based rather than explicitly separated clinical attributes. |
Table 1 : Representative diagnostic reasoning systems and their relationship to the proposed framework. “Learned” indicates that a substantive diagnostic component is estimated or fine-tuned from data.
| Dataset | Target | Package schema | N / outcome | Evidence profile |
|---|---|---|---|---|
| A | Dengue | C1_Bangladesh_Dengue_1000 | 1000; 533 positive / 467 negative | Mixed serological, clinical, and routine laboratory evidence |
| B | Dengue | C2_D4_Dengue_Hematology_1523 | 1523; 1042 positive / 481 negative | Hematology-dominant evidence |
| C | Malaria | C3_M1_Malaria_2190 | 2190; 1068 positive / 1122 negative | Malaria-specific clinical/laboratory evidence |
| D | Influenza | C4_I1_Thailand_Influenza_4569 | 4569; 1493 positive / 3076 negative | Symptoms plus molecular PCR and rapid-antigen evidence |
| E | Dengue | D7 additional evaluation | 989; 644 positive / 345 negative | Hematology-focused; four headings mapped to existing Dengue EFNs |
| F | Dengue | D3 additional evaluation | 1018; 697 positive / 321 negative | Clinical symptoms plus platelet and WBC evidence; eight headings mapped to existing Dengue EFNs |
Table 2 : Datasets used in the executed CKG Reasoner evaluation.
| Original variable | Ontology feature | Original variable | Ontology feature |
|---|---|---|---|
| Gender | Gender | Age | Age |
| NS1 | NS1 | IgG | IGG |
| IgM | IGM | Fever Duration | FEV_dura_dy |
| Body Temperature | FEV_temperature_c | Platelet Count | MTP_platelet_count |
| WBC Count | LEU_wbc | Joint Pain | ART_severity |
| Headache | HDH | Retro-Orbital Pain | ROP |
Table 3 : Audited mapping of variables in Dengue Cohort A to ontology features.
| Study | Method / Architecture Description | Artifact / Access Status | Technical Reason Precluding Execution on Target Cohorts |
|---|---|---|---|
| [ 1 ] | Hybrid rule-based/probabilistic expert system combining expert outputs via neural network. | Paywalled (IEEE) No public code | Historical 1992 proceeding pre-dating digital code repositories; no source code, inference software, or calibrated parameters available. |
| [ 6 ] | Ontology-grounded fuzzy decision support system using semantic similarity for diabetes. | Open Access (IEEE Access) No public code | Code unreleased; hospital training dataset is private; transfer to acute febrile cohorts impossible without calibrated fuzzy membership functions. |
| [ 10 ] | Siamese Bayesian networks incorporating symptom absence as negative evidence over learned BNs. | Paywalled (ACM) Proprietary commercial IP | Developed as proprietary commercial IP for the mFine telemedicine platform; network topologies, learned weights, and code are strictly unreleased. |
| [ 12 ] | DKDR: Knowledge graph reasoning with deep reinforcement learning for interactive diagnosis. | Paywalled (IEEE) No public code | Closed conference proceeding; no public code repository, pre-trained policy checkpoints, or KG simulation environment released. |
| [ 16 ] | Multi-hop KG path reasoning with dynamic TF-IDF and Naive Bayes weights for TCM diagnosis. | Open Access (CMC) No public code | Open-access publication, but no source code, reasoning scripts, or path weight tables were publicly deposited. |
| [ 27 ] | Semantic KG reasoning with likelihood-ratio-weighted edges and conditional patient constraints. | Paywalled (Springer) No public code | Published as a conceptual book chapter in conference proceedings; no software implementation, graph exports, or code packages released. |
Table 4 : Reproducibility and empirical execution audit of representative diagnostic reasoning systems surveyed in Table 1 when considered for baseline benchmarking on our target cross-sectional cohorts.
| Cohort | Dataset Name | Accuracy | F1 Score | Sensitivity | Specificity | Precision | ROC-AUC | PR-AUC | Test Size (Pos / Neg) |
|---|---|---|---|---|---|---|---|---|---|
| A | Bangladesh mixed evidence (Dengue) | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% | 1.0000 | 1.0000 | 200 (107 / 93) |
| B | Hematology (Dengue) | 60.00% | 68.23% | 62.68% | 54.17% | 74.86% | 0.6513 | 0.7793 | 305 (209 / 96) |
| C | Clinical Data (Bangladesh) (Malaria) | 70.09% | 69.89% | 71.03% | 69.20% | 68.78% | 0.7539 | 0.7022 | 438 (214 / 224) |
| D | Thailand ILI cohort (Influenza) | 88.84% | 82.59% | 80.94% | 92.68% | 84.32% | 0.9268 | 0.8784 | 914 (299 / 615) |
| E | D7 additional evaluation (Dengue) | 92.42% | 94.30% | 96.12% | 85.51% | 92.54% | 0.8968 | 0.8918 | 198 (129 / 69) |
| F | D3 additional evaluation (Dengue) | 99.02% | 99.29% | 99.29% | 98.44% | 99.29% | 0.9941 | 0.9971 | 204 (140 / 64) |
Table 5 : Cross-cohort supervised logistic regression baseline performance across the six evaluation cohorts. Thailand Influenza and Dengue Cohort A incorporate point-of-care rapid testing alongside clinical and laboratory presentation. Bold highlights values as formatted in the supplied baseline table; cross-cohort values are not directly comparable because cohorts differ.
| Dataset | Target | N | Accuracy | Bal. Acc. | F1 + | MCC | Coverage | Silhouette |
|---|---|---|---|---|---|---|---|---|
| A: Bangladesh mixed evidence | Dengue | 1000 | 0.996 | 0.996 | 0.996 | – | 1.000 | 0.724 |
| B: Hematology | Dengue | 1523 | 0.558 | 0.558 | 0.634 | 0.108 | 1.000 | 0.635 |
| C: Clinical Data (Bangladesh) | Malaria | 2190 | 0.707 | 0.706 | 0.695 | 0.413 | 1.000 | 0.889 |
| D: Thailand ILI cohort | Influenza | 4569 | 0.906 | 0.871 | 0.842 | 0.783 | 1.000 | 0.842 |
| E: D7 additional evaluation | Dengue | 989 | 0.914 | 0.895 | 0.936 | – | 1.000 | 0.870 |
| F: D3 additional evaluation | Dengue | 1018 | 0.893 | 0.907 | 0.917 | 0.776 | 1.000 | 0.601 |
Table 6 : Uniform KG partition results. F1 is positive-class F1; all six cohorts have full partition-decision coverage. MCC is omitted where it was unavailable for the uniform protocol.
| Dataset | Full | No | No | No | only | Profile only | No CS |
|---|---|---|---|---|---|---|---|
| B | 0.634 | 0.612 | 0.660 | 0.607 | 0.694 | 0.612 | 0.634 |
| C | 0.695 | 0.695 | 0.695 | 0.692 | 0.695 | 0.695 | 0.695 |
| D | 0.842 | 0.842 | 0.843 | 0.844 | 1.000 | 0.842 | 0.842 |
| F | 0.917 | 0.925 | 0.928 | 0.930 | 0.931 | 0.925 | 0.917 |
Table 7 : Available component ablations for Datasets B, C, D, and F relative to the full four-dimensional post-reasoning representation. Values are positive-class F1. The No-CS column is the explicit three-dimensional ablation.