ArticleSensors (Basel, Switzerland)2022
Intelligent Clinical Decision Support.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Data-driven decision making in patient management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- From Intelligent Operating Room to Smart ICU: Digital Continuity, AI-Supported Decision-Making and CRRT as a Model of Pharmacokinetic Personalization in Critical Care.Healthcare (Basel, Switzerland) · 2026Review
- Integrating AI into infection control: Evaluating the accuracy and consistency of four leading platforms across three regions.Journal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada · 2026Article
- AI and clinicians growing together: A cross-sectional survey of clinicians' attitudes toward AI-CDSS with comparison to 2020 data.Clinical medicine (London, England) · 2026Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- Interventions for adult survivors discharged from the intensive care unit: a scoping review.BMJ open · 2025Article
- Article
- The Digital Transformation of Healthcare Through Intelligent Technologies: A Path Dependence-Augmented-Unified Theory of Acceptance and Use of Technology Model for Clinical Decision Support Systems.Healthcare (Basel, Switzerland) · 2025Article
- Artificial intelligence technology in ophthalmology public health: current applications and future directions.Frontiers in cell and developmental biology · 2025Review
- Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting.Sensors (Basel, Switzerland) · 2024Review
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design.JMIR formative research · 2024Article
- Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches.NPJ digital medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system.
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.