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ai-gone-astray

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software/ai-gone-astray

Code for "AI Gone Astray: Technical Supplement", which investigates the effect of time drift on clinically deployed machine learning models

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Documentation
unknown
Tags
deep-learning · ehr · machine-learning · sepsis
Regulatory
unknown
built by · 1

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similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • Virulent infectious agents such as SARS-CoV-2 and Methicillin Resistant Staphylococcus Aureus (MRSA) induce tissue damage that recruits neutrophils and monocyte/macrophages that promote T cell…

  • Abstract Background: Cardiopulmonary bypass generates an exacerbated response that may lead to sepsis Objective: To describe the association between procalcitonin levels and sepsis diagnosis in…

  • EHR systems integrate with ABHA and ABDM, enabling seamless access to patient records and improving healthcare services in India through effective EHR integration.

  • apiehr

    APIs para ANDES

  • Electronic Health Record (EHR) and Electronic Medical Record (EMR) systems. However, they still face some issues regarding the security of medical records, user ownership of data, data integrity…

  • Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves…

sources
  1. api.github.com/repos/mariehane/ai-gone-astray
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2022-03-31, 3 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

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machine-readable

/v1/entries/20.json→ .entries["ai-gone-astray"]

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