Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: a systematic review

Background: Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts. Methods: Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias. Results: More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment. Discussion: Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare

Use of Artificial Intelligence in Early Warning Score in Critical ill Patients: Scoping Review

Early Warning Score (EWS) systems can identify critical patients through the application of artificial intelligence (AI). Physiological parameters like blood pressure, body temperature, heart rate, and respiration rate are encompassed in the EWS. One of AI's advantages is its capacity to recognize high-risk individuals who need emergency medical attention because they are at risk of organ failure, heart attack, or even death. The objective of this study is to review the body of research on the use of AI in EWS to accurately predict patients who will become critical. The analysis model of Arksey and O'Malley is employed in this study. Electronic databases such as ScienceDirect, Scopus, PubMed, and SpringerLink were utilized in a methodical search. Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA SR) guidelines were utilized in the creation and selection of the literature. This analysis included a total of 14 articles. This article summarizes the findings on several aspects: the usefulness of AI algorithms in EWS for critical patients, types of AI algorithm models, and the accuracy of AI in predicting the quality of life of patients in EWS. The results of this review show that the integration of AI into EWS can increase accuracy in predicting patients in critical condition, including cardiac arrest, sepsis, and ARDS events that cause inhalation until the patient dies. The AI models that are often used are machine learning and deep learning models because they are considered to perform better and achieve high accuracy. The importance of further research is to identify the application of AI with EWS in critical care patients by adding laboratory result parameters and pain scales to increase prediction accuracy to obtain optimal results

Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

BACKGROUND: Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. METHODS: We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. RESULTS: Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. CONCLUSIONS: We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting

Application of Hierarchical Temporal Memory to Anomaly Detection of Vital Signs for Ambient Assisted Living

This thesis presents the development of a framework for anomaly detection of vital signs for an Ambient Assisted Living (AAL) health monitoring scenario. It is driven by spatiotemporal reasoning of vital signs that Cortical Learning Algorithms (CLA) based on Hierarchal Temporal Memory (HTM) theory undertakes in an AAL health monitoring scenario to detect anomalous data points preceding cardiac arrest. This thesis begins with a literature review on the existing Ambient intelligence (AmI) paradigm, AAL technologies and anomaly detection algorithms used in a health monitoring scenario. The research revealed the significance of the temporal and spatial reasoning in the vital signs monitoring as the spatiotemporal patterns of vital signs provide a basis to detect irregularities in the health status of elderly people. The HTM theory is yet to be adequately deployed in an AAL health monitoring scenario. Hence HTM theory, network and core operations of the CLA are explored. Despite the fact that standard implementation of the HTM theory comprises of a single-level hierarchy, multiple vital signs, specifically the correlation between them is not sufficiently considered. This insufficiency is of particular significance considering that vital signs are correlated in time and space, which are used in the health monitoring applications for diagnosis and prognosis tasks. This research proposes a novel framework consisting of multi-level HTM networks. The lower level consists of four models allocated to the four vital signs, Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Heart Rate (HR) and peripheral capillary oxygen saturation (SpO2) in order to learn the spatiotemporal patterns of each vital sign. Additionally, a higher level is introduced to learn spatiotemporal patterns of the anomalous data point detected from the four vital signs. The proposed hierarchical organisation improves the model’s performance by using the semantically improved representation of the sensed data because patterns learned at each level of the hierarchy are reused when combined in novel ways at higher levels. To investigate and evaluate the performance of the proposed framework, several data selection techniques are studied, and accordingly, a total record of 247 elderly patients is extracted from the MIMIC-III clinical database. The performance of the proposed framework is evaluated and compared against several state-of-the-art anomaly detection algorithms using both online and traditional metrics. The proposed framework achieved 83% NAB score which outperforms the HTM and k-NN algorithms by 15%, the HBOS and INFLO SVD by 16% and the k-NN PCA by 21% while the SVM scored 34%. The results prove that multiple HTM networks can achieve better performance when dealing with multi-dimensional data, i.e. data collected from more than one source/sensor

Digital early warning scores in cardiac care settings: Mixed-methods research

The broad adoption of the National Early Warning Score (NEWS2) was formally endorsed for prediction of early deterioration across all settings. With current digitalisation of the Early Warning Score (EWS) through electronic health records (EHR) and automated patient monitoring, there is an excellent opportunity for facilitating and evaluating NEWS2 implementation. However, no evidence yet shows the success of such standardisation or digitalisation of EWS in cardiac care settings. Individuals with cardiovascular disease (CVD) have a significant risk of developing critical events, and CVD-related morbidity is a critical burden for health and social care. However, there is a gap in research evaluating the performance and implementation of EWS in cardiac settings and the role of digital solutions in the implementation and performance of EWS and clinicians' practice. This PhD aims to provide high-quality evidence on the effectiveness of NEWS2 in predicting worsening events in patients with CVD, the implementation of the digital NEWS2 in two healthcare settings, the experience of escalation of care during the COVID-19 pandemic, and the evaluation of EHR-integrated dashboard for auditing NEWS2 and clinicians' performance

ICU prognostication: Time to re-evaluate? Register-based studies on improving prognostication for patients admitted to the intensive care unit (ICU)

Background: ICU prognostication is difficult because of patients’ prior comorbidities and their varied reasons for admission. The model used for ICU prognostication in Sweden is the Simplified Acute Physiology Score 3 (SAPS 3), which uses information gathered within one hour of ICU admission to predict 30-day mortality. Since the SAPS 3 model was introduced, no biomarkers have been added to it to improve its prognostic performance. For comatose patients admitted to the ICU after cardiac arrest, the prognostication performed after 72 h will either result in the continued observation of the patient or the withdrawal of life-sustaining treatment.Purpose: 1) To investigate whether adding the biomarker lactate (study I) or high-sensitivity troponin T (hsTnT) (study II) to SAPS 3 adds prognostic value. 2) To investigate whether using a supervised machine learning algorithm called artificial neural networks (ANNs) can improve the prognostic performance of SAPS 3 (study III). 3) To explore whether ANNs can create reliable predictions for comatose patients at the time of hospital admission (study IV) and during the first three days after ICU admission, with or without promising biomarkers (study V).Methods: 1) To investigate whether the laboratory values of lactate or hsTnT could improve the performance of SAPS 3, we combined patients’ laboratory values on ICU admission at Skåne University Hospital with their SAPS 3 score. 2) Based on all first-time ICU admissions in Sweden from 2009–2017 as retrieved from the Swedish Intensive Care Registry (SIR), we investigated whether ANNs could improve SAPS 3 using the same variables. 3) All out-of-hospital cardiac arrest (OHCA) patients from the Target Temperature Management trial were included for data analysis. Background and prehospital data, along with clinical variables at admission, were used in study IV. Clinical variables from the first three days were used in study V along with different levels of biomarkers defined as clinically accessible (e.g. neuron-specific enolase, or NSE) and research-grade biomarkers (e.g. neurofilament light, or NFL). Patient outcome was the dichotomised Cerebral Performance Category scale (CPC); a CPC of 1–2 was considered a good outcome, and a CPC of 3–5 was considered a poor outcome.Results: 1) Both lactate and hsTnT were independent SAPS 3 predictors for 30-day mortality in the logistic regression model. In a subgroup analysis, the use of lactate improved the area under the receiver operating characteristic curve (AUROC) for cardiac arrest and septic patients, and the use of hsTnT improved the AUROC for septic patients. 2) The overall performance of the SAPS 3 model in Sweden was improved by the use of ANNs. Both the discrimination (AUROC 0.89 vs 0.85, p < 0.001) and the calibration were improved when the two models were compared on a separate test set (n = 36,214). 3) An ANN model outperformed a logistic-regression-based model in predicting poor outcome on hospital admission for OHCA patients. Incorporating biomarkers such as NSE improved the AUROC over the course of the first three days of the ICU stay; when NFL was incorporated, the prognostic performance was excellent from day 1.Conclusion: Lactate and hsTnT probably add prognostic value to SAPS 3 for patients admitted to the ICU with sepsis or after cardiac arrest (lactate only). An ANN model was found to be superior to the SAPS 3 model (Swedish modification) and corrected better for age than SAPS 3. A simplified ANN model with eight variables showed performance similar to that of the SAPS 3 model. For comatose OHCA patients, an ANN model improved the accuracy of the prediction of the long-term neurological outcome at hospital admission. Furthermore, when it used cumulative information from the first three days after ICU admission, an ANN model showed promising prognostic performance on day 3 when it incorporated clinically accessible biomarkers such as NSE, and it showed promising performance on days 1–3 when it incorporated research-grade biomarkers such as NFL

Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients

This paper presents a novel risk stratification method using extreme learning machine (ELM). ELM was integrated into a scoring system to identify the risk of cardiac arrest in emergency department (ED) patients. The experiments were conducted on a cohort of 1025 critically ill patients presented to the ED of a tertiary hospital. ELM and voting based ELM (V-ELM) were evaluated. To enhance the prediction performance, we proposed a selective V-ELM (SV-ELM) algorithm. The results showed that ELM based scoring methods outperformed support vector machine (SVM) based scoring method in the receiver operation characteristic analysis

The Significance of Machine Learning in Clinical Disease Diagnosis: A Review

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD). The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics.Comment: 8 page

A Systematic Approach to Manage Clinical Deterioration on Inpatient Units in the Health Care System

The transformation of health care delivery in the United States is accelerating at unbelievable speed. The acceleration is a result of many variables including health care reform as well as the covariation occurring with adjustments in regulations related to resident work hours. The evolving care delivery model has exposed a vulnerability of the health system, specifically in academic medical centers of the United States. Academic medical centers have established a care delivery model grounded and predicated in resident presence and performance. With changes in resident work expectations and reduced time spent in hospitals, an urgent need exists to evaluate and recreate a model of care that produces quality outcomes in an efficient, service driven organization. One potential care model that would stabilize organizations is infusion of APNs with the expanded skills and knowledge to instill practice continuity in the critical care environment. A Medicare demonstration project is proposed for funding an APN expanded role and alteration in the care delivery model. Formative and summative evaluation and impact of such an expanded practice role is included in the proposed project. An evolved partnership between the advanced practice nurse and physician will serve to fill some of the gap currently existing in the delivery system of today. As the complexity and acuity of the patients in the hospital escalates, innovation is demanded to ensure a care model that will foster achievement of the quality outcomes expected and deserved