Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline

• URL: http://arxiv.org/abs/2605.26560v1
• Date: Tue, 26 May 2026 05:14:33 GMT
• Title: Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline
• Abstract summary: We test a hybrid neural-symbolic pipeline against direct generation.<n>We evaluate on a 2,000-note synthetic outpatient corpus with action-disjoint splits.
• Score: 43.0484058393522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective. Outpatient notes carry follow-up instructions pairing actions with future times ("MRI brain in two weeks"). Extracting (action, date) pairs supports scheduling and audit, but generative extractors miss the date because linking and arithmetic are implicit in decoding. We test a hybrid neural-symbolic pipeline against direct generation. Methods. We define TestSpecification and TimeSpecification entities and a ScheduledFor relation. BioBERT feeds BIO tagging and a biaffine linker; entities are canonicalized via a 28-action ontology and times normalized to day offsets deterministically. We evaluate on a 2,000-note synthetic outpatient corpus with action-disjoint splits (18 train, 6 OOV-test) against zero-shot GPT-4o-mini and LoRA-fine-tuned LLaMA-3 8B with note-level bootstrap 95% CIs. Results. On 259-note seen and OOV splits the hybrid pipeline achieves Test-Time Pair F1 of 0.997 and 0.986 with 0.00-day MAE. Baselines reach high action F1 (LLaMA-3 0.992; GPT-4o-mini 0.963 seen) but Pair F1 stays at 0.51-0.57 (LLaMA-3) and 0.53 (GPT-4o-mini), CIs non-overlapping with the hybrid. Conclusion. Separating learned entity extraction from deterministic date arithmetic outperforms generation on this benchmark, generalizes to held-out actions, and exposes failure modes. Transfer to real EHR notes is the next validation; a first-pass realism check is in Limitations.

Related papers

• Modalities Should Talk to Each Other: Dual-Stream Multimodal Learning for Long-Horizon Influenza Forecasting [1.2599533416395763]
  Publicly available surveillance datasets typically pair numeric epidemiological signals with noisy, loosely structured, only indirectly related to near-term trends, and often lagged relative to the numeric signal.<n>We propose Dual-Stream Attention (DSA), a multimodal deep learning framework that forecasts 12-week-ahead ILI activity from a 36-week multimodal history by letting the numerical and textual streams condition each other.
  arXiv  Detail & Related papers  (2026-08-24T15:20:01Z)
• CT-HEG: A Bidirectional, Timestamp-Attributed Event Graph for ICU In-Hospital Mortality Prediction - An Architectural Ablation Study [0.23453441553817042]
  Continuous-Time Heterogeneous EHR Graph (CT-HEG) encodes each ICU stay as a typed, timestamped graph with three node types.<n>We instantiate CT-HEG as CHIRP-Net, a four-layer heterogeneous GATv2Conv network, evaluated on MIMIC-IV v3.1.
  arXiv  Detail & Related papers  (2026-08-01T19:10:38Z)
• Leakage-Robust Evaluation and Data-Scale Sensitivity of Attention-Enhanced Multi-Task Learning for Joint Fault Diagnosis and Remaining Useful Life Estimation [0.0]
  Multi-task deep learning models can be affected by how sliding-window sequences are split into training and test sets.<n>We show that naive splitting can inflate classification accuracy from a genuine 20-60 percent to 99.9 percent.<n>We introduce a chunk-based, leakage-audited splitting protocol and evaluate all models using five seeds, one-way ANOVA, and Tukey HSD tests.
  arXiv  Detail & Related papers  (2026-07-17T20:33:36Z)
• Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence [45.78409219344429]
  Multimodal large language models (MLLMs) show strong promise for clinical VQA and radiology report generation, yet inference-time hallucinations undermine trustworthy use.<n>We propose a holistic, training-free evidence-injection framework that systematically mitigates hallucinations through dual-side evidence injection.<n>We conduct systematic evaluations on 2 tasks and 5 datasets using textttLLaVA-1.5-7B, texttLLaVA-Med-1.5-7B, texttQwen3-VL-8B/32B, and textttInternVL
  arXiv  Detail & Related papers  (2026-06-30T08:07:25Z)
• Improving IoT Intrusion Detection Through SMOTE-Based Oversampling and Extended Multi-Model Evaluation on Side-Channel Power Data [0.0]
  This paper deals with the problem of imbalance in the side-channel dataset for power-based intrusion detection.<n>Random Forest reached a micro-averaged F1 score of 0.9989 and macro F1 of 0.9794, outperforming the previously best micro-F1 result.<n>Extra Trees provided the same performance as well, but at 10 times faster.
  arXiv  Detail & Related papers  (2026-05-29T10:10:13Z)
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• A Controlled Synthetic Benchmark for Educational Aspect-Based Sentiment Analysis [47.027290803102666]
  This study introduces a controlled benchmark for educational ABSA built from 10,000 synthetic course reviews.<n>The strongest untuned model, BERT, reaches a held-out detection micro-F1 of 0.2760.<n>A conservative external evaluation on 2,829 mapped student-feedback reviews from Herath et al. yields a micro-F1 of 0.4593 for BERT on a 9-aspect overlap.
  arXiv  Detail & Related papers  (2026-05-25T07:05:21Z)
• Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputation [0.0]
  We propose a two-stage pipeline for treatment effect estimation from incomplete longitudinal EHRs.<n>CaulFlow-T, a directed acyclic graph (DAG)-constrained normalizing flow with long short-term memory (LSTM)-encoded patient history.<n>CaulFlow-T performs exact invertible counterfactual inference, avoiding approximation errors from variational inference and separating confounding through explicit causal structure.
  arXiv  Detail & Related papers  (2026-05-06T16:53:08Z)
• When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors [66.18091962164219]
  Existing metrics fail to distinguish mandatory behaviors required for task success from non-mandatory patterns that reflect a model's autonomous preferences.<n>We propose two complementary metrics to isolate non-mandatory behavioral patterns: textbfResponse Pattern Similarity (RPS) for verbal alignment and textbfAction Graph Similarity (AGS) for tool-use habits modeled as directed graphs.
  arXiv  Detail & Related papers  (2026-04-23T03:48:56Z)
• Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models [0.3250525349446657]
  We evaluate how representation decisions affect downstream prediction after a shared one-epoch pretraining budget.<n>We train 28 matched transformers on MIMIC-IV and evaluate them on 30 clinical outcomes.
  arXiv  Detail & Related papers  (2026-04-18T01:38:47Z)
• Benchmarking IoT Time-Series AD with Event-Level Augmentations [34.864214444544565]
  We introduce an evaluation protocol with unified event-level augmentations that simulate real-world issues.<n>We evaluate 14 representative models on five public anomaly datasets.
  arXiv  Detail & Related papers  (2026-02-17T09:45:44Z)
• When Domain Pretraining Interferes with Instruction Alignment: An Empirical Study of Adapter Merging in Medical LLMs [0.6345523830122167]
  Large language models can exhibit surprising adapter interference when combining domain adaptation and instruction alignment.<n>We study a two-stage LoRA pipeline for medical LLMs, where domain-oriented pre-training (PT) and supervised fine-tuning (SFT) are trained separately and later merged.
  arXiv  Detail & Related papers  (2026-01-26T10:54:06Z)
• U-Mamba2-SSL for Semi-Supervised Tooth and Pulp Segmentation in CBCT [44.3806898357896]
  We propose U-Mamba2-SSL, a novel semi-supervised learning framework that builds on the U-Mamba2 model and employs a multi-stage training strategy.<n>U-Mamba2-SSL achieved an average score of 0.789 and a DSC of 0.917 on the hidden test set, achieving first place in Task 1 of the STSR 2025 challenge.
  arXiv  Detail & Related papers  (2025-09-24T14:19:33Z)
• Advanced Deep Learning Techniques for Automated Segmentation of Type B Aortic Dissections [4.545298205355719]
  We developed four deep learning-based pipelines for Type B aortic dissection segmentation.<n>Our approach achieved superior segmentation accuracy, with Dice Coefficients of 0.91 $pm$ 0.07 for TL, 0.88 $pm$ 0.18 for FL, and 0.47 $pm$ 0.25 for.
  arXiv  Detail & Related papers  (2025-06-27T13:38:33Z)
• MAWIFlow Benchmark: Realistic Flow-Based Evaluation for Network Intrusion Detection [47.86433139298671]
  This paper introduces MAWIFlow, a flow-based benchmark derived from the MAWILAB v1.1 dataset.<n>The resulting datasets comprise temporally distinct samples from January 2011, 2016, and 2021, drawn from trans-Pacific backbone traffic.<n>Traditional machine learning methods, including Decision Trees, Random Forests, XGBoost, and Logistic Regression, are compared to a deep learning model based on a CNN-BiLSTM architecture.
  arXiv  Detail & Related papers  (2025-06-20T14:51:35Z)
• Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference [44.644520116360106]
  We estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control.<n>We propose a clustered switchback design, where units are grouped into clusters and time steps are grouped into blocks.<n>We show that for graphs that admit good clustering, a truncated Horvitz-Thompson estimator achieves a $tilde O(1/NT)$ mean squared error (MSE)<n>Our results simultaneously generalize the results from citethu2022switchback,ugander2013graph and citetleung2022rate
  arXiv  Detail & Related papers  (2023-12-25T01:00:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.