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arxiv: 2604.18570 · v2 · submitted 2026-04-20 · 💻 cs.LG · cs.AI· cs.CL

A multimodal and temporal foundation model for virtual patient representations at healthcare system scale

Andrew Zhang , Tong Ding , Sophia J. Wagner , Caiwei Tian , Ming Y. Lu , Rowland Pettit , Joshua E. Lewis , Alexandre Misrahi
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Dandan Mo Long Phi Le Faisal Mahmood
This is my paper

Pith reviewed 2026-05-10 04:57 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL
keywords multimodal foundation modelselectronic health recordsclinical forecastingpatient representationstemporal datamedical AIprognostic modelsvirtual patients
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The pith

A multimodal foundation model unifies full patient records into embeddings for forecasting hundreds of clinical outcomes.

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

The paper establishes that a single model can integrate 28 medical modalities from 25 billion records into virtual patient representations. These representations are then shown to support accurate predictions on 322 tasks, including disease onset up to five years ahead. This matters because it suggests that the complete temporal and multimodal context of a patient's care can be made available for computational analysis without manual intervention. If correct, it lays groundwork for systems that reason over entire care journeys rather than fragmented data pieces.

Core claim

Apollo is a multimodal temporal foundation model that learns a unified representation space from over 100 thousand medical events, images, and clinical text across 7.2 million patients. The resulting virtual patient representations enable generalized clinical forecasting on 95 new disease onset tasks up to five years ahead, 78 disease progression tasks, 59 treatment response tasks, 17 adverse event risk tasks, and 12 hospital operations tasks, while also supporting 61 semantic retrieval tasks and showing alignment with interpretable biomarkers.

What carries the argument

The Apollo model itself, which acts as a compressor turning sequences of structured events, unstructured text, and images into unified virtual patient embeddings that capture the full care journey.

If this is right

  • The embeddings allow prediction of new disease onset risk up to five years in advance across 95 tasks.
  • Disease progression forecasting is possible in 78 tasks.
  • Treatment response prediction covers 59 tasks and adverse event risks cover 17 tasks.
  • Hospital operations endpoints are addressed in 12 tasks and semantic search in 61 retrieval tasks.
  • Feature attribution confirms that predictions rely on clinically relevant multimodal signals.

Where Pith is reading between the lines

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

  • This could enable searching for similar patient trajectories using text or image queries to guide care in complex cases.
  • Performance on external datasets from other hospitals would test if the representations are truly general or system-specific.
  • Integration into existing record systems could provide automated risk alerts based on the full record history.
  • The model might support cross-modal queries that link images directly to future outcome probabilities.

Load-bearing premise

The test set patients and their data patterns are representative of future patients, and the model captures generalizable medical signals instead of hospital-specific documentation biases.

What would settle it

If the predictive performance on the 95 disease onset tasks drops significantly when applied to patient data from a different hospital system, this would falsify the claim of generalized representations.

Figures

Figures reproduced from arXiv: 2604.18570 by Alexandre Misrahi, Andrew Zhang, Caiwei Tian, Dandan Mo, Faisal Mahmood, Joshua E. Lewis, Long Phi Le, Ming Y. Lu, Rowland Pettit, Sophia J. Wagner, Tong Ding.

Figure 1
Figure 1. Figure 1: Overview of MGB-7M and APOLLO. (a) Overview of the pretraining dataset MGB-7M curated from 17 hospitals in one large-scale health care system consisting of 7.15 million patients. (b-e) Detailed distribution of MGB-7M including (b) LOINC code distribution of measurements, (c) distribution of diagnostic reports across medical domains, (d) medications grouped by ATC classification, and (e) ICD10 codes grouped… view at source ↗
Figure 2
Figure 2. Figure 2: APOLLO generates an atlas of medical concepts. (a) Uniform manifold approximation and projection (UMAP) of the 103,940 discrete tokens that occur more than 100 times shows that APOLLO learns the underlying semantics of the discrete concepts. The emerging atlas of medical concepts exhibits meaningful spatial relationships both within modalities, as seen for (b) diagnosis codes and (c) for medications, as we… view at source ↗
Figure 3
Figure 3. Figure 3: Evaluation of APOLLO’s patient embeddings. (a) Uniform Manifold Approximation and Projection (UMAP) visual￾ization of 100 thousand randomly sampled patient embeddings from the data partition for downstream evaluation, labeled by age. Local neighborhoods reveal clustering of patients with similar clinical phenotypes. (b) Patient trajectories of 10 random patients before their diagnosis of Schizophrenia show… view at source ↗
Figure 4
Figure 4. Figure 4: APOLLO enhances patient retrieval. (a) 61 patient retrieval tasks curated from combinations of ICD10 diagnosis codes and medications, assessed with accuracy among the five closest (Acc@5) embedded patients compared to retrieval of the latest progress note embedding, (b) qualitative evaluation of the closest embedded patient to a patient for kidney transplant maintenance. (c) Text-based retrieval on the exa… view at source ↗
Figure 5
Figure 5. Figure 5: APOLLO yields interpretable biomarkers at both the local and global level. (a–c) Local analysis. We plotted the model’s predicted 3-year risk for three example patients: (a) chronic kidney disease, (b) lung cancer, and (c) heart failure, as a function of age; markers indicate encounter times. At each prominent increase in risk, we performed a leave-one-token-out (LOTO) sensitivity analysis over events betw… view at source ↗
read the original abstract

Modern medicine generates vast multimodal data across siloed systems, yet no existing model integrates the full breadth and temporal depth of the clinical record into a unified patient representation. We introduce Apollo, a multimodal temporal foundation model trained and evaluated on over three decades of longitudinal hospital records from a major US hospital system, composed of 25 billion records from 7.2 million patients, representing 28 distinct medical modalities and 12 major medical specialties. Apollo learns a unified representation space integrating over 100 thousand unique medical events in our clinical vocabulary as well as images and clinical text. This "atlas of medical concepts" forms a computational substrate for modeling entire patient care journeys comprised of sequences of structured and unstructured events, which are compressed by Apollo into virtual patient representations. To assess the potential of these whole-patient representations, we created 322 prognosis and retrieval tasks from a held-out test set of 1.4 million patients. We demonstrate the generalized clinical forecasting potential of Apollo embeddings, including predicting new disease onset risk up to five years in advance (95 tasks), disease progression (78 tasks), treatment response (59 tasks), risk of treatment-related adverse events (17 tasks), and hospital operations endpoints (12 tasks). Using feature attribution techniques, we show that model predictions align with clinically-interpretable multimodal biomarkers. We evaluate semantic similarity search on 61 retrieval tasks, and moreover demonstrate the potential of Apollo as a multimodal medical search engine using text and image queries. Together, these modeling capabilities establish the foundation for computable medicine, where the full context of patient care becomes accessible to computational reasoning.

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.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces Apollo, a multimodal temporal foundation model trained on 25 billion records from 7.2 million patients spanning 28 medical modalities and 12 specialties from a single US hospital system. It constructs unified representations integrating over 100k medical events, images, and clinical text into 'virtual patient representations' that compress entire care journeys. These representations are assessed via 322 prognosis and retrieval tasks on a temporally held-out cohort of 1.4 million patients, with claims of forecasting new disease onset up to five years ahead (95 tasks), disease progression (78 tasks), treatment response (59 tasks), adverse events (17 tasks), hospital operations (12 tasks), plus semantic similarity search on 61 tasks and multimodal query capabilities.

Significance. If the results hold, the work has substantial significance due to the unprecedented scale of the integrated dataset and the breadth of evaluated tasks, which together position the embeddings as a potential substrate for computable medicine. The temporal hold-out design and use of feature attribution for interpretability are positive elements. The large patient cohort and multimodal coverage represent a clear strength that could enable downstream applications if generalizability is established.

major comments (3)
  1. [Abstract] Abstract and evaluation description: the central claim of 'generalized clinical forecasting potential' across 322 tasks is unsupported because no quantitative performance metrics (AUC, F1, calibration, or statistical tests), baseline comparisons, or ablation results are reported for any task, preventing assessment of whether the embeddings outperform trivial or existing methods.
  2. [Data and Evaluation] Data section: all training (7.2M patients) and evaluation (1.4M held-out patients) occurs within a single hospital system's records. This single-center limitation means the 95 onset, 78 progression, and other tasks test only intra-site patterns (coding, documentation, demographics), directly threatening the headline claims of generalization 'at healthcare system scale' and transportable clinical signals without external validation.
  3. [Methods] Methods: no architecture details, training objective, loss functions, optimization procedure, or hyperparameter choices are provided for the foundation model that produces the embeddings used in all 322 tasks, rendering the central modeling contribution impossible to reproduce or critique.
minor comments (2)
  1. [Abstract] The phrase 'virtual patient representations' is used repeatedly without a formal definition or equation distinguishing it from standard sequence embeddings.
  2. [Abstract] The abstract lists task counts (95, 78, 59, etc.) but does not indicate how tasks were constructed or balanced, which affects interpretation of the forecasting results.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for their constructive review and for highlighting areas where the manuscript can be strengthened. We address each major comment in turn below, with plans for targeted revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract and evaluation description: the central claim of 'generalized clinical forecasting potential' across 322 tasks is unsupported because no quantitative performance metrics (AUC, F1, calibration, or statistical tests), baseline comparisons, or ablation results are reported for any task, preventing assessment of whether the embeddings outperform trivial or existing methods.

    Authors: We agree that the abstract, as a high-level summary, does not contain specific numerical results. The current manuscript defines the 322 tasks and describes the overall evaluation framework but does not report the requested quantitative metrics, baseline comparisons, or ablations. In the revised version we will add a concise Results subsection (or expanded evaluation paragraph) that reports representative AUC-ROC, F1, calibration, and statistical test values across task categories, includes comparisons to standard baselines (e.g., logistic regression on structured features), and presents modality and temporal ablations. We will also update the abstract to include one or two key quantitative highlights so readers can immediately gauge performance. revision: yes

  2. Referee: [Data and Evaluation] Data section: all training (7.2M patients) and evaluation (1.4M held-out patients) occurs within a single hospital system's records. This single-center limitation means the 95 onset, 78 progression, and other tasks test only intra-site patterns (coding, documentation, demographics), directly threatening the headline claims of generalization 'at healthcare system scale' and transportable clinical signals without external validation.

    Authors: We acknowledge the single-center constraint as a genuine limitation. Although the temporal hold-out design tests forecasting on future patients within the same system and the cohort size is large, the evaluation cannot speak to transportability across institutions with differing coding practices or populations. We cannot obtain external datasets for additional validation at this time. In revision we will insert an explicit Limitations section that states this restriction, discusses potential site-specific biases, and outlines the need for future multi-center studies. We will also moderate language in the abstract, introduction, and title to clarify that claims refer to scale within one large healthcare system rather than universal generalizability. revision: partial

  3. Referee: [Methods] Methods: no architecture details, training objective, loss functions, optimization procedure, or hyperparameter choices are provided for the foundation model that produces the embeddings used in all 322 tasks, rendering the central modeling contribution impossible to reproduce or critique.

    Authors: The referee correctly identifies that the current Methods section lacks sufficient technical detail for reproducibility. We will expand it substantially to include: (1) a precise description of the multimodal transformer architecture with temporal encodings, (2) the composite training objective (masked event modeling plus cross-modal contrastive loss), (3) the exact loss functions and weighting, (4) the optimizer, learning-rate schedule, and batching strategy, and (5) a table of all key hyperparameters. A high-level pseudocode block and an architecture diagram will also be added. These additions will allow readers to understand and, where data access permits, reproduce the embedding generation process. revision: yes

standing simulated objections not resolved
  • The single-center data constraint and consequent inability to supply external validation experiments with data from other healthcare systems.

Circularity Check

0 steps flagged

No significant circularity; evaluations independent of training objective

full rationale

The paper trains Apollo on 7.2M patients' multimodal longitudinal records to learn unified embeddings, then evaluates those embeddings on a temporally held-out cohort of 1.4M patients using 322 separately defined downstream tasks (95 disease-onset, 78 progression, 59 treatment-response, etc.). These forecasting and retrieval tasks are not quantities defined by the training objective itself, nor do they reduce to fitted parameters or self-citations by construction. No equations, self-definitional steps, or load-bearing self-citations appear in the provided text; the derivation chain is self-contained against external held-out benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the quality and representativeness of the proprietary longitudinal dataset plus standard assumptions that deep learning embeddings trained on observational records will generalize to future patients and tasks.

axioms (2)
  • domain assumption The 25 billion records accurately capture patient states, events, and outcomes without systematic documentation bias
    Invoked implicitly when claiming the embeddings form a reliable computational substrate for forecasting.
  • domain assumption The held-out 1.4 million patients are statistically exchangeable with future patients at the same institution
    Required for the 322-task evaluation to support claims of generalized forecasting potential.
invented entities (1)
  • virtual patient representations no independent evidence
    purpose: Compressed unified embedding of a patient's full multimodal temporal record
    The embeddings are the model's output; no external falsifiable signature (e.g., predicted biomarker) is provided beyond internal task performance.

pith-pipeline@v0.9.0 · 5623 in / 1456 out tokens · 42530 ms · 2026-05-10T04:57:35.491714+00:00 · methodology

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Forward citations

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