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ARXIV:2603.11594 · MEDICAL AI · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.11594MEDICAL AISUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
A predictive model using LLMs for early chemotherapy outcome prediction to enhance patient management.
Opportunity summary
Pain A predictive model using LLMs for early chemotherapy outcome prediction to enhance patient management.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A predictive model using LLMs for early chemotherapy outcome prediction to enhance patient management. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels…
Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve…
Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.
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A predictive model using LLMs for early chemotherapy outcome prediction to enhance patient management.
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10.48550/arXiv.2603.11594A predictive model using LLMs for early chemotherapy outcome prediction to enhance patient management.
Abstract
Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer progression and toxicity. This study addresses these challenges by employing Large Language Models (LLMs) and ontology-based techniques for phenotypes and outcome label extraction from patient notes. We focused on one of the most frequently occurring cancers, breast cancer, due to its high prevalence and significant variability in patient response to treatment, making it a critical area for improving predictive modeling. The dataset included features such as vitals, demographics, staging, biomarkers, and performance scales. Drug regimens and their combinations were extracted from the chemotherapy plans in the EMR data and shortlisted based on NCCN guidelines, verified with NIH standards, and analyzed through survival modeling. The proposed approach significantly reduced phenotypes sparsity and improved predictive accuracy. Random Survival Forest was used to predict time-to-failure, achieving a C-index of 73%, and utilized as a classifier at a specific time point to predict treatment outcomes, with accuracy and F1 scores above 70%. The outcome probabilities were validated for reliability by calibration curves. We extended our approach to four other cancer types. This research highlights the potential of early prediction of treatment outcomes using LLM-based clinical data extraction enabling personalized treatment plans with better patient outcomes.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 6.0
PROBLEM
A predictive model using LLMs for early chemotherapy outcome prediction to enhance patient management. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer prog...
METHOD
Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using real-world...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed dec...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A predictive model using LLMs for early chemotherapy outcome prediction to enhance patient management. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer progression and toxicity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer progression and toxicity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A predictive model using LLMs for early chemotherapy outcome prediction to enhance patient management.
Segment
Medical AI
Adoption evidence
No public code link in the paper record yet
Commercial read
6.0/10 public viability
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Build Passport
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status
missing
reason
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proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
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Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 0 sources, 33% evidence coverage.
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Buyer clarity
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Defensibility
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Integration burden
missing
Current read
No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Paper authors are not treated as operators without consent.
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Prototype owner missing.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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