Machine learning for real-time aggregated prediction of hospital admission for emergency patients

King, Zella; Farrington, Joseph; Utley, Martin; Kung, Enoch; Elkhodair, Samer; Harris, Steve; Sekula, Richard; Gillham, Jonathan; Li, Kezhi; Crowe, Sonya

doi:10.1038/s41746-022-00649-y

Cited by 30 publications

(10 citation statements)

References 42 publications

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“…Similarly, after documenting the clinical history in an electronic health record (perhaps assisted by an AI scribe), AI tools could extract key features using natural language processing and with other parameters such as physiological observations and biomarkers, can support clinicians by generating a differential diagnosis, ensuring that critical diagnoses are considered, and identifying patients at higher risk of deteriorating [ 10 , 11 ]. At the hospital level, routine clinical data collected from a patient's arrival at the emergency department could be used to predict the patient's risk of needing admission, assisting with patient flow and service planning [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

Breaking Bias: The Role of Artificial Intelligence in Improving Clinical Decision-Making

Brown¹,

Nazeer²,

Gibbs³

et al. 2023

Cureus

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This case report reflects on a delayed diagnosis for a 27-year-old woman who reported chest pain and shortness of breath to the emergency department. The treating clinician reflects upon how cognitive biases influenced their diagnostic process and how multiple missed opportunities resulted in missteps. Using artificial intelligence (AI) tools for clinical decision-making, we suggest how AI could augment the clinician, and in this case, delayed diagnosis avoided.Incorporating AI tools into clinical decision-making brings potential benefits, including improved diagnostic accuracy and addressing human factors contributing to medical errors. For example, they may support a real-time interpretation of medical imaging and assist clinicians in generating a differential diagnosis in ensuring that critical diagnoses are considered. However, it is vital to be aware of the potential pitfalls associated with the use of AI, such as automation bias, input data quality issues, limited clinician training in interpreting AI methods, and the legal and ethical considerations associated with their use.The report draws attention to the utility of AI clinical decision-support tools in overcoming human cognitive biases. It also emphasizes the importance of clinicians developing skills needed to steward the adoption of AI tools in healthcare and serve as patient advocates, ensuring safe and effective use of health data.

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Section: Discussionmentioning

confidence: 99%

Breaking Bias: The Role of Artificial Intelligence in Improving Clinical Decision-Making

Brown¹,

Nazeer²,

Gibbs³

et al. 2023

Cureus

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“…In addition to classical statistical methods such as regression, machine learning methods have attracted much attention from medical researcher due to their simplicity and sometimes more accurate predictions. Recently, many studies have compared machine learning methods in survival analysis [6,9]. Machine learning techniques as non-parametric and less complex are good alternatives to statistical methods.…”

Section: Survival Machine Learning Analysismentioning

confidence: 99%

Classification Based on Event in Survival Machine Learning Analysis of Cardiovascular Disease Cohort

Ahmad

Ahmed

2023

Preprint

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The goal of this paper is to evaluate supervised learning classification models in a survival analysis problem of cardiovascular patients with a significant cured fraction. A sample include 919 patients referred to Sulaymaniyah Cardiac Hospital (including 365 females and 554 male) were followed up for a maximum of 650 days in 2021 to 2023. In the sample, 162 patients (17.6%) during research time died. Cure fraction in this cohort was confirmed based on the Mahler and Zhu test (P<0.01), so for this case several machine learning classifications are applied to find the best patient status prediction procedure. Using several machine learning algorithms, the patients were classified into alive and dead. The results based on several indicators were almost the same. But in general, the random forest was identified as the best method in most of the indicators and of course with the Area under ROC = 0.934. The just weakness point of this method is that it did not work relatively well in the correct diagnosis of the deceased, and instead, the SVM with FP Rate = 0.263 performed better. After that, logistic and simple regression with Area under ROC = 0.911 and 0.909 showed better performance than others.

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“…In this context, machine learning (ML), an application of artificial intelligence, is a promising and feasible tool to be used on large scale to identify these population subgroups. Some previous studies have demonstrated that ML models can predict the demand for urgent and emergency services 10,11 . Besides, a systematic review showed that ML could accurately predict the triage of patients entering emergency care 12 .…”

Section: Introductionmentioning

confidence: 99%

Emergency department use and Artificial Intelligence in Pelotas: design and baseline results

Delpino

Figueiredo

Costa

et al. 2023

Rev. bras. epidemiol.

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RESUMO Objetivo: To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil. Methods: The study is entitled “Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). Between September and December 2021, a baseline was carried out with participants. A follow-up was planned to be conducted after 12 months in order to assess the use of urgent and emergency services in the last year. Afterwards, machine learning algorithms will be tested to predict the use of urgent and emergency services over one year. Results: In total, 5,722 participants answered the survey, mostly females (66.8%), with an average age of 50.3 years. The mean number of household people was 2.6. Most of the sample has white skin color and incomplete elementary school or less. Around 30% of the sample has obesity, 14% diabetes, and 39% hypertension. Conclusion: The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state.

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Machine learning for real-time aggregated prediction of hospital admission for emergency patients

Cited by 30 publications

References 42 publications

Breaking Bias: The Role of Artificial Intelligence in Improving Clinical Decision-Making

Breaking Bias: The Role of Artificial Intelligence in Improving Clinical Decision-Making

Classification Based on Event in Survival Machine Learning Analysis of Cardiovascular Disease Cohort

Emergency department use and Artificial Intelligence in Pelotas: design and baseline results

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