Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.
Davide Chicco, Giuseppe Jurman
February 2020 BMC Med Inform Decis MakSynopsis of Social media discussions
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.
Agreement
Moderate agreementMost discussions express support or acknowledgment of the study's findings, indicating general agreement with its significance.
Interest
High level of interestPosts demonstrate high curiosity, often mentioning the innovative use of machine learning for survival predictions, demonstrating keen interest.
Engagement
Moderate level of engagementSome comments delve into details about the dataset and methodology, showing moderate engagement and understanding.
Impact
Moderate level of impactUser comments suggest an appreciation for the study's potential influence on healthcare practices, though not all see it as a groundbreaking shift.
Social Mentions
YouTube
2 Videos
35 Posts
Blogs
2 Articles
News
16 Articles
Metrics
Video Views
35,610
Total Likes
910
Extended Reach
132,224
Social Features
55
Timeline: Posts about article
Top Social Media Posts
Posts referencing the article
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 factors.
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.
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Join the discussion and read the entire study at https://t.co/50MlhLsziD ! How about employing basic signs to make intricate forecasts?
view full postJune 25, 2024
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балканский дата-пёс
@unemployed_ds (Twitter)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
view full postOctober 31, 2022
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Al VA
@alzapress (Twitter)#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
view full postMarch 15, 2022
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İTF Yapay Zeka
@itfyapayzeka (Twitter)Örnek medikal veri işlemesi (Kalp yetmezliği) https://t.co/nQTohw8EC8 Makale; https://t.co/AwYYDIogOU
view full postAugust 13, 2020
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The Data Science Bot
@datasciencebot_ (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postAugust 11, 2020
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HubOfML
@hubofml (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postAugust 11, 2020
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La Ouest-africaine
@west_africaine (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postAugust 11, 2020
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Gnuts about Code
@CodeGnuts (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postAugust 11, 2020
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DavideChicco.it
@DavideChicco_it (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postAugust 11, 2020
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Windy
@HelanDisney (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 20, 2020
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uh-oh
@qualystat (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Gnuts about Code
@CodeGnuts (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Medicine_News
@Medicine__News (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Aaron ''Midlife Coder'' Cuddeback
@AaronCuddeback (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Rajesh
@imrajeshberwal (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Curious Luke
@TheCuriousLuke (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Lane
@Loiss_Vaku (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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BOT Kitty
@BotRaj1 (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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LinuxDreams
@LinuxDreams (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Code Newbie Bot
@_codenewbiebot (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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I'm a Bot #END SARS #SARSMUSTEND
@End_Sars2020 (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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The Data Science Bot
@datasciencebot_ (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Inferno
@theInfernobot (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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JS Bits
@js_bits (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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HubOfML
@hubofml (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Tech Bot
@TechBot19 (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Machine Learning Bot
@ML_Tweet_Bot (Twitter)RT @PoetryAssessor: Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #…
view full postJuly 19, 2020
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Poetry Assessor
@PoetryAssessor (Twitter)Machine learning outperforms 'traditional' tests in predicting heart failure survival: https://t.co/iN9eeJOgm5 #Linux #Python #Java #DataScience #100DaysOfCode #Analytics #BigData #MachineLearning #Javascript #Flask #Django #Artscorer #FemTech #WomenWhoCode #Medical #diagnosis
view full postJuly 19, 2020
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Giuseppe Jurman
@Giuseppe_Jurman (Twitter)RT @DavideChicco_it: The heart failure clinical record dataset @Giuseppe_Jurman and I used for our "Machine learning can predict survival o…
view full postJune 16, 2020
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DavideChicco.it
@DavideChicco_it (Twitter)The heart failure clinical record dataset @Giuseppe_Jurman and I used for our "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone" article is now available on the @UCIrvine ML Repository: https://t.co/T15KDeK4p3
view full postJune 16, 2020
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THE AI FUTURE SHOW Podcast #intoAI #AI
@into_AI (Twitter)Machine learning can predict survival of patients with heart failure from serum - https://t.co/kGeW8X3qGF #machinelearning #intoAInews
view full postFebruary 5, 2020
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Elisa Solano
@elitayoan (Twitter)RT @DavideChicco_it: The new paper "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection…
view full postFebruary 4, 2020
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Alessia Marcolini
@viperale (Twitter)RT @DavideChicco_it: The new paper "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection…
view full postFebruary 3, 2020
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Giuseppe Jurman
@Giuseppe_Jurman (Twitter)RT @DavideChicco_it: The new paper "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection…
view full postFebruary 3, 2020
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DavideChicco.it
@DavideChicco_it (Twitter)The new paper "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone" that I wrote with @Giuseppe_Jurman (@FBKcom @mpbalab) has been published on @BioMedCentral BMC Medical Informatics!
view full postFebruary 3, 2020
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s13369 023 08183 Z - Arabian | PDF | Heart Failure | Mathematical ...
https://doi.org/10.1186/s12911-020-1023-5 doi.org/10.1016/j.ins.2021.11.051 4. Mehedi Zaman, S. M.; Qureshi, W. M.; Raihan, M. M. S.; Bin 20. Doğan, C ...
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Grupo 3 | PDF | Polycystic Ovary Syndrome | Machine Learning
https://doi.org/10.1186/s12911-020-1023-5. PMID: 32013925; PMCID: PMC6998201. ple trials, it was identified that PSO and FF algorithm with RF classifier
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IJGM 325609 Machine Learning Model Applied On Chest X Ray ...
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view full postDecember 18, 2025
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Savedrecs 4 | PDF
... 10.1186/s13040-021-00244-z. Chicco D, 2020, BMC MED INFORM DECIS, V20, DOI 10.1186/s12911-020-1023-5. Chicco D, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone ...
view full postDecember 18, 2025
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22 Vol 102 No 5 | PDF | Machine Learning | Systematic Review
10.1186/s12911-020-1023-5. [87] D. Mpanya, T. Celik, E. Klug, and H. Ntsinjana, “Machine learning and statistical methods for predicting mortality in heart ...
view full postDecember 18, 2025
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Chicco-Jurman2020 Article MachineLearningCanPredictSurvi | PDF ...
Chicco and Jurman BMC Medical Informatics and Decision Making (2020) 20:16. https://doi.org/10.1186/s12911-020-1023-5 ...
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Enhancing predictive modelling and interpretability in heart failure ...
doi: 10.1186/s12911-020-1023-5. [24] M. A-M. Hasan, J. Shin, U. Das, and A. Y. Srizon, “Identifying prognostic features for predicting heart failure by using
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Prognostic Modeling For Heart Failure Survival A Classification ...
DOI: 10.1186/s12911-020-1023-5. PMID: 32013925; PMCID: Regres- PMC6998201. ... Analysis of Heart Failure Patients: A Case Study,” PLOS ONE, vol. 12, p. e0181001, ...
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... 10.1186/s12911-020-1023-5#Sec2]. Written By. Ivo Bernardo. See all from Ivo Bernardo · Artificial Intelligence, Deep Learning, Machine Learning ...
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Pytorch Introduction — Enter NonLinear Functions | by Ivo Bernardo ...
... under licence Creative Commons https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-1023-5#Sec2]. 174. Pytorch.
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https://doi.org/10.1186/s12911-020-1023-5. This data set has 12 features and you can download it from the UCI Machine Learning Repository. It ...
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Machine Learning Mini-Project 4: Finding Important Features using ...
https://doi.org/10.1186/s12911-020-1023-5. This data set has 12 features and you can download it from the UCI Machine Learning Repository. It ...
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Predicting Heart Failure Using Machine Learning, Part 1 | by Andrew ...
https://doi.org/10.1186/s12911-020-1023-5. The above table describes the features of clinical and laboratory data provided in the dataset ...
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view full postJune 21, 2020
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Abstract Synopsis
- 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.]
No more
@Heart_livingM (Twitter)