Paper Citation Record · LEDGER

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests

As of 23 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.15330.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.15330 v1

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Pith citing papers itemized under the disclosed page cap.

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External citation measurements

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Outbound references

Observation 94c72bd2-6d44-40f2-a921-7600d4692dcb · outbound

This paper cites The selection and use of essential in vitro diagnostics: Report of the third meeting of the strategic advisory group of experts on in vitro diagnostics,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests The selection and use of essential in vitro diagnostics: Report of the third meeting of the strategic advisory group of experts on in vitro diagnostics,

Reference 1

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Observation a059f702-7a98-4a54-ac2d-377f8f9bcb75 · outbound

This paper cites Artificial intelligence in routine blood tests,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Artificial intelligence in routine blood tests,

Reference 2

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This paper cites Usefulness of complete blood count (cbc) to assess cardiovascular and metabolic diseases in clinical settings: A comprehensive literature review,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Usefulness of complete blood count (cbc) to assess cardiovascular and metabolic diseases in clinical settings: A comprehensive literature review,

Reference 3

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Observation 2b2a06a4-6bff-4b9f-9b3a-bb4a26592f20 · outbound

This paper cites Automated prediction of low ferritin concentrations using a machine learning algorithm,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Automated prediction of low ferritin concentrations using a machine learning algorithm,

Reference 4

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Observation 26fef770-5c13-4684-8d8a-cad82a18c3be · outbound

This paper cites Using machine learning to predict laboratory test results,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Using machine learning to predict laboratory test results,

Reference 5

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Observation d5aca6fc-756c-40d9-a6a1-833be5bf5866 · outbound

This paper cites Classification and Explanation of Iron Deficiency Anemia from Complete Blood Count Data Using Machine Learning,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Classification and Explanation of Iron Deficiency Anemia from Complete Blood Count Data Using Machine Learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:33.551355Z

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Observation 470728f6-aec4-468d-a8de-a836c1617a90 · outbound

This paper cites Use of machine learning and routine laboratory tests for diabetes mellitus screening,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Use of machine learning and routine laboratory tests for diabetes mellitus screening,

Reference 7

Resolution: verified exact; doi, observed 2026-08-15T19:41:32.925316Z

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This paper cites A comparative analysis of machine learning models for the detection of undiagnosed diabetes patients,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests A comparative analysis of machine learning models for the detection of undiagnosed diabetes patients,

Reference 8

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This paper cites Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population,

Reference 9

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Observation 53e77418-c0c1-4ef1-856e-20fbf75a02b9 · outbound

This paper cites Exploration of machine learning for hyperuricemia prediction models based on basic health checkup tests,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Exploration of machine learning for hyperuricemia prediction models based on basic health checkup tests,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:33.509695Z

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Observation ab141664-ffbd-43c8-9d1f-54170be2cc8c · outbound

This paper cites Prediction model of random forest for the risk of hyperuricemia in a chinese basic health checkup test,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Prediction model of random forest for the risk of hyperuricemia in a chinese basic health checkup test,

Reference 11

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Observation 0700fa43-aeac-4619-95fc-a0f87795fb05 · outbound

This paper cites A Survey of Large Language Models in Medicine: Progress, Application, and Challenge.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests A Survey of Large Language Models in Medicine: Progress, Application, and Challenge

Reference 12

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This paper cites Revisiting Deep Learning Models for Tabular Data.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Revisiting Deep Learning Models for Tabular Data

Reference 13

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This paper cites Blood uric acid prediction with machine learning: Model development and performance comparison,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Blood uric acid prediction with machine learning: Model development and performance comparison,

Reference 14

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Observation 1da9d26b-7320-4984-806a-2657917e3396 · outbound

This paper cites Multi-class classification algorithms for the diagnosis of anemia in an outpatient clinical setting,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Multi-class classification algorithms for the diagnosis of anemia in an outpatient clinical setting,

Reference 15

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Observation 5b8482aa-d756-4f1d-b111-cea07747d1ac · outbound

This paper cites A survey on missing data in machine learning,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests A survey on missing data in machine learning,

Reference 16

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This paper cites A review of missing data handling techniques for machine learning,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests A review of missing data handling techniques for machine learning,

Reference 17

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Observation 03655a49-32c4-40ba-b0ab-c0b583a144b6 · outbound

This paper cites A review on genetic algorithm: past, present, and future,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests A review on genetic algorithm: past, present, and future,

Reference 18

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This paper cites Development of optimal transmission rate of the kinematic chain by using genetic algorithms coded in mathcad,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Development of optimal transmission rate of the kinematic chain by using genetic algorithms coded in mathcad,

Reference 19

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Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Gamin: Generative adversarial multiple imputation network for highly missing data,

Reference 20

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Observation 32e28e43-3b7f-42a6-9f15-6c44c4144066 · outbound

This paper cites A survey of missing data imputation using generative adversarial networks,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests A survey of missing data imputation using generative adversarial networks,

Reference 21

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Observation 0b6394cc-ba2c-40cc-a97e-43f1b321b83a · outbound

This paper cites A machine learning model for hemoglobin estimation and anemia classification,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests A machine learning model for hemoglobin estimation and anemia classification,

Reference 22

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This paper cites Set transformer: A framework for attention-based permutation-invariant neural networks,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Set transformer: A framework for attention-based permutation-invariant neural networks,

Reference 23

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This paper cites Attention is All you Need,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Attention is All you Need,

Reference 24

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This paper cites Available: https://yandex.cloud/en-ru/services/yandexgpt.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Available: https://yandex.cloud/en-ru/services/yandexgpt

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:33.345952Z

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This paper cites TabTransformer: Tabular Data Modeling Using Contextual Embeddings.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests TabTransformer: Tabular Data Modeling Using Contextual Embeddings

Reference 26

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This paper cites Cholletet al., “Keras,” https://keras.io, 2015.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Cholletet al., “Keras,” https://keras.io, 2015

Reference 27

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Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests TensorFlow: Large-scale machine learning on heterogeneous systems,

Reference 28

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This paper cites A survey of multi-task learning methods in chemoinformatics,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests A survey of multi-task learning methods in chemoinformatics,

Reference 29

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Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests On Embeddings for Numerical Features in Tabular Deep Learning

Reference 30

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Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling

Reference 31

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This paper cites Federated learning: Overview, strategies, applications, tools and future directions,.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Federated learning: Overview, strategies, applications, tools and future directions,

Reference 32

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This paper cites Available: https://onlinelibrary.wiley.com/doi/abs/10.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Available: https://onlinelibrary.wiley.com/doi/abs/10

Reference 2019

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This paper cites Available: https://doi.org/10.1371/journal.pone.0269685.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Available: https://doi.org/10.1371/journal.pone.0269685

Reference 2022

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This paper cites Available: https://www.mdpi.com/2673-7426/4/1/36.

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests Available: https://www.mdpi.com/2673-7426/4/1/36

Reference 2024

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Pith citing papers

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