Using machine learning to improve bovine tuberculosis control in herd level outbreaks.

Bovine tuberculosis (bTB), a chronic disease of cattle, is caused by the Mycobacterium bovis infection. Despite having a serious social and economic impact in the United Kingdom and Ireland, there is no antemortem gold standard diagnostic test. Tuberculin skin tests (CICT) are commonly used as a control measure with the interferon gamma (IFN-γ) assay being applied in certain circumstances. This paper utilizes data gathered describing tuberculin regression in reactors (test positive cattle) following the CICT at 72 ± 4 h post injection in herds with large bTB outbreaks. The work then applies machine learning techniques (Decision Trees, Bagging Trees and Random Forests, alongside several balancing approaches) to predict which cattle were likely to be truly infected with tuberculosis, enabling identification of atypical breakdowns that require extra investigation and providing a mechanism for quality assurance of the existing CICT bTB surveillance scheme. The analysis showed that Random Forests (RF) trained using SMOTE balancing had the joint best performance and accuracy (0.90). The importance of the two components of the interferon gamma assay within the RF model also indicated that varying the assay threshold for large outbreaks would be beneficial. Furthermore, the combined use of the RF and IFN- γ models could lead to the improved detection of infection within breakdown herds, reducing the scale and duration of outbreaks. An additional use of these models would be for quality assuring the current bTB surveillance based on CICT and post mortem inspection. Quality control is well recognized as an essential component of a disease surveillance/eradication programme.Clinical Relevance- Bovine tuberculosis remains a disease that is hard to control on a national level. The use of the machine learning model could lead to significant improved detection of infection within breakdown herds, reducing the scale and duration of outbreaks. Advanced modelling, such as this, has the potential to strengthen the efficacy of disease surveillance and the eradication strategy and can meaningfully contribute to animal disease national control plans.

A Biological Approach to Building Resilience and Wellness Capacity Among Police Exposed to Posttraumatic Stress Injuries: Protocol for a Randomized Controlled Trial.

BACKGROUND: Law enforcement officers are routinely exposed to hazardous, disturbing events that can impose severe stress and long-term psychological trauma. As a result, police and other public safety personnel (PSP) are at increased risk of developing posttraumatic stress injuries (PTSIs) and disruptions to the autonomic nervous system (ANS). ANS functioning can be objectively and noninvasively measured by heart rate (HR), heart rate variability (HRV), and respiratory sinus arrhythmia (RSA). Traditional interventions aimed at building resilience among PSP have not adequately addressed the physiological ANS dysregulations that lead to mental and physical health conditions, as well as burnout and fatigue following potential psychological trauma. OBJECTIVE: In this study, we will investigate the efficacy of a web-based Autonomic Modulation Training (AMT) intervention on the following outcomes: (1) reducing self-reported symptoms of PTSI, (2) strengthening ANS physiological resilience and wellness capacity, and (3) exploring how sex and gender are related to baseline differences in psychological and biological PTSI symptoms and response to the AMT intervention. METHODS: The study is comprised of 2 phases. Phase 1 involves the development of the web-based AMT intervention, which includes 1 session of baseline survey measures, 6 weekly sessions that integrate HRV biofeedback (HRVBF) training with meta-cognitive skill practice, and 1 session of follow-up survey measures. Phase 2 will use a cluster randomized control design to test the effectiveness of AMT on the following prepost outcomes: (1) self-report symptoms of PTSI and other wellness measures; (2) physiological indicators of health and resilience including resting HR, HRV, and RSA; and (3) the influence of sex and gender on other outcomes. Participants will be recruited for an 8-week study across Canada in rolling cohorts. RESULTS: The study received grant funding in March 2020 and ethics approval in February 2021. Due to delays related to COVID-19, phase 1 was completed in December 2022, and phase 2 pilot testing began in February 2023. Cohorts of 10 participants in the experimental (AMT) and control (prepost assessment only) groups will continue until a total of 250 participants are tested. Data collection from all phases is expected to conclude in December 2025 but may be extended until the intended sample size is reached. Quantitative analyses of psychological and physiological data will be conducted in conjunction with expert coinvestigators. CONCLUSIONS: There is an urgent need to provide police and PSP with effective training that improves physical and psychological functioning. Given that help-seeking for PTSI is reduced among these occupational groups, AMT is a promising intervention that can be completed in the privacy of one's home. Importantly, AMT is a novel program that uniquely addresses the underlying physiological mechanisms that support resilience and wellness promotion and is tailored to the occupational demands of PSP. TRIAL REGISTRATION: ClinicalTrials.gov NCT05521360; https://clinicaltrials.gov/ct2/show/NCT05521360. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/33492.

Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach.

BACKGROUND: Wait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers. OBJECTIVE: The first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system's knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers). METHODS: We analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system's knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments. RESULTS: The average wait time varied widely between different types of mental health clinics. For more than half of the clinics, the average wait time was longer than 3 months. The number of scheduled appointments and the rate of no-shows varied widely among clinics. Despite these variations, the random forest method provided the minimum root mean square error values for 4 of the 8 clinics, and the second minimum root mean square error for the other 4 clinics. Utilizing the system's knowledge increased the utility of our highly deidentified data and improved the predictive power of the models. CONCLUSIONS: The random forest method, enhanced with the system's knowledge, provided reliable wait time predictions for new outpatients, regardless of low utility of the highly deidentified input data and the high variation in wait times across different clinics and patient types. The priority system was identified as a factor that contributed to long wait times, and a fast-track system was suggested as a potential solution.

A Delphi Survey of Canadian Respiratory Therapists' Practice Statements on Pediatric Mechanical Ventilation.

BACKGROUND: Pediatric mechanical ventilation practice guidelines are not well established; therefore, the European Society for Paediatric and Neonatal Intensive Care (ESPNIC) developed consensus recommendations on pediatric mechanical ventilation management in 2017. However, the guideline's applicability in different health care settings is unknown. This study aimed to determine the consensus on pediatric mechanical ventilation practices from Canadian respiratory therapists' (RTs) perspectives and consensually validate aspects of the ESPNIC guideline. METHODS: A 3-round modified electronic Delphi survey was conducted; contents were guided by ESPNIC. Participants were RTs with at least 5 years of experience working in standalone pediatric ICUs or units with dedicated pediatric intensive care beds across Canada. Round 1 collected open-text feedback, and subsequent rounds gathered feedback using a 6-point Likert scale. Consensus was defined as ≥ 75% agreement; if consensus was unmet, statements were revised for re-ranking in the subsequent round. RESULTS: Fifty-two RTs from 14 different pediatric facilities participated in at least one of the 3 rounds. Rounds 1, 2, and 3 had a response rate of 80%, 93%, and 96%, respectively. A total of 59 practice statements achieved consensus by the end of round 3, categorized into 10 sections: (1) noninvasive ventilation and high-flow oxygen therapy, (2) tidal volume and inspiratory pressures, (3) breathing frequency and inspiratory times, (4) PEEP and FIO2 , (5) advanced modes of ventilation, (6) weaning, (7) physiological targets, (8) monitoring, (9) general, and (10) equipment adjuncts. Cumulative text feedback guided the formation of the clinical remarks to supplement these practice statements. CONCLUSIONS: This was the first study to survey RTs for their perspectives on the general practice of pediatric mechanical ventilation management in Canada, generally aligning with the ESPNIC guideline. These practice statements considered information from health organizations and institutes, supplemented with clinical remarks. Future studies are necessary to verify and understand these practices' effectiveness.

Silver linings: Observed reductions in aggression and use of restraints and seclusion in psychiatric inpatient care during COVID-19.

WHAT IS KNOWN ABOUT THE SUBJECT?: In a survey conducted by the World Health Organization (WHO) in the summer of 2020, 93% of countries worldwide acknowledged negative impacts on their mental health services. Previous research during the H1N1 pandemic in 2009 established an increase of patient aggression in psychiatric facilities. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: Despite expected worsening of mental health, our hospital observed reductions in aggressive behaviour among inpatients and subsequent use of coercive interventions by staff in the months following Covid-19 pandemic restrictions being implemented. The downward trend in incidents observed during the pandemic has suggested that aggression in mental health hospitals may be more situation-specific and less so a factor of mental illness. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: We believe that the reduction in aggressive behaviour observed during the pandemic is related to changes in our organization that occurred in response to concerns about patient well-being; our co-design approach shifted trust, choice and power. Therefore, practices that support these constructs are needed to maintain the outcomes we experienced. Rather than return to normal in the wake of the pandemic, we are strongly encouraged to sustain the changes we made and continue to find better ways to support and work with the individuals who rely on or use our services. ABSTRACT: The global COVID-19 pandemic has dramatically changed the operation of health care such that many services were put on hold as patients were triaged differently, people delayed seeking care, and transition to virtual care was enacted, including in psychiatric facilities. Most of the media dialogue has been negative; however, there have been some silver linings observed. Coinciding with the pandemic has been a reduction in aggressive incidents at our psychiatric hospital, along with the decreased need to use restraints and seclusion to manage behaviour. In this paper, we are taking stock of the changes that have occurred in response to the pandemic in an attempt to share our learnings and offer suggestions so that health care does not necessarily return to "normal".

Antenatal Care in Australia: Process Mapping to Visualise Resources and Care.

Antenatal Care (ANC) in Australia is of a high standard internationally and is an important care model for mothers. ANC is able to help prevent preterm birth complications. Process mapping enables the visualization of the journal of care, however different functionality is available from different process mapping tools. This paper presents and critically analyses Lean VSM and PaJMa modelling for the ANC pathway in Australia.Clinical Relevance-This work can help inform discrepancies in perceived care and received care and can be used as a tool to help guide organizations in the decision-making for health services deployments for ANC services.

Health Analytics as a Service with Artemis Cloud: Service Availability.

Critical care units internationally contain medical devices that generate Big Data in the form of high speed physiological data streams. Great opportunities exist for systemic and reliable approaches for the analysis of high speed physiological data for clinical decision support. This paper presents the instantiation of a Big Data analytics based Health Analytics as-a-Service model. The availability results of the deployment of two instances of Artemis Cloud to support two neonatal ICUs (NICUs) in Ontario Canada are presented.

Adaptive API for Real-Time Streaming Analytics as a Service.

A significant amount of physiological data is generated from bedside monitors and sensors in neonatal intensive units (NICU) every second, however facilitating the ingestion of such data into multiple analytical processes in a real time streaming architecture remains a central challenge for systems that seek effective scaling of real-time data streams. In this paper we demonstrate an adaptive streaming application program interface (API) that provides real time streams of data for consumption by multiple analytics services enabling real-time exploration and knowledge discovery from live data streams. We have designed, developed and evaluated an adaptive API with multiple ingestion of data streamed out of bedside monitors that is passed to a middleware for standardization and structuring and finally distributed as a service for multiple analytical services to consume and perform further processing. This approach allows, (a) multiple applications to process the same data streams using multiple algorithms, (b) easy scalability to manage diverse data streams, (c) processing of analytics for each patient monitored at the NICU, (d) ability to integrate analytics that seek to evaluate multiple patients at the same point in time, and (e) a robust automated process with no manual interruptions that effectively adapts to changing data volumes when bedside monitors increases or the amount of data emitted by a monitor changes. The proposed architecture has been instantiated within the Artemis Platform which provides a framework for real-time high speed physiological data collection from multiple and diverse bed side monitors and sensors in NICUs from multiple hospitals. Results indicate this is a robust approach that can scale effectively as data volumes increase or data sources change.

A Review of Visual Representations of Physiologic Data.

BACKGROUND: Physiological data is derived from electrodes attached directly to patients. Modern patient monitors are capable of sampling data at frequencies in the range of several million bits every hour. Hence the potential for cognitive threat arising from information overload and diminished situational awareness becomes increasingly relevant. A systematic review was conducted to identify novel visual representations of physiologic data that address cognitive, analytic, and monitoring requirements in critical care environments. OBJECTIVE: The aims of this review were to identify knowledge pertaining to (1) support for conveying event information via tri-event parameters; (2) identification of the use of visual variables across all physiologic representations; (3) aspects of effective design principles and methodology; (4) frequency of expert consultations; (5) support for user engagement and identifying heuristics for future developments. METHODS: A review was completed of papers published as of August 2016. Titles were first collected and analyzed using an inclusion criteria. Abstracts resulting from the first pass were then analyzed to produce a final set of full papers. Each full paper was passed through a data extraction form eliciting data for comparative analysis. RESULTS: In total, 39 full papers met all criteria and were selected for full review. Results revealed great diversity in visual representations of physiological data. Visual representations spanned 4 groups including tabular, graph-based, object-based, and metaphoric displays. The metaphoric display was the most popular (n=19), followed by waveform displays typical to the single-sensor-single-indicator paradigm (n=18), and finally object displays (n=9) that utilized spatiotemporal elements to highlight changes in physiologic status. Results obtained from experiments and evaluations suggest specifics related to the optimal use of visual variables, such as color, shape, size, and texture have not been fully understood. Relationships between outcomes and the users' involvement in the design process also require further investigation. A very limited subset of visual representations (n=3) support interactive functionality for basic analysis, while only one display allows the user to perform analysis including more than one patient. CONCLUSIONS: Results from the review suggest positive outcomes when visual representations extend beyond the typical waveform displays; however, there remain numerous challenges. In particular, the challenge of extensibility limits their applicability to certain subsets or locations, challenge of interoperability limits its expressiveness beyond physiologic data, and finally the challenge of instantaneity limits the extent of interactive user engagement.

An Evaluation of Understandability of Patient Journey Models in Mental Health.

BACKGROUND: There is a significant trend toward implementing health information technology to reduce administrative costs and improve patient care. Unfortunately, little awareness exists of the challenges of integrating information systems with existing clinical practice. The systematic integration of clinical processes with information system and health information technology can benefit the patients, staff, and the delivery of care. OBJECTIVES: This paper presents a comparison of the degree of understandability of patient journey models. In particular, the authors demonstrate the value of a relatively new patient journey modeling technique called the Patient Journey Modeling Architecture (PaJMa) when compared with traditional manufacturing based process modeling tools. The paper also presents results from a small pilot case study that compared the usability of 5 modeling approaches in a mental health care environment. METHOD: Five business process modeling techniques were used to represent a selected patient journey. A mix of both qualitative and quantitative methods was used to evaluate these models. Techniques included a focus group and survey to measure usability of the various models. RESULTS: The preliminary evaluation of the usability of the 5 modeling techniques has shown increased staff understanding of the representation of their processes and activities when presented with the models. Improved individual role identification throughout the models was also observed. The extended version of the PaJMa methodology provided the most clarity of information flows for clinicians. CONCLUSIONS: The extended version of PaJMa provided a significant improvement in the ease of interpretation for clinicians and increased the engagement with the modeling process. The use of color and its effectiveness in distinguishing the representation of roles was a key feature of the framework not present in other modeling approaches. Future research should focus on extending the pilot case study to a more diversified group of clinicians and health care support workers.

Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework.

BACKGROUND: Analytics-as-a-service (AaaS) is one of the latest provisions emerging from the cloud services family. Utilizing this paradigm of computing in health informatics will benefit patients, care providers, and governments significantly. This work is a novel approach to realize health analytics as services in critical care units in particular. OBJECTIVE: To design, implement, evaluate, and deploy an extendable big-data compatible framework for health-analytics-as-a-service that offers both real-time and retrospective analysis. METHODS: We present a novel framework that can realize health data analytics-as-a-service. The framework is flexible and configurable for different scenarios by utilizing the latest technologies and best practices for data acquisition, transformation, storage, analytics, knowledge extraction, and visualization. We have instantiated the proposed method, through the Artemis project, that is, a customization of the framework for live monitoring and retrospective research on premature babies and ill term infants in neonatal intensive care units (NICUs). RESULTS: We demonstrated the proposed framework in this paper for monitoring NICUs and refer to it as the Artemis-In-Cloud (Artemis-IC) project. A pilot of Artemis has been deployed in the SickKids hospital NICU. By infusing the output of this pilot set up to an analytical model, we predict important performance measures for the final deployment of Artemis-IC. This process can be carried out for other hospitals following the same steps with minimal effort. SickKids' NICU has 36 beds and can classify the patients generally into 5 different types including surgical and premature babies. The arrival rate is estimated as 4.5 patients per day, and the average length of stay was calculated as 16 days. Mean number of medical monitoring algorithms per patient is 9, which renders 311 live algorithms for the whole NICU running on the framework. The memory and computation power required for Artemis-IC to handle the SickKids NICU will be 32 GB and 16 CPU cores, respectively. The required amount of storage was estimated as 8.6 TB per year. There will always be 34.9 patients in SickKids NICU on average. Currently, 46% of patients cannot get admitted to SickKids NICU due to lack of resources. By increasing the capacity to 90 beds, all patients can be accommodated. For such a provisioning, Artemis-IC will need 16 TB of storage per year, 55 GB of memory, and 28 CPU cores. CONCLUSIONS: Our contributions in this work relate to a cloud architecture for the analysis of physiological data for clinical decisions support for tertiary care use. We demonstrate how to size the equipment needed in the cloud for that architecture based on a very realistic assessment of the patient characteristics and the associated clinical decision support algorithms that would be required to run for those patients. We show the principle of how this could be performed and furthermore that it can be replicated for any critical care setting within a tertiary institution.

Streaming Physiological Data: General Public Perceptions of Secondary Use and Application to Research in Neonatal Intensive Care.

High speed physiological data represents one of the most untapped resources in healthcare today and is a form of Big Data. Physiological data is captured and displayed on a wide range of devices in healthcare environments. Frequently this data is transitory and lost once initially displayed. Researchers wish to store and analyze these datasets, however, there is little evidence of any engagement with citizens regarding their perceptions of physiological data capture for secondary use. This paper presents the findings of a self-administered household survey (n=165, response rate = 34%) that investigated Australian and Canadian citizens' perceptions of such physiological data capture and re-use. Results indicate general public support for the secondary use of physiological streaming data. Discussion considers the potential application of such data in neonatal intensive care contexts in relation to our Artemis research. Consideration of the perceptions of secondary use of the streaming data as early as possible will assist in building appropriate use models, with a focus on parents in the neonatal context.

Health Informatics for Neonatal Intensive Care Units: An Analytical Modeling Perspective.

The effective use of data within intensive care units (ICUs) has great potential to create new cloud-based health analytics solutions for disease prevention or earlier condition onset detection. The Artemis project aims to achieve the above goals in the area of neonatal ICUs (NICU). In this paper, we proposed an analytical model for the Artemis cloud project which will be deployed at McMaster Children's Hospital in Hamilton. We collect not only physiological data but also the infusion pumps data that are attached to NICU beds. Using the proposed analytical model, we predict the amount of storage, memory, and computation power required for the system. Capacity planning and tradeoff analysis would be more accurate and systematic by applying the proposed analytical model in this paper. Numerical results are obtained using real inputs acquired from McMaster Children's Hospital and a pilot deployment of the system at The Hospital for Sick Children (SickKids) in Toronto.

Heart rate variability as an indicator for morphine pharmacokinetics and pharmacodynamics in critically ill newborn infants.

Morphine is the commonest drug used for analgesia in newborn infants. It is a natural opioid that acts as an agonist at the mu and kappa receptors, which are receptors for analgesia and sedation. Morphine pharmacokinetics and pharmacodynamics (PKPD) for the newborn infant population are not well understood. The objective of this study is to use morphine PKPD parameters to estimate morphine plasma concentrations to be correlated with heart rate variability in the neonatal population.

Remote, real-time monitoring and analysis of vital signs of neonatal graduate infants.

This paper presents a system for the remote monitoring of a newborn infant's physiological data outside the Neonatal Intensive Care Unit. By providing a simple means for parents to enable monitoring, and physicians a simple mobile application to monitor live and historical physiological information, this system provides the insight once only possible in an Intensive Care Unit. The system utilizes a variety of connectivity means such as Wi-Fi and 3G to facilitate the communication between a multitude of industry standard vital sign monitor and a remote server. A system trial monitoring an infant to simulate neonatal graduate monitoring has determined the system was able to successfully transmit 99.99% of data generated from the vital sign monitor.

The impact of cervical manipulation on heart rate variability.

Heart Rate variability (HRV) is the inter-beat variability in heart rate and is moderated by the balance of sympathetic and parasympathetic divisions of the autonomic nervous system. Electrocardiography (ECG) can be utilized to obtain Low frequency (LF) to high frequency (HF) ratios that represent sympathetic to parasympathetic response, respectively and these ratios may be increased in people with chronic pain. Spinal manipulation is often used to manage musculoskeletal disorders such as neck pain. This study assesses the influence of cervical manipulation on HRV using LF/HF ratio. Ten subjects without neck pain formed the control condition and passive head movement (PHM) condition during which their head was flexed, extended and rotated. Ten subjects with subclinical neck pain underwent the same conditions. A separate session was performed for an actual manipulation. LabChart™ software was utilized to collect and analyze five minute pre and post R-R intervals. Repeated measures of ANOVA demonstrated significant interaction effect on HRV (F (1, 18) = 6.841, p = 0.018) following manipulation vs. PHM. Subsequent analysis showed a significant decrease in the ratio during manipulation condition (p = 0.0316), that was not seen in any other conditions, suggesting a significant autonomic nervous system alteration. This study may lead to new techniques to assess the effectiveness of various treatment interventions.

Integration of drug dosing data with physiological data streams using a cloud computing paradigm.

Many drugs are used during the provision of intensive care for the preterm newborn infant. Recommendations for drug dosing in newborns depend upon data from population based pharmacokinetic research. There is a need to be able to modify drug dosing in response to the preterm infant's response to the standard dosing recommendations. The real-time integration of physiological data with drug dosing data would facilitate individualised drug dosing for these immature infants. This paper proposes the use of a novel computational framework that employs real-time, temporal data analysis for this task. Deployment of the framework within the cloud computing paradigm will enable widespread distribution of individualized drug dosing for newborn infants.

Detection of apnoea from respiratory time series data using clinically recognizable features and kNN classification.

Apnoea is a sleep related breathing disorder that is common in adults and can be described as a temporary closure in the upper airway during sleep. A system using time series analysis of one minute epochs of respiratory impedance signals to detect apnoea is described. An algorithm has been developed using MATLAB for extracting clinically recognizable features from the respiratory impedance signal. One minute samples are classified using kNN classification of the feature set. The output of the system has been shown to detect apnoeic episodes in eight eight-hour patient records collected from the PhysioNet database. The specificity of the classifier is 88.1% and the sensitivity is 95.7%. ROC analysis was performed and the area under the ROC curve is 0.9604. Future research will include testing the classifier in a much larger dataset and also a novel method for the presentation of classification results to physicians.

Real-time multidimensional temporal analysis of complex high volume physiological data streams in the neonatal intensive care unit.

The intensive care of immature preterm infants is a challenging, dynamic clinical task that is complicated because these infants frequently develop a range of comorbidities as they grow and develop after their premature birth. Earliest reliable condition onset detection is a goal within this setting and high frequency physiological analysis is showing potential new pathophysiological indicators for earlier onset detection of several conditions. To realise this, a platform for multi-stream, multi-condition, multi-feature risk scoring is required. In this paper we demonstrate our multi-stream online analytics approach for condition onset detection and demonstrate a user interface approach for patient state that can be available in real-time to support condition risk scoring.

Implementation of artifact detection in critical care: a methodological review.

Artifact detection (AD) techniques minimize the impact of artifacts on physiologic data acquired in critical care units (CCU) by assessing quality of data prior to clinical event detection (CED) and parameter derivation (PD). This methodological review introduces unique taxonomies to synthesize over 80 AD algorithms based on these six themes: 1) CCU; 2) physiologic data source; 3) harvested data; 4) data analysis; 5) clinical evaluation; and 6) clinical implementation. Review results show that most published algorithms: a) are designed for one specific type of CCU; b) are validated on data harvested only from one OEM monitor; c) generate signal quality indicators (SQI) that are not yet formalized for useful integration in clinical workflows; d) operate either in standalone mode or coupled with CED or PD applications; e) are rarely evaluated in real-time; and f) are not implemented in clinical practice. In conclusion, it is recommended that AD algorithms conform to generic input and output interfaces with commonly defined data: 1) type; 2) frequency; 3) length; and 4) SQIs. This shall promote: a) reusability of algorithms across different CCU domains; b) evaluation on different OEM monitor data; c) fair comparison through formalized SQIs; d) meaningful integration with other AD, CED and PD algorithms; and e) real-time implementation in clinical workflows.