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PANDIA: Personalized neuro-symbolic multimodal fusion for interpretable neonatal pain assessment.

Code ↔ Paper

31 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 31 matches
  1. [1] § 4. Experimentation and validation › 4.1 Datasets › 4.1.1 Dataset access and reproducibility. ↔ preprocessing/preprocess.py, lines 1–15 · score 0.96 · timestamp alignment, wavelet denoising, mel spectrogram, standardized preprocessing, face detection, linear interpolation
  2. [2] § 3. Methodology › 3.2 PANDIA architecture › 3.2.3 Concept bottleneck layer. ↔ pandia/models/concept_bottleneck.py, lines 1–28 · score 0.94 · eye squeeze, harmonic distortion, silence periods, mouth opening, cry intensity, brow lowering
  3. [3] § 3. Methodology › 3.2 PANDIA architecture › 3.2.3 Concept bottleneck layer. ↔ pandia/models/pandia.py, lines 149–186 · score 0.91 · eye squeeze, harmonic distortion, silence periods, mouth opening, cry intensity, brow lowering
  4. [4] § 3. Methodology › 3.2 PANDIA architecture › 3.2.7 Symbolic rule engine. ↔ pandia/models/symbolic_rules.py, lines 1–17 · score 0.88 · high confidence pain, conflicting indicator, NICU clinicians, pain baseline, moderate pain, escalation
  5. [5] § 3. Methodology › 3.2 PANDIA architecture › 3.2.7 Symbolic rule engine. ↔ pandia/models/symbolic_rules.py, lines 1–17 · score 0.88 · high confidence pain, NICU clinicians, pain baseline, moderate pain, gestational age, rule engine
  6. [6] § 3. Methodology › 3.2 PANDIA architecture › 3.2.4 Relational graph reasoner. ↔ pandia/models/graph_reasoner.py, lines 49–108 · score 0.85 · edges incident, absent modalities, concept nodes, missing modality, adjacency, zero
  7. [7] § 4. Experimentation and validation › 4.3 Evaluation metrics ↔ pandia/evaluation/metrics.py, lines 16–62 · score 0.84 · Brier Score, F1 macro, metrics, NPV, PPV, MCE
  8. [8] § 3. Methodology › 3.2 PANDIA architecture › 3.2.1 Module integration and data flow. ↔ pandia/models/symbolic_rules.py, lines 30–42 · score 0.81 · tier conflict resolution, human readable, Concept activations, rule engine, Algorithm, Symbolic
  9. [9] § 3. Methodology › 3.3 Training strategy › 3.3.3 Cybersecurity and deployment security. ↔ pandia/training/federated.py, lines 1–16 · score 0.80 · KL divergence, Anomaly detection, DP SGD, gradient, clipping, round
  10. [10] § 3. Methodology › 3.2 PANDIA architecture › 3.2.7 Symbolic rule engine. ↔ pandia/models/symbolic_rules.py, lines 30–42 · score 0.77 · tier conflict resolution, concept activations, rule engine, Algorithm, Symbolic, thresholds
  11. [11] § 3. Methodology › 3.2 PANDIA architecture › 3.2.5 Meta-learning personalization. ↔ pandia/models/meta_learner.py, lines 1–16 · score 0.77 · graph attention weights, concept bottleneck parameters, Meta learning, frozen, classifier, personalization
  12. [12] § 3. Methodology › 3.2 PANDIA architecture › 3.2.7 Symbolic rule engine. ↔ pandia/models/symbolic_rules.py, lines 101–147 · score 0.76 · High pain evidence, HR spike, intense crying, facial distress, Symbolic
  13. [13] § 4. Experimentation and validation › 4.2 Implementation details ↔ pandia/utils/data_utils.py, lines 17–103 · score 0.71 · random horizontal flipping, jittering, speed, Augmentation, noise, Temporal
  14. [14] § 3. Methodology › 3.2 PANDIA architecture › 3.2.5 Meta-learning personalization. ↔ pandia/utils/data_utils.py, lines 17–103 · score 0.70 · postnatal age, Contextual metadata, gestational age, 35 days, infant, weights
  15. [15] § 4. Experimentation and validation › 4.3 Evaluation metrics ↔ pandia/evaluation/metrics.py, lines 16–62 · score 0.68 · cohen kappa score, metrics, Quadratic, matrix, QWK, Weighted
  16. [16] § 3. Methodology › 3.2 PANDIA architecture ↔ pandia/training/losses.py, lines 113–150 · score 0.66 · graph sparsity, ordinal pain, concept supervision, Tensor, prediction, evidential
  17. [17] § 4. Experimentation and validation › 4.2 Implementation details ↔ pandia/training/trainer.py, lines 22–94 · score 0.65 · cosine annealing, AdamW, optimizer, batch, Training
  18. [18] § 3. Methodology › 3.2 PANDIA architecture › 3.2.1 Module integration and data flow. ↔ pandia/models/graph_reasoner.py, lines 49–108 · score 0.64 · GraphSAGE, learned adjacency, fused, nodes, layers, Module
  19. [19] § 3. Methodology › 3.2 PANDIA architecture › 3.2.1 Module integration and data flow. ↔ pandia/models/meta_learner.py, lines 1–16 · score 0.63 · graph attention weights, Meta learning, frozen, classifier, infant
  20. [20] § 3. Methodology › 3.2 PANDIA architecture ↔ pandia/training/trainer.py, lines 1–19 · score 0.61 · federated contrastive pretraining, multi task supervised, meta learning, PANDIA
  21. [21] § 3. Methodology › 3.2 PANDIA architecture › 3.2.1 Module integration and data flow. ↔ pandia/models/evidential_output.py, lines 1–12 · score 0.61 · epistemic uncertainty, system abstains, defers, Evidential, clinician, layer
  22. [22] § 3. Methodology › 3.3 Training strategy › 3.3.5 Multi-task supervised learning. ↔ pandia/training/losses.py, lines 113–150 · score 0.58 · ordinal loss, Concept supervision, prediction, weights, training, pain
  23. [23] § 3. Methodology › 3.3 Training strategy › 3.3.4 Federated infrastructure risk assessment (NIST CSF). ↔ pandia/training/federated.py, lines 78–197 · score 0.57 · global model, server, clients, aggregates, round, weighted
  24. [24] § 3. Methodology › 3.3 Training strategy › 3.3.4 Federated infrastructure risk assessment (NIST CSF). ↔ scripts/federated_train.py, lines 39–94 · score 0.57 · global model, server, clients, round, epochs, DP
  25. [25] § 3. Methodology › 3.2 PANDIA architecture › 3.2.2 Lightweight modality encoders. ↔ pandia/encoders/video_encoder.py, lines 48–69 · score 0.57 · Video Encoder, MobileNetV3, temporal, TCN
  26. [26] § 3. Methodology › 3.2 PANDIA architecture › 3.2.2 Lightweight modality encoders. ↔ pandia/encoders/audio_encoder.py, lines 1–9 · score 0.56 · mel spectrograms, Audio Encoder, TCN
  27. [27] § 3. Methodology › 3.2 PANDIA architecture › 3.2.3 Concept bottleneck layer. ↔ pandia/training/losses.py, lines 14–30 · score 0.56 · weak supervision, semi supervised, training
  28. [28] § 3. Methodology › 3.3 Training strategy › 3.3.4 Federated infrastructure risk assessment (NIST CSF). ↔ pandia/training/federated.py, lines 1–16 · score 0.54 · FedAvg, 1–3, IID, federated, training
  29. [29] § 3. Methodology › 3.3 Training strategy › 3.3.4 Federated infrastructure risk assessment (NIST CSF). ↔ scripts/federated_train.py, lines 39–94 · score 0.53 · global model, federated model, rounds, loss, device, PANDIA
  30. [30] § 4. Experimentation and validation › 4.2 Implementation details ↔ pandia/models/meta_learner.py, lines 37–79 · score 0.53 · inner loop, query, gradient, Meta, infant
  31. [31] § 4. Experimentation and validation › 4.2 Implementation details ↔ pandia/models/concept_bottleneck.py, lines 55–102 · score 0.51 · supervised concepts, concept bottleneck, dropout, hidden

Paper

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The authors' code

Python · 220 lines · 9.9 KB · MIT · 5 matches

  1. """
  2. Symbolic Rule Engine
  3. ────────────────────
  4. 18 hand-crafted rules (3 tiers) developed with NICU clinicians.
  5. Tier 1 (6 rules): High-confidence pain — all 3 primary indicators fire
  6. Tier 2 (5 rules): Moderate pain — any 2 indicators agree
  7. Tier 3 (7 rules): Conflicting indicators → uncertainty escalation / no-pain baseline
  9. Thresholds are gestational-age-adjusted:
  10. τ_b ∈ [0.60 (<28w) … 0.75 (≥37w)]
  12. Algorithm 1 & 2 from the paper.
  13. """
  14. from dataclasses import dataclass, field
  15. from typing import Optional
  16. import torch
  17. from pandia.models.concept_bottleneck import get_ga_stratum, GA_THRESHOLDS, CONCEPT_NAMES
  20. @dataclass
  21. class RuleOutput:
  22. pain_level: int # 0=none, 1=mild, 2=moderate, 3=severe
  23. confidence_tier: int # 1, 2, or 3
  24. abstain: bool
  25. explanation: str
  26. recommendation: str
  27. active_concepts: list = field(default_factory=list)
  30. class SymbolicRuleEngine:
  31. """
  32. Translates 12 concept activations → human-readable pain assessment.
  33. Implements the three-tier conflict-resolution mechanism (Algorithm 2).
  34. """
  36. # Default thresholds (term infant, ≥37w)
  37. TAU_DEFAULT = dict(
  38. brow_lower=0.70, cry_intensity=0.60, hr_accel=0.50,
  39. eye_squeeze=0.70, nasolabial=0.65, mouth_open=0.65,
  40. f0_elev=0.60, harmonic=0.55, silence=0.55,
  41. o2_desat=0.55, resp_irreg=0.55, bp_elev=0.55,
  42. )
  44. def __init__(self, abstain_u_threshold=0.50):
  45. self.abstain_u_threshold = abstain_u_threshold
  47. def _get_thresholds(self, gestational_age_weeks: float) -> dict:
  48. """Scale default thresholds by GA stratum (±0.15 range)."""
  49. s = get_ga_stratum(gestational_age_weeks)
  50. ga_row = GA_THRESHOLDS[s] # tensor (12,)
  51. base = GA_THRESHOLDS[3] # term baseline
  52. delta = ga_row - base # negative for preterm
  53. tau = dict(self.TAU_DEFAULT)
  54. for i, name in enumerate(CONCEPT_NAMES):
  55. key = name.replace("_acceleration", "_accel") \
  56. .replace("_desaturation", "_desat") \
  57. .replace("_irregularity", "_irreg") \
  58. .replace("_elevation", "_elev")
  59. if key in tau:
  60. tau[key] = float(tau[key] + delta[i].item())
  61. return tau
  63. def evaluate(self, concepts: torch.Tensor, uncertainty: float,
  64. gestational_age_weeks: float = 37.0,
  65. ventilated: bool = False) -> RuleOutput:
  66. """
  67. Args:
  68. concepts: (12,) tensor of concept activations ∈ [0,1]
  69. uncertainty: scalar epistemic uncertainty u
  70. gestational_age_weeks: float
  71. ventilated: suppress audio concepts C5-C8 if True
  72. Returns:
  73. RuleOutput
  74. """
  75. c = concepts.detach().cpu().float()
  76. tau = self._get_thresholds(gestational_age_weeks)
  78. # Named activations
  79. C = {name: float(c[i]) for i, name in enumerate(CONCEPT_NAMES)}
  81. # Ventilated infant protocol — suppress audio concepts
  82. if ventilated:
  83. for k in ["cry_intensity", "f0_elevation", "harmonic_distortion", "silence_periods"]:
  84. C[k] = 0.0
  86. # ── Tier 1: Physiological Override ───────────────────────────────
  87. tier1_fire = self._tier1_rules(C, tau, uncertainty)
  88. if tier1_fire:
  89. return tier1_fire
  91. # ── Tier 2: Symbolic Consensus ───────────────────────────────────
  92. tier2_fire = self._tier2_rules(C, tau, uncertainty)
  93. if tier2_fire:
  94. return tier2_fire
  96. # ── Tier 3: Uncertainty Escalation ───────────────────────────────
  97. return self._tier3_rules(C, tau, uncertainty)
  99. # ── Tier 1 Rules (6) ─────────────────────────────────────────────────
  101. def _tier1_rules(self, C, tau, u) -> Optional[RuleOutput]:
  102. """All 3 primary indicators exceed threshold → immediate high pain alert."""
  103. bl = C["brow_lowering"] > tau.get("brow_lower", 0.70)
  104. ci = C["cry_intensity"] > tau.get("cry_intensity", 0.60)
  105. hr = C["hr_acceleration"] > tau.get("hr_accel", 0.50)
  106. o2 = C["o2_desaturation"] > tau.get("o2_desat", 0.55)
  107. es = C["eye_squeeze"] > tau.get("eye_squeeze", 0.70)
  108. mo = C["mouth_opening"] > tau.get("mouth_open", 0.65)
  110. active = [n for n, v in [("brow_lowering", bl), ("cry_intensity", ci),
  111. ("hr_acceleration", hr), ("o2_desaturation", o2),
  112. ("eye_squeeze", es), ("mouth_opening", mo)] if v]
  114. # Rule T1-1: All three primary indicators
  115. if bl and ci and hr:
  116. return RuleOutput(3, 1, False,
  117. "High pain evidence: facial distress, intense crying, HR spike.",
  118. "Immediate clinical assessment required.", active)
  119. # Rule T1-2: Facial + O2
  120. if bl and es and o2:
  121. return RuleOutput(3, 1, False,
  122. "High pain evidence: facial grimacing, O2 desaturation.",
  123. "Immediate clinical assessment required.", active)
  124. # Rule T1-3: Cry + HR + O2
  125. if ci and hr and o2:
  126. return RuleOutput(3, 1, False,
  127. "High pain evidence: cry, HR spike, O2 drop.",
  128. "Immediate clinical assessment required.", active)
  129. # Rule T1-4: All facial
  130. if bl and es and mo:
  131. return RuleOutput(2, 1, False,
  132. "Moderate-high pain: complete facial pain expression.",
  133. "Prompt pain assessment recommended.", active)
  134. # Rule T1-5: HR + resp
  135. ri = C["resp_irregularity"] > tau.get("resp_irreg", 0.55)
  136. if hr and ri and o2:
  137. return RuleOutput(3, 1, False,
  138. "High pain: physiological triad (HR, RR, SpO2).",
  139. "Immediate clinical assessment required.", active)
  140. # Rule T1-6: F0 + harmonic + cry
  141. f0 = C["f0_elevation"] > tau.get("f0_elev", 0.60)
  142. hd = C["harmonic_distortion"] > tau.get("harmonic", 0.55)
  143. if ci and f0 and hd:
  144. return RuleOutput(2, 1, False,
  145. "High pain cry acoustics: intensity, F0, harmonic distortion.",
  146. "Prompt pain assessment recommended.", active)
  147. return None
  149. # ── Tier 2 Rules (5) ─────────────────────────────────────────────────
  151. def _tier2_rules(self, C, tau, u) -> Optional[RuleOutput]:
  152. bl = C["brow_lowering"] > tau.get("brow_lower", 0.70)
  153. ci = C["cry_intensity"] > tau.get("cry_intensity", 0.60)
  154. hr = C["hr_acceleration"]> tau.get("hr_accel", 0.50)
  155. mo = C["mouth_opening"] > tau.get("mouth_open", 0.65)
  156. ri = C["resp_irregularity"] > tau.get("resp_irreg", 0.55)
  158. active = [n for n, v in [("brow_lowering", bl), ("cry_intensity", ci),
  159. ("hr_acceleration", hr), ("mouth_opening", mo),
  160. ("resp_irregularity", ri)] if v]
  161. # Rule T2-1
  162. if bl and ci:
  163. return RuleOutput(2, 2, False,
  164. "Moderate pain: facial distress with crying.",
  165. "Pain assessment recommended within 5 minutes.", active)
  166. # Rule T2-2
  167. if bl and hr:
  168. return RuleOutput(2, 2, False,
  169. "Moderate pain: facial distress with HR elevation.",
  170. "Pain assessment recommended within 5 minutes.", active)
  171. # Rule T2-3
  172. if ci and hr:
  173. return RuleOutput(1, 2, False,
  174. "Mild-moderate pain: crying with HR response.",
  175. "Monitor closely; reassess in 10 minutes.", active)
  176. # Rule T2-4
  177. if mo and ci:
  178. return RuleOutput(1, 2, False,
  179. "Mild pain: mouth opening with vocalization.",
  180. "Monitor; non-pharmacological comfort measures.", active)
  181. # Rule T2-5
  182. if hr and ri:
  183. return RuleOutput(1, 2, False,
  184. "Mild pain: physiological indicators (HR, RR).",
  185. "Monitor; reassess in 15 minutes.", active)
  186. return None
  188. # ── Tier 3 Rules (7) ─────────────────────────────────────────────────
  190. def _tier3_rules(self, C, tau, u) -> RuleOutput:
  191. # Always increment uncertainty
  192. u_adjusted = min(u + 0.10, 1.0)
  194. if u_adjusted > self.abstain_u_threshold:
  195. return RuleOutput(0, 3, True,
  196. "Conflicting or insufficient signals.",
  197. "Clinician review required.", [])
  199. # Sub-rules T3-1…T3-4: single-indicator mild signals
  200. bl = C["brow_lowering"] > tau.get("brow_lower", 0.70) * 0.8
  201. ci = C["cry_intensity"] > tau.get("cry_intensity", 0.60) * 0.8
  202. hr = C["hr_acceleration"] > tau.get("hr_accel", 0.50) * 0.8
  204. if bl:
  205. return RuleOutput(1, 3, False,
  206. "Possible mild pain: isolated brow lowering.",
  207. "Observe; reassess in 15 minutes.", ["brow_lowering"])
  208. if ci:
  209. return RuleOutput(1, 3, False,
  210. "Possible mild pain: isolated cry signal.",
  211. "Observe; reassess in 15 minutes.", ["cry_intensity"])
  212. if hr:
  213. return RuleOutput(1, 3, False,
  214. "Possible mild pain: isolated HR response.",
  215. "Observe; reassess in 15 minutes.", ["hr_acceleration"])
  217. # No pain baseline
  218. return RuleOutput(0, 3, False,
  219. "No significant pain indicators detected.",
  220. "Continue routine monitoring.", [])

symbolic_rules.py at commit 60bfd15, under MIT · at the source

Overview

Authors: Oussama El Othmani1,2, Sami Naouali3
ORCID iDs: Sami Naouali
  1. Computer Science Department, Military Academy of Fondouk Jedid, Nabeul, Tunisia
  2. Military Research Center, Aouina, Tunisia
  3. Information Systems Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa, Saudi Arabia
Institutions: Military Academy, Tunisia (Tunisia); King Faisal University (Saudi Arabia)
Journal: PLOS digital health, volume 5, issue 5, article e0001442
Dates: received 28 January 2026; accepted 3 May 2026; published online 26 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pdig.0001442 · PMID 42189831 · PMCID PMC13210372 · OpenAlex W7162465201
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), pain (population), developmental (subfield)
Methods: Statistics, Spectral & time-frequency, Evoked potentials, Machine learning, fMRI & imaging, Physiology & signal measures
Topic: Pediatric Pain Management Techniques (Pediatrics, Perinatology and Child Health, Medicine), according to OpenAlex
Funding: Nvidia; King Faisal University; Medical Research Council
Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Effective pain assessment in infants aged 0–3 months is a critical challenge in neonatal intensive care units (NICUs) and family medicine clinics, where self-reporting is impossible and current observational tools remain subjective and inconsistent. This paper presents PANDIA (Personalized Adaptive Neuro-symbolic Data-fusion for Infant Assessment), a novel multimodal AI system that combines hierarchical representation learning, graph-based inter-modal reasoning, meta-learning personalization, and symbolic concept-bottleneck explanations for robust infant pain assessment. Unlike transformer-centric approaches, PANDIA employs lightweight CNN/TCN backbones with a graph neural network for inter-modal fusion, achieving clinical interpretability through explicit concept bottlenecks and symbolic reasoning. Our federated learning framework enables privacy-preserving multi-site collaboration while meta-learning adaptation provides personalized assessment with minimal per-infant data. Evaluated on 2,847 infants across four datasets, PANDIA achieves 87.3% accuracy with 92.1% clinician acceptance rate for explanations, achieving a 12.4% accuracy improvement over the best baseline, consistent across all four datasets and an independent out-of-distribution test set, while maintaining fewer than 30M parameters for edge deployment. The proposed system offers a structured and interpretable step toward deploying explainable AI in early-life pain management, with potential to improve care quality and support medical decision-making. Key limitations include the retrospective validation design, dataset heterogeneity across collection sites, and the need for prospective clinical trials before deployment in live clinical settings. All code, trained models, preprocessing pipelines, and supplementary materials are fully publicly available without restriction at: https://github.com/oussama123-ai/pandia. The NICU-MM dataset is available upon request subject to an ethical data use agreement; the access procedure is detailed in Section 4.1.1.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 31 matches between paragraphs and lines of code.

oussama123-ai/pandia

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 60bfd153f3c7389f907072ebdf32a88d6322b86d, 16 March 2026
Languages: Python (33)
Size: 43 files, 33 scripts
Software Heritage: not archived
Found in: “4.1.1 Dataset access and reproducibility.”
Holds: README, license file, environment (Dockerfile, environment.yml, requirements.txt, setup.py), tests
Not found: CITATION.cff, continuous integration, documentation
Tools: PyTorch (21 files), NumPy (6 files), OpenCV (1 file), Pillow (1 file), PyWavelets (1 file), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
35 files
At the source: github.com/oussama123-ai/pandia

The paper's code and data availability statement is in the Data section.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data

Datasets cited

Data Availability

All code, trained models, preprocessing pipelines, and evaluation scripts required to reproduce the findings of this study are fully publicly available without restriction at: https://github.com/oussama123-ai/pandia. No registration, data use agreement, or institutional approval is required to access these materials. The iCOPE, NPAD, and APN datasets are publicly available or accessible via the sources described in Section 4.1.1. In particular, the NPAD dataset [42] is available at https://rpal.cse.usf.edu/project_neonatal_pain/dataset.html. To support transparency and reproducibility, an anonymized subset of processed data, including representative feature embeddings, model outputs, and evaluation splits, is publicly available at: https://doi.org/10.5281/zenodo.19727881. These data enable independent verification of the reported results without requiring access to sensitive raw recordings. Due to the sensitive nature of neonatal video and audio recordings involving minor participants and multi-jurisdictional ethical oversight (HIPAA, GDPR, IRB-MRC-MHT-2022-001, KFU-REC-2023-10-28), the NICU-MM dataset cannot be made fully publicly available. This restriction is consistent with the PLOS Digital Health exception policy for ethically restricted data [43]. Researchers may request access to the NICU-MM dataset for legitimate scientific purposes by contacting the Military Research Center Ethics Committee, Military Hospital of Tunis, Tunisia (IRB-MRC-MHT-2022-001; ). The committee is independent of the authors and is responsible for data governance. Requests should include a description of the intended use and appropriate data protection measures and will be reviewed within 30 business days.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

  • Funding: added Nvidia; King Faisal University; Medical Research Council

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 23 references.

Cite

This paper

El Othmani, O., & Naouali, S. (2026). PANDIA: Personalized neuro-symbolic multimodal fusion for interpretable neonatal pain assessment. PLOS digital health, 5(5), e0001442. https://doi.org/10.1371/journal.pdig.0001442

BibTeX

@article{elothmani2026pandia,
author = {El Othmani, Oussama and Naouali, Sami},
title = {{PANDIA: Personalized neuro-symbolic multimodal fusion for interpretable neonatal pain assessment}},
journal = {PLOS digital health},
year = {2026},
month = may,
volume = {5},
number = {5},
pages = {e0001442},
publisher = {PLOS},
issn = {2767-3170},
doi = {10.1371/journal.pdig.0001442},
url = {https://doi.org/10.1371/journal.pdig.0001442},
pmid = {42189831},
pmcid = {PMC13210372}
}

RIS

TY - JOUR
AU - El Othmani, Oussama
AU - Naouali, Sami
TI - PANDIA: Personalized neuro-symbolic multimodal fusion for interpretable neonatal pain assessment
T2 - PLOS digital health
J2 - PLOS Digit Health
PY - 2026
DA - 2026/05/26
VL - 5
IS - 5
SP - e0001442
SN - 2767-3170
PB - PLOS
DO - 10.1371/journal.pdig.0001442
UR - https://doi.org/10.1371/journal.pdig.0001442
LA - en
ER -

CSL-JSON

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"date-parts": [
[
2026,
5,
26
]
]
}
}

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