Optimising antibiotic switching via forecasting of patient physiology

arXiv:2603.08242 · cs.LG, stat.AP · Submitted 2026-03-09 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Optimising antibiotic switching via forecasting of patient physiology".

Jane: The gist: Forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship by predicting switch-readiness through probabilistic modeling rather than learning from historical actions > 1.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So we're looking at this paper, "Optimising antibiotic switching via forecasting of patient physiology". It’s about using neural processes to predict when a patient is ready to switch from an IV antibiotic to an oral one based on their vital signs.

Jane: Right, so the big idea here seems to be moving away from just looking at past decisions and instead using probabilistic modeling of how the patient's physiology might change in the near future. It’s about predicting switch-readiness through that forecasting approach, which is a big step for decision support in antibiotic stewardship.

Lu: Exactly, it proposes training a neural process model to forecast five key vital signs over a twelve-hour window given the history of measurements, minimizing the negative log-likelihood of those target observations under a Gaussian predictive distribution #pg2 <ref:2603.08242#pg3>. That's the core mechanism they are proposing for modeling these trajectories.

Meng: From an engineering standpoint, I’m interested in how robust this forecasting model is when we actually put it into a production setting against real EHR data, not just simulated sets. What kind of performance metrics are they using to ensure this works reliably?

Lalam: The paper shows they validated the neural process model on two distinct datasets, MIMIC-IV v3 point 1 and UCLH #pg3 <ref:2603.08242#pg1>. They found that the neural process model showed significant improvement over both baseline models on all measurements except for SpO2, with mean improvements of two point two five percent for UCLH and two point nine nine percent for MIMIC #pg4.

Tom: That’s a solid start because it shows the forecasting aspect itself is better than what we usually see in those baseline approaches, right? But how does that improve the actual classification part—determining if they are switch-ready?

Jane: The paper defines a patient as "switch-ready" if all five of those forecasted vital signs remain within clinically normal ranges throughout that twelve-hour window #pg2 <ref:2603.08242#pg3>. They calculate the probability of this happening by computing the probability that all forecast measurements satisfy the clinical criteria, which factorises under the conditional independence assumption #pg4.

Lu: And they rank patients based on this switch-readiness probability to prioritize them for clinical review, and they even show some specific probabilities in Figure one like a patient having an "eighty-eight percent" chance of being ready for switch #pg2 <ref:2603.08242#pg2>. That ranking is what makes it useful for the clinicians who are overwhelmed with decisions.

Meng: That ranking sounds useful, but how does this system handle things when the clinical guidelines themselves change? If we update our thresholds because of new evidence, does this model need a full retraining cycle every time?

Tom: It’s designed to be adaptive without retraining; they state the design yields interpretable outputs and adapts to updated guidelines without retraining #pg1. That flexibility is important for real-world deployment because it avoids the delays you see when we just keep updating old, static decision support systems.

Title and authors: Jane: So, this system isn't just learning from past switches; it’s using the forecast to compare against the current clinical criteria at every point in time #pg1. That makes the output much more transparent for a clinician because they can inspect those specific forecasts to understand why a patient was flagged #pg1.

Lalam: The paper uses several evaluation metrics, including AUROC of zero point eight six seven on the UCLH data and a Precision@five score indicating it would select two point two times more relevant patients than random selection on the UCLH data #pg6. They also used a Brier score for accuracy and calibration of those probabilities #pg6.

Tom: Okay, so we have some solid performance numbers showing good discrimination, but I want to know about the practical constraints they mentioned, because that’s where most ML projects hit a wall. What are the limitations they highlight in this work?

Jane: They are quite clear about what's missing; one major limitation is the absence of prospective clinical evaluation to definitively assess whether these predictions actually translate into improved clinical care #pg12. They also noted that because the model relies on just five vital signs, it doesn’t incorporate other potentially relevant information, like inflammatory markers #pg12.

Lu: That limitation about not incorporating other markers is something I think we need to address next; maybe we could explore how to integrate those features into the neural process structure without losing interpretability #pg12. And they also mentioned that standard neural process models can exhibit poor calibration for switch-readiness classification because of the interaction between the training objective and outliers in EHR data #pg15.

Meng: Poor calibration is a real problem when you’re making high-stakes decisions; if the probabilities aren't accurate, clinicians won't trust the ranking system at all. How did they specifically address that calibration issue?

Tom: The authors addressed it by adding a classification head trained directly on switch-readiness labels to produce well-calibrated probabilities #pg15. They also stress that their approach of modeling patient physiology keeps the prescribing clinician front and center, which makes the decision support flexible enough to adapt to clinical evidence and changing guidelines #pg12.

Jane: So, while they acknowledge not incorporating everything, they show a way to mitigate the calibration issue by fine-tuning the model specifically for that classification task #pg15. This moves it from just a forecasting tool into a more reliable decision support system.

Lalam: Overall, the paper demonstrates that this specific approach—forecasting physiology rather than learning from historical actions—can effectively identify patients for IVOS review, regardless of the specific model used #pg12.

Tom: It does seem like they are setting a new direction by focusing on modeling the patient's trajectory and comparing it against guidelines, which is different from just training a classifier to look at past data points #pg1. That's a key distinction for me as an engineer.

Title and authors: Jane: It really is about providing that principled ranking based on predicted uncertainty rather than just a binary yes or no prediction, which gives the clinicians more information to make their own judgment #pg12.

Lu: And from a representation learning side, the use of the convolutional conditional neural process with a UNet architecture to model the predictive distribution p(X

τ,τ+∆: m Xτm) is quite sophisticated for capturing those time-series dependencies #pg4.

Meng: From an operational standpoint, if we were to deploy this at scale, we have to consider the data handling pipeline—they are using encounter triplets and splitting the data temporally to simulate realistic deployment and prevent look-ahead bias #pg13. How much computational overhead is this forecasting step adding compared to a simple logistic regression baseline?

Tom: The paper compares it against GBDT models for ranking performance, noting they perform similarly well across both datasets, with the neural process model providing a small improvement on MIMIC #pg30. That suggests the added complexity of the neural process isn't massively penalizing in terms of ranking quality.

Jane: It sounds like this paper gives us a practical framework that uses existing EHR data and focuses on generating actionable insights—a ranked list—without getting bogged down in just training a black box classifier on historical labels #pg2.

Lalam: The overall message of "Optimising antibiotic switching via forecasting of patient physiology" is that by modeling the physiological trajectory, we can create a machine learning system that prioritizes patients for review based on predicted future stability, which is what this work accomplishes #pg1.

Tom: So to wrap up, they’ve presented a method where you train a neural process to forecast vital signs over twelve hours and use those forecasts against clinical guidelines to rank patients for review #pg2 <ref:2603.08242#pg3>. It’s an approach that focuses on physiology over past actions, which I find very compelling from a modeling standpoint.

Jane: And the main thing is that it's designed to be a practical machine learning system that surfaces patients for expert review and discussion with the primary team, offering predictions based on their physiological trajectory #pg2.

Lu: We need to keep watching how this forecasting model evolves, especially if we can get them to incorporate those other clinical markers they mentioned, because that’s where the next big leap in accuracy is likely hiding #pg12.

Meng: For now, it seems like a sensible way to build a decision support tool using the data they have available and focusing on making the process transparent for the clinician #pg12.

Lalam: So, by optimizing antibiotic switching via forecasting of patient physiology, they offer a system that produces clinically sensible predictions that track the patient’s physiological trajectory and could support timely identification of switch-ready patients #pg8.

The paper's summary: Tom: So, to recap, this paper is about using a neural process model to predict how patient vital signs will behave over the next twelve hours and using that forecast to rank patients who are ready for an oral antibiotic switch.

Jane: Right, so instead of just looking at what happened last week when we decide on switching from IV to oral, this system tries to predict the physiological trajectory—the whole path—and see if that path stays within safe clinical limits.

Lu: The core idea is that we’re training a neural process model to forecast five specific vital signs over a twelve-hour window based on the patient's past measurements, minimizing the negative log-likelihood of those future observations under a Gaussian predictive distribution.

Meng: That sounds like heavy lifting for the training pipeline, but what does this actually mean in terms of clinical utility? Is this just another complicated black box that doesn't help us decide anything faster?

Tom: No, it’s about providing a principled ranking based on predicted uncertainty. They aren't just giving you a yes or no; they’re giving you a probability score for switch-readiness, which helps prioritize who gets looked at first.

Jane: That probabilistic output is key because it allows the clinician to inspect the actual forecast trajectories. It shows *why* a patient was flagged—they can see if the heart rate or blood pressure is trending toward a dangerous zone in twelve hours.

Lalam: They use this forecasting model to define switch-readiness as a probability calculation, factoring in the conditional independence assumption for those five vital signs. The system ranks patients by this probability to help teams prioritize their reviews.

Tom: And it’s important to note that they’ve designed the architecture so it can adapt to updated clinical guidelines without needing a full retraining cycle every time. That flexibility is something I always look for when shipping models.

Jane: It really changes how we think about decision support in this area because instead of relying on static rules, we're using dynamic physiological modeling that stays current with new evidence.

Lu: The model performs well on the validation sets, showing significant improvement over baseline models in forecasting performance across both MIMIC-IV and UCLH datasets, even though they noted a small gap for SpO2.

Meng: I'm looking at those metrics—AUROC of zero point eight six seven and Precision@five—that shows good discrimination on the switch-readiness part, but I wonder what happens when we try to incorporate more variables than just these five vitals?

Tom: That’s a fair question, Meng. The paper itself flags that the limitation is relying only on five vital signs, so incorporating other markers like inflammatory markers isn't in this version.

Jane: So, while the current system is very specific to those five measurements for stability assessment, it sets a foundation for how we can build even more comprehensive models down the road.

Lu: The authors did address that calibration issue by adding a classification head trained on switch-readiness labels to ensure those probabilities are well-calibrated, which makes the ranking output much more trustworthy.

Meng: Trustworthiness is everything for us in production, especially when it comes to clinical alerts—if the probability scores are off, the whole system loses its value immediately.

Tom: So, they’ve moved past just learning from historical actions and created a method that models patient physiology to predict future stability and rank patients accordingly.

Jane: It's a shift toward using predictive modeling of the body's actual state rather than just looking at what happened in the past for every decision.

Lu: This framework is different because it focuses on generating a distribution over predicted measurements, which gives clinicians a principled way to assess risk based on that uncertainty.

Tom: It’s a practical machine learning system designed to surface patients for expert review and discussion with the primary team based on their physiological trajectory.

Jane: And that’s where we should keep our eyes—how this approach of modeling physiology instead of just looking at past actions can fundamentally improve the accuracy and flexibility of antibiotic switching decisions.

The paper's improvements: Tom: So, we’re talking about what this paper suggests as improvements—essentially, how they'd take this concept and make it even better for real-world use at a hospital.

Jane: Right, so beyond just getting a ranking, the authors suggest using this forecasting method to create an adaptive system that doesn't need constant retraining when clinical guidelines change.

Lu: The architecture is designed to be flexible, meaning it can adapt to updated guidelines without requiring a full model retraining cycle every time they update their protocols.

Meng: That’s huge for deployment because continuous retraining is a nightmare for production pipelines; what are the specific mechanisms they use to keep it from breaking at scale?

Tom: They achieve this adaptability by keeping the core architecture focused on modeling patient physiology, which allows it to adjust to new clinical evidence without needing a complete overhaul of the model weights.

Jane: It means we can deploy a system that remains relevant for longer because it responds to new medical knowledge dynamically rather than being stuck with outdated rules.

Lalam: The paper also suggests using the probabilistic nature of the output—the distribution over measurements—to provide more nuanced decision support, moving beyond just a simple binary prediction.

Lu: They emphasize that this approach of modeling patient physiology keeps the prescribing clinician right in the center, making it a CDSS that can be flexible enough to adapt to evolving clinical evidence.

Meng: So it’s not just about better accuracy on a test set; it’s about building a system that is inherently more robust against the constant changes we see in medical practice.

Tom: Exactly. The improvement isn't just the AUROC score; it's the operational flexibility of a system that evolves with clinical reality, which I think makes this method much more compelling for real deployment.

Jane: It shifts the focus from building a static classifier to building a dynamic model that understands how a patient’s body is actually changing over time.

Lu: The implication is that forecasting physiological trajectories can serve as a principled foundation for decision support, independent of the specific learning algorithm used to make the final classification.

Meng: If we could take this principle—forecasting physiology over past actions—and apply it to other areas, like predicting patient response to new treatments, that would be a massive win for our deployment strategy.

Tom: So it’s about leveraging the physical process of patient change rather than just reacting to historical data points when building decision support tools.

Jane: It really shows how modeling the underlying biology can lead to a more intuitive and clinically sensible tool for doctors.

Lu: The authors are also very clear about what they didn't include, which is important because it sets the stage for future work on incorporating those other markers we talked about earlier.

Meng: So, while this version is limited to five vitals, the next step has to be integrating those other clinical markers into that forecasting structure.

Conclusion: Tom: So, to wrap up, this paper on "Optimising antibiotic switching via forecasting of patient physiology" shows that modeling how a patient’s body changes over time can provide a principled way to identify when they are ready for an oral antibiotic switch.

Jane: Exactly. The core message is that forecasting the physiological trajectory, rather than just looking at past actions, gives clinicians a more complete picture to support timely decisions.

Lu: It moves the discussion from reactive decision support based on history toward a proactive system that predicts future stability using probabilistic modeling of those vital signs.

Meng: I think what this does is give us a foundation for building CDSSs that are inherently more flexible, which is something we need when dealing with the constant churn of clinical evidence.

Lalam: The paper demonstrates that this forecasting approach can effectively identify switch-ready patients based on predicted future stability, and its formulation differs from previous approaches to integrating machine learning in IVOS decision support.

Tom: It’s a practical system designed to surface patients for expert review based on their predicted physiological path, which I think is a really solid way to handle this problem.

Jane: It's about giving the clinician transparent insights into the forecast trajectories themselves, so they can understand exactly why a patient was flagged.

Lu: The model’s ability to adapt without retraining when clinical guidelines change is what makes it more robust than many standard supervised learning approaches we see in this domain.

Meng: That adaptability is key for production; if a system doesn't break every time the hospital updates its protocol, it has real value in the long run.

Tom: It confirms that forecasting physiology can be an effective way to identify patients for IVOS review, regardless of whether you use a neural process or another model.

Jane: So while they did highlight limitations—like only using five vital signs and not incorporating other markers—the overall implication is a powerful new direction for decision support.

Lu: It’s about leveraging the physical process of patient change to create a more principled ranking system based on predicted uncertainty rather than just a simple binary prediction.

Meng: I’m still focused on that missing piece: how we get those other relevant clinical markers into the model without sacrificing the interpretability they built in.

Tom: Yeah, that seems like our next big engineering challenge if we want to take this concept and scale it up properly.

Jane: We have a lot of ground to cover with this kind of predictive modeling, especially when it comes to integrating those broader clinical markers into the forecasting structure.

Department of Computer Science, Centre for AI, University College London, UK · Institute of Health Informatics, University College London, UK

cs.LG, stat.AP

Submitted: 2026-03-09

Updated: 2026-03-09

Comments: 32 pages, 8 figures

Journal ref: Nature Communications volume 17, 2026

DOI: 10.1038/s41467-026-76715-w

Code: https://github.com/SAFEHR-data/ivos-model-public

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 88/100

The gist: The gist: Forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship by predicting switch-readiness through probabilistic modeling rather than

Key concepts

Neural Process (NP) Model: This is a machine learning technique used to model complex data by providing a probability distribution for predictions rather than just a single answer. It forecasts five key vital signs over the next 12 hours based on past measurements, giving clinicians not just one prediction but a range of likely outcomes, which helps quantify uncertainty.
Switch-readiness Prediction: A patient is deemed 'switch-ready' if the model predicts that all five monitored vital signs will stay within clinically normal ranges throughout the next 12 hours. This probability is calculated by assessing how likely it is for all predicted measurements to satisfy predefined clinical criteria, such as temperature or heart rate thresholds.
Forecasting vs. Learning from History: The system predicts future physiology based on current trends rather than learning from past decisions about switching. This approach avoids the delays and inconsistencies found in systems that rely solely on historical actions, allowing the model to provide a principled, forward-looking assessment of clinical stability.

Summary

The gist: Forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship by predicting switch-readiness through probabilistic modeling rather than learning from historical actions > 1.

Problem and Motivation

Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare costs > 1. One in five patients in England remain on IV antibiotics despite meeting switching criteria > 1. IVOS decisions depend on two linked components: clinical stability and oral suitability > 1. The first component targets clinical stability by assessing trends in vital signs like temperature, heart rate, blood pressure, respiratory rate, and oxygen saturation > 2. Clinically decision support systems (CDSSs) can improve switching rates but approaches learning from historical decisions reproduce delays and inconsistencies of routine practice > 2.

Proposed Methodology

The proposed system uses neural processes to model vital sign trajectories probabilistically to predict switch-readiness by comparing forecasts against clinical guidelines rather than learning from past actions > 2. The design yields interpretable outputs, adapts to updated guidelines without retraining, and preserves clinical judgement > 2. The architecture involves three main steps: A) Train forecasting model, B) Forecast and apply criteria, and C) Rank switchable patients > 2.

Forecasting: A Neural Process model is used to forecast five key vital signs over a 12-hour future window given the history of measurements > 2. The model is trained by minimizing the negative log-likelihood of the target observations under a Gaussian predictive distribution > 4.
Switch-readiness Prediction: Patients are defined as “switch-ready” if all forecast vital signs remain within clinically normal ranges throughout this window > 4. This probability is calculated by computing the probability that all forecast measurements satisfy the clinical criteria, which factorises under the conditional independence assumption > 4.
Ranking: Patients with a higher probability of switch-readiness are ranked higher in the list presented to clinicians for prioritization for review > 2.

Model Validation and Performance

The method was validated on two distinct EHR datasets: MIMIC-IV v3.1 and UCLH, representing significantly different populations and clinical practices > 2. The NP model showed a significant improvement over both baseline models on all measurements except SpO2, with mean improvements of 2.25% for UCLH and 2.99% for MIMIC > 4. The NP-tuned model achieved the highest scores across all metrics on the UCLH data > 6.

Forecasting Performance: The NP model provided a significant improvement over both baseline models on all measurements except SpO2, with mean improvements of 2.25% for UCLH and 2.99% for MIMIC > 4.
Switch-readiness Prediction: The NP-tuned model achieved an AUROC of 0.867 on the UCLH data, and a Precision@5 score indicating it would select 2.2× more relevant patients than random selection on the UCLH data > 6.
Model Comparison: Both GBDT and NP models performed similarly well in ranking performance across both datasets, with the NP model providing a small improvement on MIMIC > 7.

Interpretability and Clinical Utility

The system provides explainability through its outputs, as it produces forecasts of vital signs into the near future that clinicians already have a clear understanding of > 11. When a patient is flagged as switch-ready, clinicians can inspect the underlying vital sign forecasts to understand why > 2. A key structural advantage is that a single model serves both forecasting and classification tasks, which allows for flexible deployment where respiratory rate can be discounted if controlled by mechanical ventilation > 11. The system does not rely on ground-truth switching labels, capturing only the physiological improvement component of the decision > 12.

Limitations and Future Directions

The primary limitation of the work is the absence of prospective clinical evaluation to definitively assess whether predictions translate into improved clinical care > 12. The model’s reliance on five vital signs means that other potentially clinically relevant information, such as inflammatory markers, was not incorporated > 12. Furthermore, the standard NP model exhibits poor calibration for switch-readiness classification due to the interaction between training objective and outliers in EHR data > 15. The NP-tuned variant addresses this by adding a classification head trained directly on switch-readiness labels to produce well-calibrated probabilities > 15. The authors stress that their approach of modelling patient physiology keeps the prescribing clinician front and centre, resulting in a CDSS sufficiently flexible to adapt to clinical evidence and changing guidelines > 12.

Conclusion

The model produces clinically sensible predictions that track the patient’s physiological trajectory and could support timely identification of switch-ready patients > 8. The formulation itself differs from previously proposed approaches for the integration of ML for IVOS decision support, demonstrating that forecasting physiology can effectively identify patients for IVOS review, independent of the specific model used > 12. The NP and GBDT models perform similarly in terms of ranking performance, raising the question why use probabilistic forecasting at all > 12. The system is designed to be a practical machine learning system that surfaces patients for expert review and discussion with the primary team > 2. The model’s reliance on five vital signs means that other potentially clinically relevant information is not incorporated > 12. The simple set of five vitals were chosen because they are measured with sufficient regularity in the clinical contexts in which the proposed IVOS system could be applied > 12.

How it works

The process involves a two-step approach: first forecasting vital signs, and then applying criteria to estimate switch-readiness probabilities > 2. The NP model produces a distribution over predicted measurements, and this distribution is used to determine the probability a patient will meet the criteria in the forecast window > 2. In the NP-tuned variant, a classification head is added to aggregate the encoding from the final convolutional layer across time using a max-pooling operation, then applies a multi-layer perceptron to output a single switch-readiness probability > 4.

Key Components and Metrics

The system utilizes specific components for forecasting and classification:

Neural Process (NP) Model: A Convolutional Conditional Neural Process (ConvCNP) is employed, which uses a UNet architecture to model the predictive distribution p(X[τ,τ+∆]m Xτm) > 4. This model minimizes the negative log-likelihood of the target observations under this predictive distribution > 4.
Switch-readiness Criteria: Labels are defined based on whether all vital signs fall within clinically normal ranges over the forecast window [τ, τ + ∆] > 4. For instance, Temperature is set to be between 96.8 – 100.4 °F > 23.
Evaluation Metrics: The system uses several metrics to assess performance, including AUROC for discrimination, Average Precision (AP) for imbalanced tasks, Brier score for accuracy and calibration of probabilities, and Precision@5 which measures the proportion of truly switch-ready patients among the top 5 ranked patients > 28.

Data Handling

Data preprocessing involves several critical steps:

Data Sources: The study uses two databases: MIMIC-IV v3.1 and UCLH, representing different populations and clinical practices > 4.
Data Structure: Encounters are represented using a triplet representation Xm = (tm,i, cm,i, xm,i), where tm,i is the time of measurement and xm,i is the measured value > 13.
Task Construction: One task per day is constructed for each encounter with prediction time set to 9 am on a given day only if certain conditions are met > 14. A history of 48 hours is used for model inputs > 14.
Temporal Split: The data is split temporally to form the test set to simulate realistic deployment and prevent look-ahead bias > 13.

Model Baselines

Baseline models are developed for both forecasting and classification tasks using tabular models that take a fixed-length feature vector derived from the look-back window Xτm as input > 4.

Classification Baselines: These directly predict the switch-readiness label yτm from the feature vector using logistic regression and gradient boosted decision trees (GBDT) > 4.
Forecasting Baselines: The repeat baseline uses only the most recent measured value of each vital sign prior to the forecast window as the prediction for all future time points > 4. The GBDT forecasting baseline is more sophisticated, predicting all vital signs at arbitrary future times within the forecast window by augmenting a shared feature vector with one-hot encoding and time elapsed since prediction time > 4.

Sensitivity Analysis

The study examined sensitivity to clinical criteria by re-running MIMIC ranking experiments with less stringent thresholds, which increased the number of positive (switch-ready) days from 12.2% to 50.0% > 29. The relative performance of the model was preserved when measured with the looser criteria > 29. The NP and GBDT models performed similarly in terms of ranking performance across both datasets, with the NP model providing a small improvement on MIMIC > 30.

Contribution Statement

M.R. conceived and designed the study, developed the model architecture, code, and visualisations > 16. S.H., A.L., E.M and N.S. provided clinical guidance and validation > 16; V.L., S.H.

Improvements for AI systems

Improved decision support for IV-to-oral switch by forecasting patient physiology rather than past clinical actions. This approach is superior because it avoid[s] learning historical delays, side-steps the need to label switches from electronic health records (EHRs), and yields outputs clinicians can inspect directly (the forecast trajectories and their uncertainty).
Enhanced clinical prioritization via a probabilistic ranking system. The system ranks patients by predicted switch-readiness, helping teams prioritise reviews while preserving clinical judgement for factors outside routine vital signs data. This is achieved by using the NP model to produce a distribution over predicted measurements and applying clinical criteria to determine the probability they are ready to be switched.
Adaptive system flexibility without retraining. The architecture allows the system to adapt to updated guidelines without retraining, and it can be adapted in non-standard cases (e.g., discounting a variable that is clinically uninformative in context such as fever in patients on antipyretics). This contrasts with supervised models that reproduce the delays and inconsistencies of routine practice.
Increased model interpretability for clinicians. The system provides transparency by having forecasts of vital signs into the near future, quantities that clinicians already have a clear understanding of, and applies transparent clinical criteria to these forecasts to determine switch-readiness. This allows clinicians to inspect the underlying vital sign forecasts to understand why a patient was flagged.
Probabilistic output for nuanced decision-making. By using the NP model's distributional output, the system provides a principled ranking of patients for review, moving beyond simple binary predictions to offer a more complete assessment of risk based on predicted uncertainty.

Abstract

Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare costs, yet one in five patients in England remain on IV antibiotics despite meeting switching criteria. Clinical decision support systems can improve switching rates, but approaches that learn from historical decisions reproduce the delays and inconsistencies of routine practice. We propose using neural processes to model vital sign trajectories probabilistically, predicting switch-readiness by comparing forecasts against clinical guidelines rather than learning from past actions, and ranking patients to prioritise clinical review. The design yields interpretable outputs, adapts to updated guidelines without retraining, and preserves clinical judgement. Validated on MIMIC-IV (US intensive care, 6,333 encounters) and UCLH (a large urban academic UK hospital group, 10,584 encounters), the system selects 2.2-3.2 times more relevant patients than random. Our results demonstrate that forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship.