Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study

BACKGROUND

Delirium in intensive care unit (ICU) patients poses a significant challenge, affecting patient outcomes and health care efficiency. Developing an accurate, real-time prediction model for delirium represents an advancement in critical care, addressing needs for timely intervention and resource optimization in ICUs.

OBJECTIVE

We aimed to create a novel machine learning model for delirium prediction in ICU patients using only continuous physiological data.

METHODS

We developed models integrating routinely available clinical data, such as age, sex, and patient monitoring device outputs, to ensure practicality and adaptability in diverse clinical settings. To confirm the reliability of delirium determination records, we prospectively collected results of Confusion Assessment Method for the ICU (CAM-ICU) evaluations performed by qualified investigators from May 17, 2021, to December 23, 2022, determining Cohen κ coefficients. Participants were included in the study if they were aged ≥18 years at ICU admission, had delirium evaluations using the CAM-ICU, and had data collected for at least 4 hours before delirium diagnosis or nondiagnosis. The development cohort from Yongin Severance Hospital (March 1, 2020, to January 12, 2022) comprised 5478 records: 5129 (93.62%) records from 651 patients for training and 349 (6.37%) records from 163 patients for internal validation. For temporal validation, we used 4438 records from the same hospital (January 28, 2022, to December 31, 2022) to reflect potential seasonal variations. External validation was performed using data from 670 patients at Ajou University Hospital (March 2022 to September 2022). We evaluated machine learning algorithms (random forest [RF], extra-trees classifier, and light gradient boosting machine) and selected the RF model as the final model based on its performance. To confirm clinical utility, a decision curve analysis and temporal pattern for model prediction during the ICU stay were performed.

RESULTS

The κ coefficient between labels generated by ICU nurses and prospectively verified by qualified researchers was 0.81, indicating reliable CAM-ICU results. Our final model showed robust performance in internal validation (area under the receiver operating characteristic curve [AUROC]: 0.82; area under the precision-recall curve [AUPRC]: 0.62) and maintained its accuracy in temporal validation (AUROC: 0.73; AUPRC: 0.85). External validation supported its effectiveness (AUROC: 0.84; AUPRC: 0.77). Decision curve analysis showed a positive net benefit at all thresholds, and the temporal pattern analysis showed a gradual increase in the model scores as the actual delirium diagnosis time approached.

CONCLUSIONS

We developed a machine learning model for delirium prediction in ICU patients using routinely measured variables, including physiological waveforms. Our study demonstrates the potential of the RF model in predicting delirium, with consistent performance across various validation scenarios. The model uses noninvasive variables, making it applicable to a wide range of ICU patients, with minimal additional risk.

A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital

BACKGROUND

The time a patient spends in the hospital from admission to discharge is known as the length of stay (LOS). Predicting LOS is crucial for enhancing patient care, managing hospital resources, and optimizing the use of patient beds. Therefore, this study aimed to predict the LOS for patients hospitalized in various clinics using different artificial intelligence (AI) models.

METHODS

The study analyzed 162,140 hospitalized patients aged 18 and older at various clinics of a university hospital in northern Türkiye from 2012 to 2020. Three soft computing methods-Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Multiple Linear Regression Analysis (MLR)-were employed to estimate LOS using inputs such as medical and imaging services (number of CT, USG, ECG, hemogram tests, medical biochemistry, and number of direct x-rays), demographic, and diagnostic data (patients' age, sex, season of hospitalization, type of hospitalization, diagnosis, and second diagnosis). The LOS predictions utilized single and double-hidden layer ANNs with various training algorithms (Levenberg-Marquardt-LM, Bayesian Regularization-BR and Scaled Conjugate Gradient-SCG) and activation functions (tangent-sigmoid, purelin), ANFIS with Grid Partitioning (ANFIS-GP), and MLR. Model performance was evaluated using the Coefficient of Determination (R²), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

RESULTS

Of the patients, 54% were male and 43.5% were treated in surgical clinics. The mean age was 55.1 years, with 32.9% of participants aged 65 years or older. Hospital stays were 2-7 days for 39.7% of patients, over 7 days for 30.9%, and 1 day for 29.4%. Neoplasm-related diagnoses (ICD codes) accounted for 25.1% of admissions. Variables influencing LOS were identified through feature selection from patients in various hospital wards. The most significant factors affecting LOS include second diagnosis, the number of hemogram tests, computerized tomography scans (CT), ultrasonography (USG), and direct X-rays. Utilizing these factors, 12 models with varied input variables were developed and analyzed. The double hidden layer ANN model with the Levenberg-Marquardt (LM) training algorithm outperformed the others, achieving R² values of 0.854 for training and 0.807 for the test dataset, with RMSE values of 2.397 days and 2.774 days and MAE values of 1.787 days and 1.994 days, respectively. Following ANN-LM, the best results were obtained with ANFIS-GP, while MLR exhibited the lowest performance.

CONCLUSIONS

Various AI models can effectively predict LOS for patients in different hospital units. Accurate LOS predictions can help health managers allocate resources more equitably across units.

Development of Multiservice Machine Learning Models to Predict Postsurgical Length of Stay and Discharge Disposition at the Time of Case Posting

Objective

Develop machine learning (ML) models to predict postsurgical length of stay (LOS) and discharge disposition (DD) for multiple services with only the data available at the time of case posting.

Background

Surgeries are scheduled largely based on operating room resource availability with little attention to downstream resource availability such as inpatient bed availability and the care needs after hospitalization. Predicting postsurgical LOS and DD at the time of case posting could support resource allocation and earlier discharge planning.

Methods

This retrospective study included 63,574 adult patients undergoing elective inpatient surgery at a large academic health system. We used surgical case data available at the time of case posting and created gradient-boosting decision tree classification models to predict LOS as short (≤1 day), medium (2-4 days), and prolonged stays (≥5 days) and DD as home versus nonhome.

Results

The LOS model achieved an area under the receiver operating characteristic curve (AUC) of 0.81. Adding relative value unit and historical LOS through the similarity cascade increased the accuracy of short and prolonged LOS prediction by 9.0% and 3.9% to 72.9% and 74%, respectively, compared with a model without these features (P = 0.001). The DD model had an AUC of 0.88 for home versus nonhome prediction.

Conclusions

We developed ML models to predict, at the time of case posting, the postsurgical LOS and DD for adult elective inpatient cases across multiple services. These models could support case scheduling, resource allocation, optimal bed utilization, earlier discharge planning, and preventing case cancelation due to bed unavailability.

Ambulatory Surgery Ensemble: Predicting Adult and Pediatric Same-Day Surgery Cases Across Specialties

Objective

To develop an ensemble model using case-posting data to predict which patients could be discharged on the day of surgery.

Background

Few models have predicted which surgeries are appropriate for day cases. Increasing the ratio of ambulatory surgeries can decrease costs and inpatient bed utilization while improving resource utilization.

Methods

Adult and pediatric patients undergoing elective surgery with any surgical specialty in a multisite academic health system from January 2021 to December 2023 were included in this retrospective study. We used surgical case data available at the time of case posting and created 3 gradient-boosting decision tree classification models to predict case length (CL) less than 6 hours, postoperative length of stay (LOS) less than 6 hours, and home discharge disposition (DD). The models were used to develop an ambulatory surgery ensemble (ASE) model to predict same-day surgery (SDS) cases.

Results

The ASE achieved an area under the receiver operating characteristic curve of 0.95 and an average precision of 0.96. In total, 139,593 cases were included, 48,464 of which were in 2023 and were used for model validation. These methods identified that up to 20% of inpatient cases could be moved to SDS and identified which specialties, procedures, and surgeons had the most opportunity to transition cases.

Conclusions

An ensemble model can predict CL, LOS, and DD for elective cases across multiple services and locations at the time of case posting. While limited in its inclusion of patient factors, this model can systematically facilitate clinical operations such as strategic planning, surgical block time, and case scheduling.

Feasibility of forecasting future critical care bed availability using bed management data

OBJECTIVES

This project aims to determine the feasibility of predicting future critical care bed availability using data-driven computational forecast modelling and routinely collected hospital bed management data.

METHODS

In this proof-of-concept, single-centre data informatics feasibility study, regression-based and classification data science techniques were applied retrospectively to prospectively collect routine hospital-wide bed management data to forecast critical care bed capacity. The availability of at least one critical care bed was forecasted using a forecast horizon of 1, 7 and 14 days in advance.

RESULTS

We demonstrated for the first time the feasibility of forecasting critical care bed capacity without requiring detailed patient-level data using only routinely collected hospital bed management data and interpretable models. Predictive performance for bed availability 1 day in the future was better than 14 days (mean absolute error 1.33 vs 1.61 and area under the curve 0.78 vs 0.73, respectively). By analysing feature importance, we demonstrated that the models relied mainly on critical care and temporal data rather than data from other wards in the hospital.

DISCUSSION

Our data-driven forecasting tool only required hospital bed management data to forecast critical care bed availability. This novel approach means no patient-sensitive data are required in the modelling and warrants further work to refine this approach in future bed availability forecast in other hospital wards.

CONCLUSIONS

Data-driven critical care bed availability prediction was possible. Further investigations into its utility in multicentre critical care settings or in other clinical settings are warranted.

Development and validation of a multivariable prediction model in pediatric liver transplant patients for predicting intensive care unit length of stay

BACKGROUND

Liver transplantation is the life-saving treatment for many end-stage pediatric liver diseases. The perioperative course, including surgical and anesthetic factors, have an important influence on the trajectory of this high-risk population. Given the complexity and variability of the immediate postoperative course, there would be utility in identifying risk factors that allow prediction of adverse outcomes and intensive care unit trajectories.

AIMS

The aim of this study was to develop and validate a risk prediction model of prolonged intensive care unit length of stay in the pediatric liver transplant population.

METHODS

This is a retrospective analysis of consecutive pediatric isolated liver transplant recipients at a single institution between April 1, 2013 and April 30, 2020. All patients under the age of 18 years receiving a liver transplant were included in the study (n = 186). The primary outcome was intensive care unit length of stay greater than 7 days.

RESULTS

Recipient and donor characteristics were used to develop a multivariable logistic regression model. A total of 186 patients were included in the study. Using multivariable logistic regression, we found that age < 12 months (odds ratio 4.02, 95% confidence interval 1.20-13.51, p = .024), metabolic or cholestatic disease (odds ratio 2.66, 95% confidence interval 1.01-7.07, p = .049), 30-day pretransplant hospital admission (odds ratio 8.59, 95% confidence interval 2.27-32.54, p = .002), intraoperative red blood cells transfusion >40 mL/kg (odds ratio 3.32, 95% confidence interval 1.12-9.81, p = .030), posttransplant return to the operating room (odds ratio 11.45, 95% confidence interval 3.04-43.16, p = .004), and major postoperative respiratory event (odds ratio 32.14, 95% confidence interval 3.00-343.90, p < .001) were associated with prolonged intensive care unit length of stay. The model demonstrates a good discriminative ability with an area under the receiver operative curve of 0.888 (95% confidence interval, 0.824-0.951).

CONCLUSIONS

We develop and validate a model to predict prolonged intensive care unit length of stay in pediatric liver transplant patients using risk factors from all phases of the perioperative period.

Predicting patient decompensation from continuous physiologic monitoring in the emergency department

Anticipation of clinical decompensation is essential for effective emergency and critical care. In this study, we develop a multimodal machine learning approach to predict the onset of new vital sign abnormalities (tachycardia, hypotension, hypoxia) in ED patients with normal initial vital signs. Our method combines standard triage data (vital signs, demographics, chief complaint) with features derived from a brief period of continuous physiologic monitoring, extracted via both conventional signal processing and transformer-based deep learning on ECG and PPG waveforms. We study 19,847 adult ED visits, divided into training (75%), validation (12.5%), and a chronologically sequential held-out test set (12.5%). The best-performing models use a combination of engineered and transformer-derived features, predicting in a 90-minute window new tachycardia with AUROC of 0.836 (95% CI, 0.800-0.870), new hypotension with AUROC 0.802 (95% CI, 0.747-0.856), and new hypoxia with AUROC 0.713 (95% CI, 0.680-0.745), in all cases significantly outperforming models using only standard triage data. Salient features include vital sign trends, PPG perfusion index, and ECG waveforms. This approach could improve the triage of apparently stable patients and be applied continuously for the prediction of near-term clinical deterioration.

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

BACKGROUND

Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. We hypothesise that blood lactate in paediatric ICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics.

METHODS

Forty-eight post-operative children, median age 4 months (2.9-11.8 interquartile range), mean baseline heart rate of 131 beats per minute (range 33-197), mean lactate level at admission of 22.3 mg/dL (range 6.3-71.1), were included. Morphological arterial waveform characteristics were acquired and analysed. Predicting lactate levels was accomplished using regression-based supervised learning algorithms, evaluated with hold-out cross-validation, including, basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. Algorithms were assessed with mean absolute error, the average of the absolute differences between actual and predicted lactate concentrations. Low values represent superior model performance.

RESULTS

The best performing algorithm was the tuned random forest, which yielded a mean absolute error of 3.38 mg/dL when predicting blood lactate with updated ground truth from the most recent blood draw.

CONCLUSIONS

The random forest is capable of predicting serum lactate levels by analysing perioperative variables, including the arterial pressure waveform. Thus, machine learning can predict patient blood lactate levels, a proxy for haemodynamic instability, non-invasively, continuously and with accuracy that may demonstrate clinical utility.

Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children

Continuous cardiorespiratory physiological monitoring is a cornerstone of care in hospitalized children. The data generated by monitoring devices coupled with machine learning could transform the way we provide care. This scoping review summarizes existing evidence on novel approaches to continuous cardiorespiratory monitoring in hospitalized children. We aimed to identify opportunities for the development of monitoring technology and the use of machine learning to analyze continuous physiological data to improve the outcomes of hospitalized children. We included original research articles published on or after January 1, 2001, involving novel approaches to collect and use continuous cardiorespiratory physiological data in hospitalized children. OVID Medline, PubMed, and Embase databases were searched. We screened 2909 articles and performed full-text extraction of 105 articles. We identified 58 articles describing novel devices or approaches, which were generally small and single-center. In addition, we identified 47 articles that described the use of continuous physiological data in prediction models, but only 7 integrated multidimensional data (e.g., demographics, laboratory results). We identified three areas for development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using continuous cardiorespiratory data. IMPACT: We performed a comprehensive scoping review of novel approaches to capture and use continuous cardiorespiratory physiological data for monitoring, diagnosis, providing care, and predicting events in hospitalized infants and children, from novel devices to machine learning-based prediction models. We identified three key areas for future development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using cardiorespiratory data.

Ensuring Safe Sleep in the Pediatric Intensive Care Unit: A Systematic Review of Informed Development of Clinical Guidelines for Implementation in Practice

INTRODUCTION

Although general safe sleep guidelines have been established, their utility and implementation have yet to be examined systematically for inpatient populations for application to infants in the pediatric intensive care unit (PICU) setting. This study aimed to complete a systematic review of inpatient safe sleep practices studies to develop then safe sleep clinical guidelines for implementation in the care of medically complex infants in the PICU.

METHOD

This review was registered with PROSPERO and adheres to Preferred Reporting Items for Systematic reviews and Meta-Analyses systematic review guidelines.

RESULTS

Nineteen articles met the inclusion criteria. A safe sleep guideline algorithm for medically complex infants in the PICU was created for implementation.

DISCUSSION

Consistent and comprehensive safe sleep education and modeling by health care professionals in the inpatient setting is an effective technique to reduce the risk of harm and promote safe sleep behaviors in the home setting.

Development and Comparative Performance of Physiologic Monitoring Strategies in the Emergency Department

IMPORTANCE

Accurate and timely documentation of vital signs affects all aspects of triage, diagnosis, and management. The adequacy of current patient monitoring practices and the potential to improve on them are poorly understood.

OBJECTIVE

To develop measures of fit between documented and actual patient vital signs throughout the visit, as determined from continuous physiologic monitoring, and to compare the performance of actual practice with alternative patient monitoring strategies.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study evaluated 25 751 adult visits to continuously monitored emergency department (ED) beds between August 1, 2020, and December 31, 2021. A series of monitoring strategies for the documentation of vital signs (heart rate [HR], respiratory rate [RR], oxygen saturation by pulse oximetry [Spo2], mean arterial pressure [MAP]) was developed and the strategies' ability to capture physiologic trends and vital sign abnormalities simulated. Strategies included equal spacing of charting events, charting at variable intervals depending on the last observed values, and discrete optimization of charting events.

MAIN OUTCOMES AND MEASURES

Coverage was defined as the proportion of monitor-derived vital sign measurements (at 1-minute resolution) that fall within the bounds of nursing-charted values over the course of an ED visit (HR ± 5 beats/min, RR ± 3 breaths/min, Spo2 ± 2%, and MAP ± 6 mm Hg). Capture was defined as the documentation of a vital sign abnormality detected by bedside monitor (tachycardia [HR >100 beats/min], bradycardia [HR <60 beats/min], hypotension [MAP <65 mm Hg], and hypoxia [Spo2 <95%]).

RESULTS

Median patient age was 60 years (IQR, 43-75 years), and 13 329 visits (51.8%) were by women. Monitored visits had a median of 4 (IQR, 2-5) vital sign charting events per visit. Compared with actual practice, a simple rule, which observes vital signs more frequently if the last observation fell outside the bounds of the previous values, and using the same number of observations as actual practice, produced relative coverage improvements of 31.5% (95% CI, 30.5%-32.5%) for HR, 31.0% (95% CI, 30.0%-32.0%) for MAP, 16.8% (95% CI, 16.0%-17.6%) for RR, and 7.8% (95% CI, 7.3%-8.3%) for Spo2. The same strategy improved capture of abnormalities by 38.9% (95% CI, 26.8%-52.2%) for tachycardia, 38.1% (95% CI, 29.0%-47.9%) for bradycardia, 39.0% (95% CI, 24.2%-55.7%) for hypotension, and 123.1% (95% CI, 110.7%-136.3%) for hypoxia. Analysis of optimal coverage suggested an additional scope for improvement through more sophisticated strategies.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, actual documentation of ED vital signs was variable and incomplete, missing important trends and abnormalities. Alternative monitoring strategies may improve on current practice without increasing the overall frequency of patient monitoring.

Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury

STUDY DESIGN

A retrospective cohort study.

OBJECTIVE

The objective of the study was to develop machine-learning (ML) classifiers for predicting prolonged intensive care unit (ICU)-stay and prolonged hospital-stay for critical patients with spinal cord injury (SCI).

SUMMARY OF BACKGROUND DATA

Critical patients with SCI in ICU need more attention. SCI patients with prolonged stay in ICU usually occupy vast medical resources and hinder the rehabilitation deployment.

METHODS

A total of 1599 critical patients with SCI were included in the study and labeled with prolonged stay or normal stay. All data were extracted from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care III-IV Database. The extracted data were randomly divided into training, validation and testing (6:2:2) subdatasets. A total of 91 initial ML classifiers were developed, and the top three initial classifiers with the best performance were further stacked into an ensemble classifier with logistic regressor. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicting outcome was prolonged ICU-stay, while the secondary predicting outcome was prolonged hospital-stay.

RESULTS

In predicting prolonged ICU-stay, the AUC of the ensemble classifier was 0.864 ± 0.021 in the three-time five-fold cross-validation and 0.802 in the independent testing. In predicting prolonged hospital-stay, the AUC of the ensemble classifier was 0.815 ± 0.037 in the three-time five-fold cross-validation and 0.799 in the independent testing. Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top three initial classifiers varied a lot in either predicting prolonged ICU-stay or discriminating prolonged hospital-stay.

CONCLUSION

The ensemble classifiers successfully predict the prolonged ICU-stay and the prolonged hospital-stay, which showed a high potential of assisting physicians in managing SCI patients in ICU and make full use of medical resources.Level of Evidence: 3.

State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review

Background

Modern clinical care in intensive care units is full of rich data, and machine learning has great potential to support clinical decision-making. The development of intelligent machine learning–based clinical decision support systems is facing great opportunities and challenges. Clinical decision support systems may directly help clinicians accurately diagnose, predict outcomes, identify risk events, or decide treatments at the point of care.

Objective

We aimed to review the research and application of machine learning–enabled clinical decision support studies in intensive care units to help clinicians, researchers, developers, and policy makers better understand the advantages and limitations of machine learning–supported diagnosis, outcome prediction, risk event identification, and intensive care unit point-of-care recommendations.

Methods

We searched papers published in the PubMed database between January 1980 and October 2020. We defined selection criteria to identify papers that focused on machine learning–enabled clinical decision support studies in intensive care units and reviewed the following aspects: research topics, study cohorts, machine learning models, analysis variables, and evaluation metrics.

Results

A total of 643 papers were collected, and using our selection criteria, 97 studies were found. Studies were categorized into 4 topics—monitoring, detection, and diagnosis (13/97, 13.4%), early identification of clinical events (32/97, 33.0%), outcome prediction and prognosis assessment (46/97, 47.6%), and treatment decision (6/97, 6.2%). Of the 97 papers, 82 (84.5%) studies used data from adult patients, 9 (9.3%) studies used data from pediatric patients, and 6 (6.2%) studies used data from neonates. We found that 65 (67.0%) studies used data from a single center, and 32 (33.0%) studies used a multicenter data set; 88 (90.7%) studies used supervised learning, 3 (3.1%) studies used unsupervised learning, and 6 (6.2%) studies used reinforcement learning. Clinical variable categories, starting with the most frequently used, were demographic (n=74), laboratory values (n=59), vital signs (n=55), scores (n=48), ventilation parameters (n=43), comorbidities (n=27), medications (n=18), outcome (n=14), fluid balance (n=13), nonmedicine therapy (n=10), symptoms (n=7), and medical history (n=4). The most frequently adopted evaluation metrics for clinical data modeling studies included area under the receiver operating characteristic curve (n=61), sensitivity (n=51), specificity (n=41), accuracy (n=29), and positive predictive value (n=23).

Conclusions

Early identification of clinical and outcome prediction and prognosis assessment contributed to approximately 80% of studies included in this review. Using new algorithms to solve intensive care unit clinical problems by developing reinforcement learning, active learning, and time-series analysis methods for clinical decision support will be greater development prospects in the future.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models

Background: Early prediction of the clinical outcome of patients with sepsis is of great significance and can guide treatment and reduce the mortality of patients. However, it is clinically difficult for clinicians. Methods: A total of 2,224 patients with sepsis were involved over a 3-year period (2016-2018) in the intensive care unit (ICU) of Peking Union Medical College Hospital. With all the key medical data from the first 6 h in the ICU, three machine learning models, logistic regression, random forest, and XGBoost, were used to predict mortality, severity (sepsis/septic shock), and length of ICU stay (LOS) (>6 days, ≤ 6 days). Missing data imputation and oversampling were completed on the dataset before introduction into the models. Results: Compared to the mortality and LOS predictions, the severity prediction achieved the best classification results, based on the area under the operating receiver characteristics (AUC), with the random forest classifier (sensitivity = 0.65, specificity = 0.73, F1 score = 0.72, AUC = 0.79). The random forest model also showed the best overall performance (mortality prediction: sensitivity = 0.50, specificity = 0.84, F1 score = 0.66, AUC = 0.74; LOS prediction: sensitivity = 0.79, specificity = 0.66, F1 score = 0.69, AUC = 0.76) among the three models. The predictive ability of the SOFA score itself was inferior to that of the above three models. Conclusions: Using the random forest classifier in the first 6 h of ICU admission can provide a comprehensive early warning of sepsis, which will contribute to the formulation and management of clinical decisions and the allocation and management of resources.

Machine learning in patient flow: a review

This work is a review of the ways in which machine learning has been used in order to plan, improve or aid the problem of moving patients through healthcare services. We decompose the patient flow problem into four subcategories: prediction of demand on a healthcare institution, prediction of the demand and resource required to transfer patients from the emergency department to the hospital, prediction of potential resource required for the treatment and movement of inpatients and prediction of length-of-stay and discharge timing. We argue that there are benefits to both approaches of considering the healthcare institution as a whole as well as the patient by patient case and that ideally a combination of these would be best for improving patient flow through hospitals. We also argue that it is essential for there to be a shared dataset that will allow researchers to benchmark their algorithms on and thereby allow future researchers to build on that which has already been done. We conclude that machine learning for the improvement of patient flow is still a young field with very few papers tailor-making machine learning methods for the problem being considered. Future works should consider the need to transfer algorithms trained on a dataset to multiple hospitals and allowing for dynamic algorithms which will allow real-time decision-making to help clinical staff on the shop floor.