Use of learning approaches to predict clinical deterioration in patients based on various variables: a review of the literature

Al-Shwaheen, Tariq Ibrahim; Moghbel, Mehrdad; Hau, Yuan Wen; Ooi, Chia Yee

doi:10.1007/s10462-021-09982-2

Cited by 8 publications

(8 citation statements)

References 85 publications

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“…In the other words, it is expected that applying emerging technologies improve EW/TTS for advanced automated real-time monitoring, timely diagnosis, accurate warning at CD, and precise prediction of SAEs as early as possible. The internet of things (IoT) and artificial intelligence are crucial technologies to bridge the remaining gaps [23,24]. The full automation of EW/TTS for vital signs monitoring and CD detection and prediction looks ideal, however, it is challenging [25].…”

Section: Plos Onementioning

confidence: 99%

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms

Kalhori

Deserno²,

Haghi³

et al. 2023

PLoS ONE

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Background This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD). Methodology This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers. Discussion This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences. Trial registration Systematic review registration: PROSPERO CRD42022334988.

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Section: Plos Onementioning

confidence: 99%

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms

Kalhori

Deserno²,

Haghi³

et al. 2023

PLoS ONE

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“…Romero-Brufau et al stated that the dynamic entities of CD (4) yield a high number of false alerts and alert fatigue (5). Emerging technologies may improve EWS for advanced automated real-time monitoring, timely diagnosis, accurately warning at CD, and precise prediction of adverse events as early as possible (6,7). Further study is required for advanced EWS design and development (8).…”

Section: Introductionmentioning

confidence: 99%

Requirement Analysis for an Intelligent Warning System to Alarm the Rapid Response Team Prior to Patient Deterioration

Kalhori

Deserno

2022

Studies in Health Technology and Informatics

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The early warning system alarms the rapid response team (RRT) for clinical deterioration monitoring and prediction. Available systems do not perform well to decrease the number of ICU transfers or death. This study aimed to address the requirement of an intelligent warning system for timely and accurate RRT activation. Methodology: A literature review was conducted in scientific databases to extract data. Then, a questionnaire was developed for experts’ views collection (N=12). The collected data were analyzed using the Content Validity Ratio (CVR). According to the Lawshe table for the corresponding number of experts, the cut-off=0.56 for items to be accepted/rejected was considered. A schematic structure was suggested. Findings: The analysis of the extracted papers (N=24) and qualitative analysis addressed 44 requirements in the frame of five involved sub-systems, including a patient monitoring system, electronic health record, clinical decision support system, remote monitoring patient, and dashboard &registries. They were confirmed by meeting the least cut-off value (CVR= 0.86). Conclusion: An integrated approach and technologies of IoT, deep and machine learning techniques, big data, advanced databases, and standards to create an intelligent EWS are required.

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“…To improve prediction accuracy and reduce alarm fatigue, new applications using more data and more sophisticated methodologies have been developed (8). Given the heterogeneous nature of EHR data and the rarity of events such as clinical deterioration, machine learning methods seem well-suited for addressing this task.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a dynamic prediction model for unplanned ICU admission and mortality in hospitalized patients

Brunak

Thorsen-Meyer

Kaas‐Hansen

et al. 2022

Preprint

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Frequent assessment of the severity of illness for hospitalized patients is essential in clinical settings to prevent outcomes such as in-hospital mortality and unplanned ICU admission. Classical severity scores have been developed typically using relatively few patient features, especially for intensive care. Recently, deep learning-based models demonstrated better individualized risk assessments compared to classic risk scores such as SOFA and NEWS, thanks to the use of aggregated and more heterogeneous data sources for dynamic risk prediction. We investigated to what extent deep learning methods can capture patterns of longitudinal change in health status using time- stamped data from electronic health records. We used medical history data, biochemical measurements, and the clinical notes from all patients admitted to non- intensive care units in 12 hospitals in Denmark’s Capital Region and Region Zealand during 2011-2016. Data from a total of 852,620 patients and 2,241,849 admissions were used to predict the composite outcome of unplanned ICU transfer and in-hospital death at different time points after admission to general departments. We subsequently examined feature interpretations of the models. The best model used all data modalities with an assessment rate of 6 hours and a prediction window of 14 days, with an AUPRC of 0.287 and AUROC of 0.898. These performances are comparable to the current state of the art and make the model suitable for further prospective validation as a risk assessment tool in a clinical setting.

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Use of learning approaches to predict clinical deterioration in patients based on various variables: a review of the literature

Cited by 8 publications

References 85 publications

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms

Requirement Analysis for an Intelligent Warning System to Alarm the Rapid Response Team Prior to Patient Deterioration

Development and validation of a dynamic prediction model for unplanned ICU admission and mortality in hospitalized patients

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