Developing a machine learning model to identify delirium risk in geriatric internal medicine inpatients

Li, Qinzheng; Zhao, Yanli; Chen, Yu; Yue, Jirong; Xiong, Yan

doi:10.1007/s41999-021-00562-9

Cited by 7 publications

(23 citation statements)

References 39 publications

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“…The search strategy identified a total of 921 records; after duplicate removal and title and abstract screening, 114 full-text studies were retrieved, of which 3916–54 met the selection criteria for inclusion in the final analysis (figure 2).…”

Section: Resultsmentioning

confidence: 99%

“…Study characteristics are summarised in online supplemental table 1. Studies originated from the USA (n=12),17 19–23 25 41 43 50 51 54 Austria (n=9),24 28–31 33 39 47 48 China (n=6),26 32 35 49 52 53 Germany (n=3),37 45 46 South Korea (n=3),27 40 44 Canada (n=3),30 36 38 Brazil (n=1),16 Japan (n=1),34 Spain (n=1)18 and one study was labelled as international 42. Over the 6-year distribution of publications to June 2022, most studies were published in 2021 (n=10) and the first half of 2022 (n=12), indicating considerable growth in research in this area since the publication of previous reviews of studies published up to 2019 11 12.…”

Section: Resultsmentioning

confidence: 99%

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Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

Strating

Hanjani

Tornvall

et al. 2023

BMJ Health Care Inform

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ObjectivesEarly identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature.MethodsWe searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reporting at least one performance measure (area under receiver-operator curve (AUROC), sensitivity, specificity, positive or negative predictive value). Included models were categorised by their stage of maturation and assessed for performance, utility and user acceptance in clinical practice.ResultsAmong 921 screened studies, 39 met eligibility criteria. In-silico performance was consistently high (median AUROC: 0.85); however, only six articles (15.4%) reported external validation, revealing degraded performance (median AUROC: 0.75). Three studies (7.7%) of models deployed within clinical workflows reported high accuracy (median AUROC: 0.92) and high user acceptance.DiscussionML models have potential to identify inpatients at risk of developing delirium before symptom onset. However, few models were externally validated and even fewer underwent prospective evaluation in clinical settings.ConclusionThis review confirms a rapidly growing body of research into using ML for predicting delirium risk in hospital settings. Our findings offer insights for both developers and clinicians into strengths and limitations of current ML delirium prediction applications aiming to support but not usurp clinician decision-making.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

Strating

Hanjani

Tornvall

et al. 2023

BMJ Health Care Inform

View full text Add to dashboard Cite

show abstract

“…A noticeable lack of internationalization was detected. Many studies focused only on local data, which contributes to small sample sizes and poor generalizability [115,127]. Encouraging partnerships and collaborations across countries and centers should be a priority, as it could facilitate external validation [71].…”

Section: Challengesmentioning

confidence: 99%

“…Some studies introduced devices and platforms to improve the monitoring of patients. The application of everyday digital tools could reduce necessary resources and therefore facilitate data collection [99,127]. Promoting the use of frequently used devices combined with the application of AI models seems like a future trend that could improve the treatment of many conditions where the chance of treatment response decreases over time [126].…”

Section: Opportunitiesmentioning

confidence: 99%

“…AI models from clinical stage to clinical practice could be difficult but powerful tools to gain insights into predictor collection, human-based decisions, and AI biases while these techniques are being implemented in clinical world. Many studies report the high potential of AI in mental health for clinical support, computer-aided systems, and possibly preliminary screening [94,127].…”

Section: Opportunitiesmentioning

confidence: 99%

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Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

Tornero-Costa¹,

Martínez-Millana²,

Azzopardi-Muscat³

et al. 2023

JMIR Ment Health

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Background Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. Objective This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. Methods A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. Results A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment—18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). Conclusions These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

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Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups

Weidmann,

Watson

2024

Int J Clin Pharm

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The advent of artificial intelligence (AI) technologies has taken the world of science by storm in 2023. The opportunities of this easy to access technology for clinical pharmacy research are yet to be fully understood. The development of a custom-made large language model (LLM) (DELSTAR) trained on a wide range of internationally recognised scientific publication databases, pharmacovigilance sites and international product characteristics to help identify and summarise medication related information on delirium, as a proof-of-concept model, identified new facilitators and barriers for robust clinical pharmacy practice research. This technology holds great promise for the development of much more comprehensive prescribing guidelines, practice support applications for clinical pharmacy, increased patient and prescribing safety and resultant implications for healthcare costs. The challenge will be to ensure its methodologically robust use and the detailed and transparent verification of its information accuracy.

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Developing a machine learning model to identify delirium risk in geriatric internal medicine inpatients

Cited by 7 publications

References 39 publications

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups

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