ArticleJMIR medical informatics2014
Making big data useful for health care: a summary of the inaugural mit critical data conference.
Article in JMIR medical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 33 papers.
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
33 citing papers in PubMed.
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- A Systematic Comparison of Age, Comorbidity and Frailty of Two Defined ICU Populations in the German Helios Hospital Group from 2016-2021.Journal of clinical medicine · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Medical Informatics Operating Room Vitals and Events Repository (MOVER): a public-access operating room database.JAMIA open · 2023Article
- A Digital Opportunity for Patients to Manage Their Health: Turkey National Personal Health Record System (The e-Nabız)Balkan medical journal · 2023Observational
- What ethical approaches are used by scientists when sharing health data? An interview study.BMC medical ethics · 2022Article
- Concept Libraries for Repeatable and Reusable Research: Qualitative Study Exploring the Needs of Users.JMIR human factors · 2022Article
- The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI).PLOS digital health · 2022Article
- Lessons learned from an enterprise-wide clinical datathon.Journal of clinical and translational science · 2022Article
- Preferences of the Public for Sharing Health Data: Discrete Choice Experiment.JMIR medical informatics · 2021Article
- Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?Cureus · 2021Review
- MIT COVID-19 Datathon: data without boundaries.BMJ innovations · 2021Article
- Agile clinical research: A data science approach to scrumban in clinical medicine.Intelligence-based medicine · 2020Article
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.JMIR mHealth and uHealth · 2019Article
- Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles From a Pilot Program at Crisis Text Line.Journal of medical Internet research · 2019Article
- Chasing Zero Harm in Radiation Oncology: Using Pre-treatment Peer Review.Frontiers in oncology · 2019Article
- Artificial intelligence, machine learning and health systems.Journal of global health · 2018Article
- Big Data and Data Science in Critical Care.Chest · 2018Review
- [Big data in imaging].Zeitschrift fur Rheumatologie · 2018Review
- Machine learning to support social media empowered patients in cancer care and cancer treatment decisions.PloS one · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines-including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology-gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.
Indexed as
Identifiers
What OpenQuestion holds
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.