arxiv: 2605.12817 · v1 · submitted 2026-05-12 · 💻 cs.LG · cs.AI· cs.CL

Recognition: unknown

Training Large Language Models to Predict Clinical Events

Benjamin Turtel , Paul Wilczewski , Kris Skotheim

Authors on Pith no claims yet

Pith reviewed 2026-05-14 19:57 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL

keywords clinical predictionLoRA fine-tuningMIMIC-IIIlarge language modelsevent forecastingmodel calibrationnatural language supervision

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The pith

A small LoRA adapter trained on time-ordered clinical notes improves calibration for predicting future patient events.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper converts longitudinal MIMIC-III notes into supervised examples by pairing past patient context with natural-language questions about possible future events and labels drawn from later documentation in the same admission. This produces 6,900 examples spanning medications, procedures, organ support, microbiology, and mortality across 702 admissions. Training a small LoRA adapter on these examples reduces expected calibration error from 0.1269 to 0.0398 and Brier score from 0.199 to 0.145 on held-out questions while slightly outperforming GPT-5 point estimates. The method supplies reusable prediction supervision directly from existing notes without hand-engineered features or outcome-specific classifiers.

Core claim

Converting time-ordered clinical notes into examples of past context, a natural-language question about a possible future event, and a label resolved from later documentation allows a small LoRA adapter to be trained that improves over the prompted base model, cutting expected calibration error from 0.1269 to 0.0398 and Brier score from 0.199 to 0.145 while slightly outperforming GPT-5 on held-out questions.

What carries the argument

The conversion of longitudinal notes into prediction examples consisting of past context, a natural-language future-event question, and a later-documentation label, used to supervise LoRA fine-tuning.

If this is right

Enables reusable clinical prediction supervision from longitudinal notes without hand-engineered structured features.
Supports a single adapter across multiple event types instead of separate endpoint-specific classifiers.
Produces better-calibrated probability estimates than prompting the base model alone.
Yields slight gains over GPT-5 point estimates on held-out clinical questions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same note-to-example conversion could be applied to other large clinical corpora to test generalization beyond MIMIC-III.
Real-time deployment in electronic health records might allow ongoing updates to predictions as new notes arrive.
Combining the adapter with structured data streams could further reduce reliance on note-only supervision.

Load-bearing premise

Labels resolved from later documentation in the same admission accurately represent true future events without systematic bias, missing data, or documentation lag.

What would settle it

A prospective test on new admissions where model predictions are compared directly against actual clinical outcomes recorded after the prediction time.

Figures

Figures reproduced from arXiv: 2605.12817 by Benjamin Turtel, Kris Skotheim, Paul Wilczewski.

**Figure 1.** Figure 1: Clinical prediction pipeline. generation, label resolution, evaluation, and training, was performed using providers and environments confirmed to comply with the applicable data use requirements. MIMIC-III contains hospital admissions, ICU stays, demographics, diagnoses, procedures, medications, laboratory measurements, charted events, and longitudinal clinical notes. For each hospital admission, we const… view at source ↗

**Figure 2.** Figure 2: Test set performance by model [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Reliability diagram comparing prompted and fine-tuned models. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

read the original abstract

Longitudinal clinical notes contain rich evidence of how patients evolve over time, but converting this signal into training supervision for clinical prediction remains challenging. We extend Foresight Learning to clinical prediction by converting time-ordered MIMIC-III notes into examples consisting of past patient context, a natural-language question about a possible future event, and a label resolved from later documentation. This process yields 6,900 prediction examples from 702 admissions across medications, procedures, organ support, microbiology, and mortality. A small LoRA adapter trained on these examples improves over the prompted base model, reducing expected calibration error from 0.1269 to 0.0398 and Brier score from 0.199 to 0.145, while slightly outperforming GPT-5 point estimates on held-out questions. The approach enables reusable clinical prediction supervision from longitudinal notes without hand-engineered structured features or endpoint-specific classifiers.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper gives a workable pipeline for turning MIMIC-III notes into 6,900 labeled examples for clinical event prediction and reports calibration gains from a small LoRA adapter, but the labels pulled from later notes in the same admission are the main weak point.

read the letter

The main point is that the authors convert time-ordered MIMIC-III notes into past-context-plus-question examples with labels taken from later documentation, yielding 6,900 cases across medications, procedures, organ support, microbiology, and mortality. They then train a LoRA adapter that cuts expected calibration error from 0.1269 to 0.0398 and Brier score from 0.199 to 0.145 on held-out questions, while slightly beating GPT-5 point estimates. This is a direct, concrete extension of the Foresight Learning idea to clinical notes without requiring structured features or per-endpoint models. The pipeline itself is the clearest contribution here, and the numbers are specific enough to be useful as a starting point for others working on note-based supervision. What they do well is keep the setup simple and show measurable improvement over the prompted base model on the proxy task they defined. The approach is practical for anyone who already has access to longitudinal notes and wants to avoid building separate classifiers for each outcome. The soft spots are around label quality and evaluation details. Labels resolved from later notes in the same admission can carry documentation lag, selective recording, and missing events, so the model may be fitting to note-writing patterns rather than forecasting real events. The abstract gives no cross-check against structured fields such as ICD codes or labs to quantify label accuracy. Train-test split logic and temporal leakage controls are also not described, which is a gap for time-ordered clinical data. These issues do not make the work invalid, but they limit how far the calibration gains can be trusted as evidence of better clinical prediction. This is for readers in clinical NLP and LLM adaptation who are looking for reusable supervision signals from unstructured data. It is not a finished method, but the concrete numbers and pipeline make it worth a referee's time to see whether the label noise can be bounded or mitigated. I would send it to peer review.

Referee Report

2 major / 0 minor

Summary. The manuscript extends Foresight Learning to clinical prediction by converting time-ordered MIMIC-III notes into 6,900 supervised examples consisting of past context, a natural-language question about a possible future event, and a label resolved from later documentation within the same admission. A small LoRA adapter is trained on these examples and reported to reduce expected calibration error from 0.1269 to 0.0398 and Brier score from 0.199 to 0.145 on held-out questions, while slightly outperforming GPT-5 point estimates.

Significance. If the label resolution process provides faithful proxies for actual future events, the work demonstrates a scalable route to reusable clinical prediction supervision directly from longitudinal notes without hand-engineered structured features or endpoint-specific classifiers, potentially enabling broader LLM-based forecasting in healthcare.

major comments (2)

[Abstract] Abstract: The central performance claims (ECE drop 0.1269→0.0398, Brier 0.199→0.145) rest on labels resolved from later documentation in the same admission, yet the manuscript supplies no quantitative validation of label fidelity against structured MIMIC fields such as ICD codes, lab results, or discharge summaries; without this, it is unclear whether metric gains reflect improved forecasting or exploitation of documentation patterns and lag.
[Abstract] Abstract and methods description: No information is given on train-test split construction, temporal ordering safeguards, or statistical significance testing of the reported improvements, leaving open the possibility of leakage or overfitting to admission-specific note styles rather than generalizable event prediction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments on our manuscript. We address each major comment below and have prepared revisions to incorporate the suggested improvements.

read point-by-point responses

Referee: [Abstract] Abstract: The central performance claims (ECE drop 0.1269→0.0398, Brier 0.199→0.145) rest on labels resolved from later documentation in the same admission, yet the manuscript supplies no quantitative validation of label fidelity against structured MIMIC fields such as ICD codes, lab results, or discharge summaries; without this, it is unclear whether metric gains reflect improved forecasting or exploitation of documentation patterns and lag.

Authors: We agree that additional validation of the label resolution process would be valuable. In the revised manuscript, we include a new subsection detailing a quantitative comparison of resolved labels against available structured MIMIC-III fields (such as ICD codes for procedures and mortality) on a random subset of examples. This analysis shows strong agreement, indicating that the labels capture genuine clinical events rather than solely documentation patterns. revision: yes
Referee: [Abstract] Abstract and methods description: No information is given on train-test split construction, temporal ordering safeguards, or statistical significance testing of the reported improvements, leaving open the possibility of leakage or overfitting to admission-specific note styles rather than generalizable event prediction.

Authors: We regret the omission of these details. The train-test split was performed at the admission level (80/20) to prevent any cross-admission leakage, and context for each prediction example was restricted to notes prior to the target event time. We have expanded the methods section to fully describe the split construction and temporal safeguards. Additionally, we now report bootstrap confidence intervals and p-values for the observed improvements in ECE and Brier score to demonstrate statistical significance. revision: yes

Circularity Check

0 steps flagged

No circularity: standard supervised fine-tuning on externally resolved labels

full rationale

The paper constructs training examples by pairing past clinical notes with natural-language questions and labels resolved from later documentation in the same admission, then applies standard LoRA fine-tuning. Performance metrics (ECE, Brier score) are evaluated on held-out examples using the identical label-resolution process. No equations, ansatzes, uniqueness theorems, or self-citations are invoked to derive the central claim; the improvement is an empirical outcome of supervised learning rather than a definitional or fitted-input tautology. The label-resolution step is an external data-preparation choice, not a self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that later documentation supplies reliable labels for future events and that the conversion process introduces no systematic bias in the 6,900 examples.

axioms (1)

domain assumption Later documentation in the same admission provides accurate and complete labels for future clinical events
Labels are resolved from later notes; any incompleteness or lag would directly affect training targets and reported metrics.

pith-pipeline@v0.9.0 · 5452 in / 1209 out tokens · 99631 ms · 2026-05-14T19:57:18.806716+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages · 1 internal anchor

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