LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes

Carl Yang; Jiaying Lu; Mingchen Shao; Yuzhang Xie

REVIEW 4 major objections 3 minor 1 cited by

Few-shot LLM prompting extracts ADRD phenotypes from clinical notes that discriminate cohorts and stage disease better than NER or dictionary baselines.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.5

2026-07-14 21:42 UTC pith:LMW2CR2N

load-bearing objection Abstract-only LLM phenotyping for ADRD: modest ARI/NMI gains over NER/dictionary baselines, useful subfield extension, but evaluation is a weak proxy and full methods are missing. the 4 major comments →

arxiv 2603.13673 v2 pith:LMW2CR2N submitted 2026-03-14 cs.AI cs.LG

LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes

Mingchen Shao , Yuzhang Xie , Carl Yang , Jiaying Lu This is my paper

classification cs.AI cs.LG

keywords Alzheimer's DiseaseRelated Dementiasphenotype extractionclinical noteslarge language modelsfew-shot promptingdisease stagingEHR

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper argues that large language models can pull clinically meaningful Alzheimer's Disease and Related Dementias (ADRD) phenotypes out of free-text clinical notes when guided by expert-defined phenotype lists and few-shot prompts. Because those phenotypes normally hide in unstructured narrative rather than structured fields, automatic extraction has been hard; the authors show that LLM-MINE recovers signals that differ statistically across patient cohorts (memory impairment is the strongest separator) and that support unsupervised disease-stage clustering. The best setting combines both expert lists under few-shot prompting and beats biomedical named-entity recognition and dictionary matching on clustering agreement metrics. If the approach holds, EHR systems could surface early ADRD markers and stage disease without relying solely on sparse structured codes.

Core claim

Few-shot prompting of an LLM with two expert-defined ADRD phenotype lists extracts phenotypes from clinical notes that show statistically significant cohort differences (memory impairment strongest) and produce the best unsupervised disease-staging clusters (ARI=0.290, NMI=0.232), substantially outperforming biomedical NER and dictionary-based baselines.

What carries the argument

LLM-MINE: a few-shot prompting pipeline that supplies combined expert-defined ADRD phenotype lists to a large language model so it can mine those phenotypes from free-text clinical notes for subsequent chi-square cohort tests and unsupervised clustering.

Load-bearing premise

That clustering agreement with cohort labels plus chi-square phenotype differences is enough to prove the extracted phenotypes are clinically valid when the same expert lists used to prompt the model also define the evaluation vocabulary.

What would settle it

A blinded clinician review of a held-out sample of notes that scores whether LLM-extracted phenotypes match expert chart review, or a prospective test showing that the same phenotypes improve actual clinical staging accuracy beyond the reported ARI/NMI figures.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

ADRD phenotypes that are currently buried in notes can be recovered at scale without manual chart review.
Memory impairment emerges as the strongest automatic cohort discriminator among the mined phenotypes.
Few-shot prompting with combined expert lists is preferred over NER or pure dictionary methods for this extraction task.
Unsupervised clusters derived from the mined phenotypes can serve as a proxy signal for disease staging in EHR cohorts.

Where Pith is reading between the lines

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

The same few-shot + expert-list pattern could be tried on other neurodegenerative or psychiatric conditions whose key phenotypes live mainly in free text.
If the extracted phenotypes are reliable, they become candidate features for early-risk models that currently under-use unstructured notes.
A natural next measurement is inter-rater reliability between LLM output and independent clinicians on a shared note set.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

Abstract-only LLM phenotyping for ADRD: modest ARI/NMI gains over NER/dictionary baselines, useful subfield extension, but evaluation is a weak proxy and full methods are missing.

read the letter

This is an abstract-only look at LLM-MINE. The one thing to know is that they take two expert ADRD phenotype lists, few-shot prompt an LLM on clinical notes, and report that the resulting features separate cohorts by chi-square (memory impairment strongest) and give the best unsupervised staging clusters (ARI 0.290, NMI 0.232) versus biomedical NER and dictionary baselines. That is a concrete empirical claim, not a restatement of a prior number.

What is actually new is narrow: applying the now-standard few-shot LLM extraction pattern to ADRD with those two lists and checking both cohort differences and unsupervised staging. They do the sensible comparison to NER and dictionary methods and show the combined lists win. For clinical NLP and dementia informatics that is useful work—chart review is expensive and early staging signals matter—even if it does not reorganize the field.

The soft spots are real and mostly structural. Absolute ARI/NMI numbers are modest; calling the gains “substantial” is hard to audit without the full baseline tables. More importantly, the evaluation design is a weak proxy for extraction validity. The phenotype vocabulary is expert-supplied and injected into the prompts, so the feature space is pre-aligned with the constructs that almost certainly define the cohorts. Clustering agreement with those labels can confirm the lists’ relevance more than the LLM’s fidelity or any independent signal mined from the notes. Without span-level F1 against human annotation, prompt ablations that remove or scramble the lists, model identity, decoding settings, or cohort definitions, we cannot tell how much is extraction skill versus prompted concept retrieval. Circularity is moderate, not fatal, but it is load-bearing for the central claim.

Who this is for: people already working on clinical phenotyping or ADRD informatics who want a practical LLM baseline. A serious referee should see the full paper if the methods, prompts, and data access are solid; the abstract alone does not clear the bar for high confidence. I would not cite it yet and would not bring the abstract to reading group, but I would accept it for peer review rather than desk-reject—revision can fix the evaluation gaps. Worth a look once the full text and artifacts appear.

Referee Report

4 major / 3 minor

Summary. The manuscript proposes LLM-MINE, a large-language-model framework that uses few-shot prompting over two expert-defined ADRD phenotype lists to extract Alzheimer's Disease and Related Dementias phenotypes from unstructured clinical notes. Evaluation, as stated in the abstract, consists of (i) chi-square tests of phenotype differences across cohorts (memory impairment reported as the strongest discriminator) and (ii) unsupervised disease-staging clusters, where few-shot prompting with the combined lists achieves ARI=0.290 and NMI=0.232 and is claimed to substantially outperform biomedical NER and dictionary-based baselines. The abstract concludes that LLM-based phenotype extraction is a promising tool for discovering clinically meaningful ADRD signals from notes.

Significance. If the extraction is faithful and the staging utility survives stronger validation, the work would be practically useful: ADRD phenotypes are often locked in free-text notes, and scalable LLM extraction could support early detection and staging research without large annotation campaigns. Reporting concrete ARI/NMI figures and comparing against standard biomedical NER and dictionary baselines is a strength relative to purely qualitative LLM demos. Significance is currently constrained by the indirect evaluation design and by the modest absolute clustering scores stated in the abstract; those limits must be resolved before the clinical claim can be treated as established.

major comments (4)

Abstract, evaluation design: the phenotype vocabulary is supplied by expert-defined ADRD lists that also structure the few-shot prompts; evaluation then tests whether those same phenotype signals separate cohorts and stage disease via unsupervised ARI/NMI against cohort labels. This is a load-bearing circularity risk for the central claim of 'accurate extraction' and 'clinically meaningful' signals: agreement may largely confirm the lists' relevance rather than extraction fidelity. Direct span-level precision/recall/F1 against human annotation, and/or ablations that remove or scramble the expert lists, are needed to secure the claim.
Abstract, reported ARI=0.290 and NMI=0.232: these absolute unsupervised agreement scores are modest. Calling them 'substantially outperforming' biomedical NER and dictionary baselines is not auditable from the abstract because baseline ARI/NMI (and any confidence intervals or significance tests on the deltas) are not reported. The outperformance claim is load-bearing for the paper's contribution and must be quantified with the baseline numbers side-by-side.
Abstract: no direct extraction-accuracy metric is mentioned. Chi-square cohort differences and unsupervised clustering agreement against cohort labels are weak proxies for extraction validity when the feature space is pre-aligned with expert ADRD constructs that almost certainly define the cohorts. Without human-annotated gold spans (or an equivalent independent clinical endpoint), the claim that the LLM is mining phenotypes rather than retrieving prompted concepts remains under-supported.
Abstract: free parameters that determine the result—LLM identity and decoding settings, the exact contents of the two expert phenotype lists, and the few-shot example set/prompt template—are not specified. Reproducibility and external audit of the central few-shot result require these to be fixed and reported (or released) in the full manuscript.

minor comments (3)

Abstract: name the LLM family/version and approximate cohort sizes (N per cohort) so readers can gauge scale and model class.
Abstract: replace the qualitative phrase 'substantially outperforming' with numeric baseline ARI/NMI (or deltas) once those are available.
Abstract: clarify how 'cohorts' and disease stages are defined (clinical diagnosis codes, cognitive scores, expert staging, etc.), since those labels are the sole external structure used in evaluation.

Circularity Check

1 steps flagged

Expert ADRD phenotype lists define both the LLM extraction targets and the evaluation feature space, creating partial circularity in the clinical-validity claim.

specific steps

other [Abstract (methods/evaluation design)]
"Using two expert-defined phenotype lists, we evaluate the extracted phenotypes by examining their statistical significance across cohorts and their utility for unsupervised disease staging. Chi-square analyses confirm statistically significant phenotype differences across cohorts, with memory impairment being the strongest discriminator. Few-shot prompting with the combined phenotype lists achieves the best clustering performance (ARI=0.290, NMI=0.232)"

The expert ADRD phenotype lists are both the extraction targets placed in the LLM prompts and the sole feature vocabulary for chi-square and clustering evaluation against ADRD-related cohorts. Success therefore partly measures whether the pre-supplied disease concepts separate the cohorts (expected if extraction is non-random) rather than solely whether the LLM mines independent clinical signal. Not forced equality of prediction to input, but the feature space is aligned with the evaluation constructs by construction of the lists.

full rationale

Only the abstract is available. The method injects two expert-defined ADRD phenotype lists into few-shot prompts, extracts those phenotypes from notes, then evaluates via chi-square cohort differences and unsupervised clustering (ARI/NMI) against cohort/stage labels. The lists therefore pre-structure the feature space around the same disease constructs that almost certainly define the cohorts, so agreement partly reconfirms list relevance rather than purely independent discovery. This is not full circularity by construction: cohort labels and staging structure are external, dictionary/NER baselines using related vocabularies underperform, and extraction fidelity can still fail. No self-citations, uniqueness theorems, fitted parameters renamed as predictions, or equation-level reductions appear in the abstract. Score 4 reflects partial circular content in the evaluation design without the central claim collapsing to its inputs.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 0 invented entities

Abstract-only audit. The method rests on expert-supplied phenotype vocabularies, few-shot prompting of an unspecified LLM, and the assumption that free-text notes contain extractable ADRD phenotypes whose presence/absence supports cohort separation and unsupervised staging. No free numeric constants are fitted in the abstract; the main 'parameters' are design choices (lists, prompt regime, clustering setup). No new physical entities are invented.

free parameters (3)

expert phenotype list contents (two lists)
Lists define the extraction vocabulary and thus the feature space for chi-square and clustering; composition is expert-chosen, not derived from data in the abstract.
few-shot example set and prompt template
Few-shot regime is reported as best-performing; example selection and wording are free design choices that affect extraction quality.
LLM identity / decoding settings
Model and generation hyperparameters are not stated in the abstract but necessarily condition all extractions.

axioms (4)

domain assumption Unstructured clinical notes contain ADRD phenotypes that LLMs can extract with useful accuracy under few-shot prompting.
Core premise of LLM-MINE stated in the abstract problem setup.
domain assumption Expert-defined phenotype lists are an adequate and unbiased vocabulary for ADRD staging signals.
Evaluation uses two expert lists as the extraction targets; validity of staging depends on list coverage and bias.
ad hoc to paper Unsupervised clustering agreement (ARI/NMI) against cohorts is a valid proxy for disease-staging utility.
Abstract treats ARI/NMI as primary utility evidence without clinical outcome linkage stated.
domain assumption Chi-square tests of phenotype prevalence across cohorts indicate clinically meaningful discriminators.
Statistical significance is used as confirmation of phenotype differences (e.g., memory impairment).

pith-pipeline@v1.1.0-grok45 · 6080 in / 2656 out tokens · 28381 ms · 2026-07-14T21:42:59.183162+00:00 · methodology

0 comments

read the original abstract

Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection and disease staging. However, this information is usually embedded in unstructured textual data rather than tabular data, making it difficult to be extracted accurately. We therefore propose LLM-MINE, a Large Language Model-based phenotype mining framework for automatic extraction of ADRD phenotypes from clinical notes. Using two expert-defined phenotype lists, we evaluate the extracted phenotypes by examining their statistical significance across cohorts and their utility for unsupervised disease staging. Chi-square analyses confirm statistically significant phenotype differences across cohorts, with memory impairment being the strongest discriminator. Few-shot prompting with the combined phenotype lists achieves the best clustering performance (ARI=0.290, NMI=0.232), substantially outperforming biomedical NER and dictionary-based baselines. Our results demonstrate that LLM-based phenotype extraction is a promising tool for discovering clinically meaningful ADRD signals from unstructured notes.

discussion (0)

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