GSA: A Framework for Rapid Prototyping of Smart Alarm Systems

We describe the Generic Smart Alarm, an architectural framework for the development of decision support modules for a variety of clinical applications. The need to quickly process patient vital signs and detect patient health events arises in many clinical scenarios, from clinical decision support to tele-health systems to home-care applications. The events detected during monitoring can be used as caregiver alarms, as triggers for further downstream processing or logging, or as discrete inputs to decision support systems or physiological closed-loop applications. We believe that all of these scenarios are similar, and share a common framework of design. In attempting to solve a particular instance of the problem, that of device alarm fatigue due to numerous false alarms, we devised a modular system based around this framework. This modular design allows us to easily customize the framework to address the specific needs of the various applications, and at the same time enables us to perform checking of consistency of the system. In the paper we discuss potential specific clinical applications of a generic smart alarm framework, present the proposed architecture of such a framework, and motivate the benefits of a generic framework for the development of new smart alarm or clinical decision support systems

Combining statistical techniques to predict postsurgical risk of 1-year mortality for patients with colon cancer

Introduction: Colorectal cancer is one of the most frequently diagnosed malignancies and a common cause of cancer-related mortality. The aim of this study was to develop and validate a clinical predictive model for 1-year mortality among patients with colon cancer who survive for at least 30 days after surgery. Methods: Patients diagnosed with colon cancer who had surgery for the first time and who survived 30 days after the surgery were selected prospectively. The outcome was mortality within 1 year. Random forest, genetic algorithms and classification and regression trees were combined in order to identify the variables and partition points that optimally classify patients by risk of mortality. The resulting decision tree was categorized into four risk categories. Split-sample and bootstrap validation were performed. ClinicalTrials.gov Identifier: NCT02488161. Results: A total of 1945 patients were enrolled in the study. The variables identified as the main predictors of 1-year mortality were presence of residual tumor, American Society of Anesthesiologists Physical Status Classification System risk score, pathologic tumor staging, Charlson Comorbidity Index, intraoperative complications, adjuvant chemotherapy and recurrence of tumor. The model was internally validated; area under the receiver operating characteristic curve (AUC) was 0.896 in the derivation sample and 0.835 in the validation sample. Risk categorization leads to AUC values of 0.875 and 0.832 in the derivation and validation samples, respectively. Optimal cut-off point of estimated risk had a sensitivity of 0.889 and a specificity of 0.758. Conclusion: The decision tree was a simple, interpretable, valid and accurate prediction rule of 1-year mortality among colon cancer patients who survived for at least 30 days after surgery.We are grateful for the support of the 22 participating hospitals, as well as the clinicians and staff members of the various services, research, quality units and medical records sections of these hospitals. We also gratefully acknowledge the patients who participated in the study. We would like to thank Editage (www.editage.com) for English language editing. We also wish to thank the anonymous referees for providing comments, which led to substantial improvement of the article. Financial support for this study was provided, in part, by grants from the Instituto de Salud Carlos III (PS09/00314, PS09/00910, PS09/00746, PS09/00805, PI09/90460, PI09/90490, PI09/90453, PI09/90441, PI09/90397 and the thematic network REDISSEC - Red de Investigacion en Servicios de Salud en Enfermedades Cronicas), co-funded by European Regional Development Fund/European Social Fund (ERDF/ESF "Investing in your future"); the Research Committee of the Hospital Galdakao; the Department of Health and the Department of Education, Language Policy and Culture from the Basque Government (2010111098, IT620-13 and BERC 2014-2017 program); the Spanish Ministry of Economy and Competitiveness MINECO and FEDER (MTM2013-40941-P, MTM2016-74931-P and BCAM Severo Ochoa excellence accreditation SEV-2013-0323). The funding agreement ensured the authors' independence in designing the study, interpreting the data, writing and publishing the report

Combining statistical techniques to predict post-surgical risk of 1-year mortality for patients with colon cancer

Introduction: Colorectal cancer is one of the most frequently diagnosed malignancies and a common cause of cancer-related mortality. The aim of this study was to develop and validate a clinical predictive model for1-year mortality among patients with colon cancer who survive for at least 30 days after surgery. Methods: Patients diagnosed with colon cancer who had surgery for the first time and who survived 30 days after the surgery were selected prospectively. The outcome was mortality within 1 year. Random forest, genetic algorithms and classification and regression trees were combined in order to identify the variables and partition points that optimally classify patients by risk of mortality. The resulting decision tree was categorized into four risk categories. Split-sample and bootstrap validation were performed. Results: A total of 1945 patients were enrolled in the study. The variables identified as the main predictors of 1-year mortality were presence of residual tumour, ASA risk score, pathological tumour staging, Charlson comorbidity index, intraoperative complications, adjuvant chemotherapy and recurrence of tumour. The model was internally validated; the area under the curve (AUC) was 0.896 in the derivation sample and 0.835 in the validation sample. Risk categorization leads to AUC values of 0.875 and 0.832 in the derivation and validation samples, respectively. Optimal cut-off point of estimated risk had a sensitivity of 0.889 and a specificity of 0.758. Conclusions: The decision-tree was a simple, interpretable, valid and accurate prediction rule of 1-year mortality among colon cancer patients who survived for at least 30 days after surgery.Instituto de Salud Carlos III (PS09/00314, PS09/00910, PS09/00746, PS09/00805, PI09/90460, PI09/90490, PI09/90453, PI09/90441, PI09/90397 and the thematic networks REDISSEC - Red de Investigación en Servicios de Salud en Enfermedades Crónicas), co-funded by European Regional Development Fund/European Social Fund (ERDF/ESF "Investing in your future"); Research Committee of the Hospital Galdakao Department of Health and the Department of Education, Language Policy and Culture from the Basque Government (2010111098, IT620-13) MINECO and FEDER (MTM2013-40941-P, MTM2016-74931-P)

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

IDENTIFYING SIGNALS OF ICU READMISSIONS AND POST-DISCHARGE MORTALITY FROM PHYSIOLOGICAL TIME SERIES DATA USING MACHINE LEARNING

Rationale Critical care utilization and costs are a vast part of our healthcare system and continue to grow. One opportunity for increasing the quality and efficiency of critical care is reducing intensive care unit (ICU) re-admissions, which are associated with higher costs and poor patient outcomes. Predictive models for ICU readmissions have been built in the past, but generally do not perform well, and rarely use complex features derived from high-frequency physiological time series data. Objectives This thesis aims to enhance the efficacy of prediction of ICU readmission and post-discharge mortality by training machine learning classifiers using features derived from physiological data signals, including oxygen saturation, heart rate, respiratory rate, and blood pressure, which are captured at high frequency during routine intensive care. Methods Predictive features from the entire ICU stay were extracted from a publicly available, multi-center database. These were used in various combinations, using logistic regression, random forest, and gradient boosting algorithms to predict a composite outcome, of ICU readmission or post-discharge mortality within 72 hours of ICU discharge. Model performance was analyzed using area under the receiver operator curve (AUROC), obtained using nested cross-validation and randomized hyper-parameter searching. The features with highest predictive value were selected using random forest feature importance and used to construct models with reduced complexity. Results The predictive model achieved a mean area under the receiver operator curve (AUROC) of 0.680 (95% confidence interval: [0.647, 0.713]) from the outer loop of nested cross-validation, and 0.656 from the test set. The highest performing feature space was a mixed feature space, that used both low and high frequency variables for feature extraction. The top features included high and low frequency variables. High frequency features included linear regression intercepts and Fourier transform coefficients. Low frequency variable features included age, sodium, glucose, weight change, and APACHE IV scores. Conclusion Newly developed models do not currently outperform previously constructed models in the literature nor clinician prediction. Complex features derived from high frequency physiological time series data did not outperform more conventional variables such as labs or demographics. Further investigation with different features, data, and modeling algorithms is warranted

Modeling Decision Making In Trauma Centers From The Standpoint Of Complex Adaptive Systems

This research examines complex clinical decision-making processes in trauma center units of hospitals in terms of the impact of complexity on the medical team involved in the trauma event. The science of complex adaptive systems together with human judgment theories provide important concepts and tools for responding to health care challenges in this century and beyond. Clinical decision-makers in trauma centers are placed in urgent and anxious situations that are increasingly complex, making decision-making and problem-solving processes multifaceted. Under stressful circumstances, physicians must derive their decision-making schemas (―internal models‖ or ―mental models‖) without the benefits of judicious identification, evaluation, and/or application of relevant medical information, and always using fragmentary data. This research developed a model of decision-making processes in trauma events that uses a Bayesian Classifier model jointly with Convolution and Deconvolution operators to study real-time observed trauma data for decision-making processes under stress. The objective was to explore and explain physicians‘ decision-making processes during actual trauma events while under the stress of time constraints and lack of data. The research addresses important operations that describe the behavior of a dynamic system resulting from stress placed on the physician‘s rational decision making processes by the conditions of the environment. Deconvolution, that is, determining the impulse response of the system, is used to understand how physicians clear out extraneous environmental noise in order to have a clearer picture of their mental models and reach a diagnosis or diagnostic course of action

Automation of Patient Trajectory Management: A deep-learning system for critical care outreach

The application of machine learning models to big data has become ubiquitous, however their successful translation into clinical practice is currently mostly limited to the field of imaging. Despite much interest and promise, there are many complex and interrelated barriers that exist in clinical settings, which must be addressed systematically in advance of wide-spread adoption of these technologies. There is limited evidence of comprehensive efforts to consider not only their raw performance metrics, but also their effective deployment, particularly in terms of the ways in which they are perceived, used and accepted by clinicians. The critical care outreach team at St Vincent’s Public Hospital want to automatically prioritise their workload by predicting in-patient deterioration risk, presented as a watch-list application. This work proposes that the proactive management of in-patients at risk of serious deterioration provides a comprehensive case-study in which to understand clinician readiness to adopt deep-learning technology due to the significant known limitations of existing manual processes. Herein is described the development of a proof of concept application uses as its input the subset of real-time clinical data available in the EMR. This data set has the noteworthy challenge of not including any electronically recorded vital signs data. Despite this, the system meets or exceeds similar benchmark models for predicting in-patient death and unplanned ICU admission, using a recurrent neural network architecture, extended with a novel data-augmentation strategy. This augmentation method has been re-implemented in the public MIMIC-III data set to confirm its generalisability. The method is notable for its applicability to discrete time-series data. Furthermore, it is rooted in knowledge of how data entry is performed within the clinical record and is therefore not restricted in applicability to a single clinical domain, instead having the potential for wide-ranging impact. The system was presented to likely end-users to understand their readiness to adopt it into their workflow, using the Technology Adoption Model. In addition to confirming feasibility of predicting risk from this limited data set, this study investigates clinician readiness to adopt artificial intelligence in the critical care setting. This is done with a two-pronged strategy, addressing technical and clinically-focused research questions in parallel

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Understanding Peripheral Blood Pressure Signals: A Statistical Learning Approach

Proper estimation of body fluid status for human or animal subjects has always been a challenging problem. Accurate and timely estimate of body fluid can prevent life threatening conditions under trauma and severe dehydration. The main objective of this research is the estimation, classification and detection of dehydration in human and animal subjects using peripheral blood pressure (PBP) signals. Peripheral venous pressure (PVP) and peripheral arterial pressure (PAP) signals have been investigated in this research. Both PVP and PAP signals are PBP signals. A dataset of PVP signals was collected using standard peripheral intravenous catheters from human subjects suffering from hypertrophic pyloric stenosis. Using this dataset, we successfully classified dehydrated subjects from hydrated subjects using regularized logistic regression on frequency domain data of the PVP signals. During the data acquisition process, the PVP signals was corrupted by noise and blood clot. So, we developed an unsupervised anomaly detection algorithm for PVP signals using hidden Markov model and Kalman filter. This anomaly detection algorithm removed the human bias in data-preprocessing. Another dataset of PAP and PVP signals was collected from pigs under anesthesia using the Millar catheter. We proposed a integral pulse frequency modulation (IPFM) based signal model for both PAP and PVP signals. The proposed model-synthesized signal is highly correlated with the experimental data. The model-synthesized signals also performs similar to experimental signals under classification tasks. We also examine the model estimated parameters both qualitatively and quantitatively. This model can also quantify the effect of respiratory rate on heart rate variability. Increasing doses of anesthesia has similar effect of getting hydrated from dehydration