ARXIV:2602.14505 · HEALTHCARE AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

arXiv

COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods. Standard probabilistic model checkers operate on the full state space, which becomes infeasible…

METHOD

Full abstract

Safe and interpretable sequential decision-making is critical in healthcare, yet reinforcement learning (RL) policies for sepsis treatment optimization remain opaque and difficult to verify. Standard probabilistic model checkers operate on the full state space, which becomes infeasible for larger MDPs, and cannot explain why a learned policy makes particular decisions. COOL-MC wraps the model checker Storm but adds three key capabilities: it constructs only the reachable state space induced by a trained policy, yielding a smaller discrete-time Markov chain amenable to verification even when full-MDP analysis is intractable; it automatically labels states with clinically meaningful atomic propositions; and it integrates explainability methods with probabilistic computation tree logic (PCTL) queries to reveal which features drive decisions across treatment trajectories. We demonstrate COOL-MC's capabilities on the ICU-Sepsis MDP, a benchmark derived from approximately 17,000 sepsis patient records, which serves as a case study for applying COOL-MC to the formal analysis of sepsis treatment policies. Our analysis establishes hard bounds via full MDP verification, trains a safe RL policy that achieves optimal survival probability, and analyzes its behavior via PCTL verification and explainability on the induced DTMC. This reveals, for instance, that our trained policy relies predominantly on prior dosing history rather than the patient's evolving condition, a weakness that is invisible to standard evaluation but is exposed by COOL-MC's integration of formal verification and explainability. Our results illustrate how COOL-MC could serve as a tool for clinicians to investigate and debug sepsis treatment policies before deployment.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. We demonstrate COOL-MC's capabilities on the ICU-Sepsis MDP, a benchmark derived from approximately 17,000 sepsis patient records, which serves as a case study for…

WHY NOW

Healthcare AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainCOOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.14505 · HEALTHCARE AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

arXiv

COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Healthcare AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainCOOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.14505

Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback74 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Healthcare AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods. Standard probabilistic model checkers operate on the full state space, which becomes infeasible for larger MDPs, and cannot explain why a learned policy makes particular decisions.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Safe and interpretable sequential decision-making is critical in healthcare, yet reinforcement learning (RL) policies for sepsis treatment optimization remain opaque and difficult to verify. Standard probabilistic model checkers operate on the full state space, which becomes infeasible for larger MDPs, and cannot explain why a learned policy makes particular decisions.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We demonstrate COOL-MC's capabilities on the ICU-Sepsis MDP, a benchmark derived from approximately 17,000 sepsis patient records, which serves as a case study for applying COOL-MC to the formal analysis of sepsis treatment policies.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Healthcare AI moved forward this cycle; last verified April 2026. Public score 5.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Healthcare AI

Competitors

not indexed

Competitive landscape

COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.

Segment

Healthcare AI

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.14505 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(74)

Safe reinforcement learning via adaptive robust model predictive shielding

2026Hilde Gerold, Sergio Lucia

Towards trustworthy artificial intelligence for decision-making: A lifecycle perspective on knowledge- and data-driven artificial intelligence systems

2026Emiel Miedema, S. Waschull et al.

Exploring Time-Step Size in Reinforcement Learning for Sepsis Treatment

2025Yingchuan Sun, Shengpu Tang

Explainable AI For Early Detection Of Sepsis

2025A. Thakur, Shruti Dhumal

Shields for Safe Reinforcement Learning

2025Bettina Könighofer, Roderick Bloem et al.

Probabilistic Model Checking: Applications and Trends

2025Marta Z. Kwiatkowska, G. Norman et al.

Creating Personalized Medicine ML Models Using Distributed Learning via Ray

2025Chaeeun Kim, William Self et al.

PCTL Model Checking for Temporal RL Policy Safety Explanations

2025Dennis Gross, Helge Spieker

Offline Safe Reinforcement Learning for Sepsis Treatment: Tackling Variable-Length Episodes with Sparse Rewards

2025Rui Tu, Zhpeng Luo et al.

Deep reinforcement learning extracts the optimal sepsis treatment policy from treatment records

2024Yunho Choi, Songmi Oh et al.

1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization

2024Muqsit Azeem, Debraj Chakraborty et al.

Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies

2024Dennis Gross, Helge Spieker

Enhancing RL Safety with Counterfactual LLM Reasoning

2024Dennis Gross, Helge Spieker

Safe Multi-Agent Reinforcement Learning via Dynamic Shielding

2024Yunbo Qiu, Yue Jin et al.

ICU-Sepsis: A Benchmark MDP Built from Real Medical Data

2024Kartik Choudhary, Dhawal Gupta et al.

How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics Models

2024Unnseo Park, Venkatesh Sivaraman et al.

Reinforced Sequential Decision-Making for Sepsis Treatment: The PosNegDM Framework With Mortality Classifier and Transformer

2024Dipesh Tamboli, Jiayu Chen et al.

Clinical knowledge-guided deep reinforcement learning for sepsis antibiotic dosing recommendations

2024Yuan Wang, Anqi Liu et al.

Optimizing sepsis treatment strategies via a reinforcement learning model

2024Tianyi Zhang, Yimeng Qu et al.

Reinforcement Learning for Intelligent Healthcare Systems: A Review of Challenges, Applications, and Open Research Issues

2023A. Abdellatif, Naram Mhaisen et al.

Showing 20 of 74 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

Builds On ThisAgentifying Patient Dynamics within LLMs through Interacting with Clinical World Model

4.0

Builds On ThisTemporal Sepsis Modeling: a Fully Interpretable Relational Way

4.0

Builds On ThisMC-CPO: Mastery-Conditioned Constrained Policy Optimization

4.0

Commercially relevant

Higher ViabilityCOOL-MC: Verifying and Explaining RL Policies for Multi-bridge Network Maintenance

7.0

Higher ViabilityEnsuring Safety in Automated Mechanical Ventilation through Offline Reinforcement Learning and Digital Twin Verification

7.0

Higher ViabilityRecurrent Deep Reinforcement Learning for Chemotherapy Control under Partial Observability

6.0

Higher ViabilityCARE: Privacy-Compliant Agentic Reasoning with Evidence Discordance

8.0

Higher ViabilityScaling Medical Reasoning Verification via Tool-Integrated Reinforcement Learning

7.0

Higher ViabilitySafeMed-R1: Clinician-Audited Safety and Ethics Alignment for Medical Large Language Models

7.0

Conflicting

Competing ApproachCOOL-MC: Verifying and Explaining RL Policies for Platelet Inventory Management

5.0

Related Resources

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.14505
Route: /paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc
Active tab: read
Artifact: formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc/paper-pack
REST build passport API/api/v1/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "add6325c-0968-4332-8eb6-f0ef677945f4",
  "arxiv_id": "2602.14505",
  "canonical_route": "/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc",
  "endpoints": {
    "paper_pack": "/api/v1/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc/paper-pack",
    "build_passport": "/api/v1/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

Canonical ID formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc | Route /paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.14505"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC",
  "normalized_query": "2602.14505",
  "route": "/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc",
  "paper_ref": "formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

/buildability/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc

Watchwatch

Subject: Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc

Paper ref

formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc

arXiv id

2602.14505

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

f8df3ef2633aad81c7d3eee12ac92cf40a3220ccdf08285a09a4b07108600ac1

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc#webpage",
      "url": "https://sciencetostartup.com/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc",
      "name": "Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC",
      "description": "COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc#scholarlyArticle",
      "headline": "Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC",
      "description": "COOL-MC offers clinicians a tool to formally verify and explain sepsis treatment policies using a model checker integrated with explainability methods.",
      "url": "https://sciencetostartup.com/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc",
      "sameAs": "https://arxiv.org/abs/2602.14505",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2602.14505"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-02-16T06:37:34.000Z",
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "4f0bd6933c054998b40bd9de9913af3453e0e748"
          },
          "url": "https://www.semanticscholar.org/paper/4f0bd6933c054998b40bd9de9913af3453e0e748"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "d90f595eb7aad6c5543c08d18b59e7ae70925a80"
          },
          "url": "https://www.semanticscholar.org/paper/d90f595eb7aad6c5543c08d18b59e7ae70925a80"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a19134143f2237999af1cfce54d89aebbf1d4dbf"
          },
          "url": "https://www.semanticscholar.org/paper/a19134143f2237999af1cfce54d89aebbf1d4dbf"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "5e5bc51fb2583b07ee26b88eaa7967c6a3ab1645"
          },
          "url": "https://www.semanticscholar.org/paper/5e5bc51fb2583b07ee26b88eaa7967c6a3ab1645"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "2c9975a2706f4fae4ca74614dcf7e7694316326c"
          },
          "url": "https://www.semanticscholar.org/paper/2c9975a2706f4fae4ca74614dcf7e7694316326c"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1276fc4c9f61eb19121ae3348bd195bca6faa0fb"
          },
          "url": "https://www.semanticscholar.org/paper/1276fc4c9f61eb19121ae3348bd195bca6faa0fb"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "078c46a88ebaf4caa00c7e01b5e99b0ab8d858d9"
          },
          "url": "https://www.semanticscholar.org/paper/078c46a88ebaf4caa00c7e01b5e99b0ab8d858d9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a1eb03bfe2844d3a4d9f58a8130b3249ed892b81"
          },
          "url": "https://www.semanticscholar.org/paper/a1eb03bfe2844d3a4d9f58a8130b3249ed892b81"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "affe7d8835eb74808ad4a00aa7e466791fcc3e50"
          },
          "url": "https://www.semanticscholar.org/paper/affe7d8835eb74808ad4a00aa7e466791fcc3e50"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "dc5a5cab5e1eae4c52d38bf564d2dde3be5c42b8"
          },
          "url": "https://www.semanticscholar.org/paper/dc5a5cab5e1eae4c52d38bf564d2dde3be5c42b8"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a8faef86f3782572e6a6333373563385d99a1386"
          },
          "url": "https://www.semanticscholar.org/paper/a8faef86f3782572e6a6333373563385d99a1386"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "81f56fe41b0eb1d5f4cb3a5787f388325d526961"
          },
          "url": "https://www.semanticscholar.org/paper/81f56fe41b0eb1d5f4cb3a5787f388325d526961"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "e2f01331c53e37e989ca02bab1102ee4e8440f0c"
          },
          "url": "https://www.semanticscholar.org/paper/e2f01331c53e37e989ca02bab1102ee4e8440f0c"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "aafbe075d394dded898be84bd2d380e651a8d0b2"
          },
          "url": "https://www.semanticscholar.org/paper/aafbe075d394dded898be84bd2d380e651a8d0b2"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "eb7c31957805b8b234d966b2040c55d91ca17571"
          },
          "url": "https://www.semanticscholar.org/paper/eb7c31957805b8b234d966b2040c55d91ca17571"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1c2c3be7196499167b7adc054017ec6b615755d8"
          },
          "url": "https://www.semanticscholar.org/paper/1c2c3be7196499167b7adc054017ec6b615755d8"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f0bab27cc9ddef85c12f17e8173a79ed0df07f2e"
          },
          "url": "https://www.semanticscholar.org/paper/f0bab27cc9ddef85c12f17e8173a79ed0df07f2e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "3d1e7315fa3a0fd0060eeea5e0189fb40394d631"
          },
          "url": "https://www.semanticscholar.org/paper/3d1e7315fa3a0fd0060eeea5e0189fb40394d631"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "b38ca740da0221a89c70d49091c11d98537507c8"
          },
          "url": "https://www.semanticscholar.org/paper/b38ca740da0221a89c70d49091c11d98537507c8"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "c1a8351fba022e4d749d941fee522872c5d3356c"
          },
          "url": "https://www.semanticscholar.org/paper/c1a8351fba022e4d749d941fee522872c5d3356c"
        }
      ],
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "propertyID": "viabilityScore",
          "value": 5
        },
        {
          "@type": "PropertyValue",
          "propertyID": "researchDomain",
          "value": "Healthcare AI"
        }
      ]
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://sciencetostartup.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Healthcare AI",
          "item": "https://sciencetostartup.com/topics"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "Formally Verifying and Explaining Sepsis Treatment Policies ",
          "item": "https://sciencetostartup.com/paper/formally-verifying-and-explaining-sepsis-treatment-policies-with-cool-mc"
        }
      ]
    }
  ]
}

Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(74)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(74)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline