ArticlePloS one2016
Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Secondary use of health records for prediction, detection, and treatment planning in the clinical decision support system: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Interpretable Machine Learning for In-Hospital Mortality Prediction in ICU Patients Using First-24-Hour Routine Vital Signs: A SHAP-Based MIMIC-IV Study.Diagnostics (Basel, Switzerland) · 2026Article
- Risk stratification and determinant identification of high-need, high-cost ICU patients using machine learning: a large-scale retrospective study from a multi-specialty ICU in a tertiary hospital.Frontiers in public health · 2026Article
- Explainable machine learning models for mortality prediction in patients with sepsis in tertiary care hospital ICU in low- to middle-income countries.Intensive care medicine experimental · 2025Article
- Early prediction of mortality upon intensive care unit admission.BMC medical informatics and decision making · 2024Article
- Artificial intelligence: a new cutting-edge tool in spine surgery.Asian spine journal · 2024Article
- Anesthesia decision analysis using a cloud-based big data platform.European journal of medical research · 2024Review
- The prediction of mortality influential variables in an intensive care unit: a case study.Personal and ubiquitous computing · 2023Article
- Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion.Sensors (Basel, Switzerland) · 2022Article
- Applied Machine Learning for Spine Surgeons: Predicting Outcome for Patients Undergoing Treatment for Lumbar Disc Herniation Using PRO Data.Global spine journal · 2022Article
- Current status and trends in researches based on public intensive care databases: A scientometric investigation.Frontiers in public health · 2022Article
- Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.BMC medical informatics and decision making · 2020Article
- Article
- Predicting adverse hemodynamic events in critically ill patients.Current opinion in critical care · 2018Review
- The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison.Journal of biomedical informatics · 2018Article
- Secondary analysis of electronic health records in critical care medicine.Annals of translational medicine · 2018Article
- Promoting Secondary Analysis of Electronic Medical Records in China: Summary of the PLAGH-MIT Critical Data Conference and Health Datathon.JMIR medical informatics · 2017Article
- Are Randomized Controlled Trials the (G)old Standard? From Clinical Intelligence to Prescriptive Analytics.Journal of medical Internet research · 2016Article
- Normalization methods in time series of platelet function assays: A SQUIRE compliant study.Medicine · 2016Observational
- Inflammation Thread Runs across Medical Laboratory Specialities.Mediators of inflammation · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner's Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.