BMC Med Inform Decis Mak February 2020 PMID 32013925

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.

Davide Chicco, Giuseppe Jurman

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Mentions

Peak: 19 in Jul 2020

across 4 sources since Feb 2020

Extended reach

132,224

potential audience

Total likes

910

Video views

35,610

Mentions per month

Feb 2020 – Aug 2025

Where it's discussed

X / Twitter35 posts
News16 articles
YouTube2 videos
Blogs2 articles

AI synopsis

What people are saying

The discussions primarily highlight the importance of machine learning in predicting heart failure outcomes using easily accessible clinical features like serum creatinine and ejection fraction, with examples such as references to the dataset and praise for the study’s potential for improving medical predictions. The tone varies from supportive technical appreciation to curiosity about practical applications, reflecting both interest and engagement.

AgreementModerate agreement

Most discussions express support or acknowledgment of the study's findings, indicating general agreement with its significance.

InterestHigh interest

Posts demonstrate high curiosity, often mentioning the innovative use of machine learning for survival predictions, demonstrating keen interest.

EngagementModerate engagement

Some comments delve into details about the dataset and methodology, showing moderate engagement and understanding.

ImpactModerate impact

User comments suggest an appreciation for the study's potential influence on healthcare practices, though not all see it as a groundbreaking shift.

Abstract in brief

Machine learning can effectively predict the survival of heart failure patients using only serum creatinine and ejection fraction, highlighting these as key risk factors.
The study compares traditional biostatistics and machine learning methods for feature ranking, both identifying serum creatinine and ejection fraction as the most important predictors.
Models utilizing just these two features outperform those using the full dataset, confirming their sufficiency and importance in accurate survival predictions.]

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All mentions

Predicting Heart Failure Survival Using Machine Learning Techniques

This video explains how data science and machine learning are used to accurately predict survival in heart failure patients by analyzing serum creatinine and ejection fraction, emphasizing their importance as key risk…

Street Light · Apr 22, 2023 35,559 views

Machine Learning for Heart Failure Survival Prediction Using Key Biomarkers

This video discusses how machine learning can effectively predict heart failure patient survival using serum creatinine and ejection fraction, emphasizing their importance as key risk factors in survival models.

JTProductions3 · Oct 11, 2021 51 views

No more

@Heart_livingM · X

Join the discussion and read the entire study at https://t.co/50MlhLsziD ! How about employing basic signs to make intricate forecasts?

Jun 25, 2024 · 1 followers View post →

балканский дата-пёс

@unemployed_ds · X

Вообще, авторы целую статью написали, где они на этих данных построили модель. Я буду в неё загляну по ходу дела. 2/n https://t.co/EE4vJOCTNP

Oct 31, 2022 · 6 followers View post →

Al VA

@alzapress · X

#ArtificialIntelligence #Artificial_Intelligence #heartattack #heartfailure #healthcare ML can predict patients’ #survival from their #data and can individuate the most important features among those included in their #Medical #records https://t.co/vchZgs27Pf

Mar 15, 2022 · 1 share · 1,265 followers View post →

İTF Yapay Zeka

@itfyapayzeka · X

Örnek medikal veri işlemesi (Kalp yetmezliği) https://t.co/nQTohw8EC8 Makale; https://t.co/AwYYDIogOU

Aug 13, 2020 · 1 like · 67 followers View post →