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Clinical Informatics (1112) Electronic Health Records (1131) Machine Learning (1644) Tools, Programs and Algorithms (308) Secondary Use of Clinical Data for Research and Surveillance (400) Natural Language Processing (767) Decision Support for Health Professionals (1301) Nephrology/Renal Disorders (72)

Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30805, first published .
Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study

Authors of this article:

Horng-Ruey Chua1, 2 Author Orcid Image ;   Kaiping Zheng3 Author Orcid Image ;   Anantharaman Vathsala1, 2 Author Orcid Image ;   Kee-Yuan Ngiam4, 5 Author Orcid Image ;   Hui-Kim Yap6, 7 Author Orcid Image ;   Liangjian Lu6, 7 Author Orcid Image ;   Ho-Yee Tiong5, 8 Author Orcid Image ;   Amartya Mukhopadhyay2, 9 Author Orcid Image ;   Graeme MacLaren5, 10 Author Orcid Image ;   Shir-Lynn Lim2, 11 Author Orcid Image ;   K Akalya1 Author Orcid Image ;   Beng-Chin Ooi3 Author Orcid Image
Article Authors Cited by (9) Tweetations (6) Metrics
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JMIR Publications JMIR Publications @jmirpub

New in JMIR: #HealthCare Analytics With Time-Invariant and Time-Variant Feature Importance to Predict #Hospital-Acq… https://t.co/DuUUdTAl8v

3:36 PM · Dec 24, 2021

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Journal of Medical Internet Research Journal of Medical Internet Research @JMIR_

RT @jmirpub: New in JMIR: #HealthCare Analytics With Time-Invariant and Time-Variant Feature Importance to Predict #Hospital-Acquired Acute…

3:37 PM · Dec 24, 2021

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Journal of Medical Internet Research Journal of Medical Internet Research @JMIR_

RT @jmirpub: New in JMIR: #HealthCare Analytics With Time-Invariant and Time-Variant Feature Importance to Predict #Hospital-Acquired Acute…

3:37 PM · Dec 24, 2021

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Oncology & Machine Learning Oncology & Machine Learning @MlOncology

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Ki… https://t.co/E6RkkDBA9u

8:09 AM · Dec 25, 2021

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Oncology & Machine Learning Oncology & Machine Learning @MlOncology

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Ki… https://t.co/E6RkkDBA9u

8:09 AM · Dec 25, 2021

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Sami Nas ????‍⚕️ Sami Nas ????‍⚕️ @digitalhealthxx

#HealthCare #Analytics With Time-#Invariant and Time-Variant Feature Importance to #Predict Hospital-Acquired… https://t.co/La5CnUZLWy

9:33 AM · Dec 25, 2021

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Sami Nas ????‍⚕️ Sami Nas ????‍⚕️ @digitalhealthxx

#HealthCare #Analytics With Time-#Invariant and Time-Variant Feature Importance to #Predict Hospital-Acquired… https://t.co/La5CnUZLWy

9:33 AM · Dec 25, 2021

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Horng Ruey Chua Horng Ruey Chua @Chua_HR

Support our NUHS informatics @JMedInternetRes https://t.co/KvbNGeBWz8

8:54 AM · Dec 26, 2021

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Horng Ruey Chua Horng Ruey Chua @Chua_HR

Support our NUHS informatics @JMedInternetRes https://t.co/KvbNGeBWz8

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pubmed_artificial_intelligence pubmed_artificial_intelligence @pubmedartifici

https://t.co/wI74eCxXpR bidirectional rnnを使用し、入院中のAKIを48時間前からのデータから予測するリアルタイムAKI監視モデルを作成。AUROCが0.81 敗血症、急性冠症候群、腎毒性、多臓器障害がリスクファクターだった。

1:57 AM · Dec 29, 2021

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pubmed_artificial_intelligence pubmed_artificial_intelligence @pubmedartifici

https://t.co/wI74eCxXpR bidirectional rnnを使用し、入院中のAKIを48時間前からのデータから予測するリアルタイムAKI監視モデルを作成。AUROCが0.81 敗血症、急性冠症候群、腎毒性、多臓器障害がリスクファクターだった。

1:57 AM · Dec 29, 2021

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