Follow Up Instructions Extractions

Reliability‑first actions and timeline extraction from unstructured doctor’s notes
BioBERT (NER + Action→Time linking) + deterministic date normalization

---

Project Motivation

Clinical outpatient notes often contain follow‑up instructions such as:

“Order MRI brain in two weeks.”

These instructions are crucial for scheduling, care coordination, and downstream EHR validation — but they are embedded in free text and can be ambiguous when multiple actions and time expressions appear in the same note.

Key challenge: robustly extract actions and their execution dates while avoiding arithmetic errors common in end‑to‑end text generation.

---

Problem Statement

Input

• note_text (free text clinical note)
• visit_date (anchor date)

Output

A JSON list of structured follow‑up items:

[
  {
    "action": "MRI Brain",
    "period_text": "in 2 weeks",
    "period_date": "2026-01-24"
  }
]

This decomposes into:

1. Action span detection (what to do)
2. Time span detection (when)
3. Action→Time linking (which time belongs to which action)
4. Date normalization anchored to visit_date

---

Visual Abstract

The system consists of:

Clinical Note ↓ Sliding Window Tokenization ↓ BioBERT Encoder ↓ Head A: BIO NER (Action/Time spans) ↓ Head B: Biaffine Linker (Action→Time) ↓ Date Normalization (visit_date anchored) ↓ Structured JSON Output

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Method Overview

We implement a joint multi‑task architecture with a shared BioBERT encoder feeding two heads:

1) Head A — NER (BIO tagging)

• Tags: O, B-ACT, I-ACT, B-TIME, I-TIME
• Learns to identify Action spans and Time spans
• Uses weighted cross‑entropy to reduce bias toward O tokens

2) Head B — Relation Extraction (Biaffine linker)

• Builds a span representation per entity: [start_state; end_state; width_embedding]
• Scores compatibility between each Action and each Time span using:
  • biaffine semantic compatibility
  • distance embeddings to encode proximity bias
  • a NONE option for actions with no explicit time

3) Post‑processing — Deterministic date normalization

• Uses dateparser with visit_date as the relative base to compute exact ISO dates.
• This separates semantic understanding (learned) from date arithmetic (deterministic), reducing hallucinated or inconsistent dates.

---

Dataset

To avoid PHI/HIPAA risks, the project uses a synthetic dataset generated with an LLM under a controlled schema.

Schema (per sample)

• Inputs:
  • note_text
  • visit_date
• Labels:
  • action_text, action_char_start, action_char_end
  • time_text, time_char_start, time_char_end
  • period_date (ISO date computed from visit_date + time_text)

Windowing for long notes

Clinical notes can exceed 512 tokens, so we use sliding windows:

• MAX_LEN = 512
• DOC_STRIDE = 128

The overlap reduces boundary truncation and preserves context around entities.

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Data Augmentation & Generation Methods

We applied:

Template randomization

Section reordering

Multi-action injection

History distractors

Clinical shorthand generation (x2w, q6mo, RTC 3mo)

Surface-form variation (weeks vs days vs months)

Stress-test subsets include:

Proximity traps

List-swapping traps

History traps

Section ambiguity

Shorthand temporal noise

---

Repository Structure

Recommended structure (matches course repo requirements):

.
├── Code/
│   ├── ll_project_follow_up_instruction_extraction_2k_dataset_submit.ipynb
│   └── requirements.txt
├── Data
│   └── synthetic_clinical_notes_2000.csv
├── Results/
│   ├── biobert_metrics.json
│   ├── chatgpt_metrics.json
│   └── llama_metrics.json
├── Slides/
│   ├── follow up instructions extraction final presentation pdf.pdf
│   ├── follow up instructions extraction final presentation.pptx
│   ├── follow up instructions extraction first presentation pdf.pdf
│   ├── follow up instructions extraction first presentation.pptx
│   ├── follow up instructions extraction interim presentation.pptx
│   └──follow up instructions extraction interim presentation pdf.pdf
├── Visuals/
│   ├── confusion matrix.png
│   ├── date error mae.png
│   ├── error distribution.png
│   ├── model comparison f1.png
│   └── note length variation by specialty plot.png
└── README.md

Training

batch_size 16 --lr 2e-5 --epochs 20 --fp16

Training details:
- Mixed precision: FP16
- Early stopping: patience 4 (based on validation loss)
- Joint objective:
    \( \mathcal{L} = \mathcal{L}_{NER} + \alpha\,\mathcal{L}_{LINK} \) with \(\alpha=1\) 

---

Expected output columns include:
- predicted action text
- predicted period text
- normalized ISO date (`period_date`)

---

Reported metrics:
- NER span F1 (Action, Time)
- Linking F1 (Action→Time)
- End‑to‑End Action+Date F1
- Date MAE (days)

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## Results Summary (synthetic held‑out)
Reported in the project report on a held‑out synthetic test split:
- **NER span F1:** > 99.7%
- **Linking F1:** > 98.2%
- **End‑to‑end date accuracy:** ~ 98.4%

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## Ethics & Data Privacy
- **No real patient data** is included in this repository.
- The dataset is synthetically generated to avoid PHI/HIPAA concerns.
- The system is intended as a research prototype; deployment requires governance, clinical validation, and privacy review.

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## Limitations
- **Synthetic‑only training/evaluation** may not fully reflect real EHR note variability.
- Date normalization depends on `dateparser` coverage and assumptions (e.g., locale).
- Complex discourse cases (implicit times, cross‑sentence references) may require additional modeling or global constraints.

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## Citation
If you use this code or dataset, cite as:

```bibtex
@misc{clinical_temporal_action_extraction_2026,
  title        = {Follow Up Instructions Extraction: Hybrid BioBERT + Deterministic Date Normalization},
  author       = {Michal Laufer},
  year         = {2026},
}