Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure

© 2013 IEEE. Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients

Multivariate sequential contrast pattern mining and prediction models for critical care clinical informatics

University of Technology Sydney. Faculty of Engineering and Information Technology.Data mining and knowledge discovery involves efficient search and discovery of patterns in data that are able to describe the underlying complex structure and properties of the corresponding system. To be of practical use, the discovered patterns need to be novel, informative and interpretable. Large-scale unstructured biomedical databases such as electronic health records (EHRs) tend to exacerbate the problem of discovering interesting and useful patterns. Typically, patients in intensive care units (ICUs) require constant monitoring of vital signs. To this purpose, significant quantities of patient data, coupled with waveform signals are gathered from biosensors and clinical information systems. Subsequently, clinicians face an enormous challenge in the assimilation and interpretation of large volumes of unstructured, multidimensional, noisy and dynamically fluctuating patient data. The availability of de-identified ICU datasets like the MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care) databases provide an opportunity to advance medical care, by benchmarking algorithms that capture subtle patterns associated with specific medical conditions. Such patterns are able to provide fresh insights into disease dynamics over long time scales. In this research, we focus on the extraction of computational physiological markers, in the form of relevant medical episodes, event sequences and distinguishing sequential patterns. These interesting patterns known as sequential contrast patterns are combined with patient clinical features to develop powerful clinical prediction models. Later, the clinical models are used to predict critical ICU events, pertaining to numerous forms of hemodynamic instabilities causing acute hypotension, multiple organ failures, and septic shock events. In the process, we employ novel sequential pattern mining methodologies for the structured analysis of large-scale ICU datasets. The reported algorithms use a discretised representation such as symbolic aggregate approximation for the analysis of physiological time series data. Thus, symbolic sequences are used to abstract physiological signals, facilitating the development of efficient sequential contrast mining algorithms to extract high risk patterns and then risk stratify patient populations, based on specific clinical inclusion criteria. Chapter 2 thoroughly reviews the pattern mining research literature relating to frequent sequential patterns, emerging and contrast patterns, and temporal patterns along with their applications in clinical informatics. In Chapter 3, we incorporate a contrast pattern mining algorithm to extract informative sequential contrast patterns from hemodynamic data, for the prediction of critical care events like Acute Hypotension Episodes (AHEs). The proposed technique extracts a set of distinguishing sequential patterns to predict the occurrence of an AHE in a future time window, following the passage of a user-defined gap interval. The method demonstrates that sequential contrast patterns are useful as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients. Chapter 4 reports a generic two stage sequential patterns based classification framework, which is used to classify critical patient events including hypotension and patient mortality, using contrast patterns. Here, extracted sequential patterns undergo transformation to construct binary valued and frequency based feature vectors for developing critical care classification models. Chapter 5 proposes a novel machine learning approach using sequential contrast patterns for the early prediction of septic shock. The approach combines highly informative sequential patterns extracted from multiple physiological variables and captures the interactions among these patterns via Coupled Hidden Markov Models (CHMM). Our results demonstrate a strong competitive accuracy in the predictions, especially when the interactions between the multiple physiological variables are accounted for using multivariate coupled sequential models. The novelty of the approach stems from the integration of sequence-based physiological pattern markers with the sequential CHMM to learn dynamic physiological behavior as well as from the coupling of such patterns to build powerful risk stratification models for septic shock patients. All of the described methods have been tested and bench-marked using numerous real world critical care datasets from the MIMIC-II database. The results from these experiments show that multivariate sequential contrast patterns based coupled models are highly effective and are able to improve the state-of-the-art in the design of patient risk prediction systems in critical care settings

Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns

© 2016 Background and objective Critical care patient events like sepsis or septic shock in intensive care units (ICUs) are dangerous complications which can cause multiple organ failures and eventual death. Preventive prediction of such events will allow clinicians to stage effective interventions for averting these critical complications. Methods It is widely understood that physiological conditions of patients on variables such as blood pressure and heart rate are suggestive to gradual changes over a certain period of time, prior to the occurrence of a septic shock. This work investigates the performance of a novel machine learning approach for the early prediction of septic shock. The approach combines highly informative sequential patterns extracted from multiple physiological variables and captures the interactions among these patterns via coupled hidden Markov models (CHMM). In particular, the patterns are extracted from three non-invasive waveform measurements: the mean arterial pressure levels, the heart rates and respiratory rates of septic shock patients from a large clinical ICU dataset called MIMIC-II. Evaluation and results For baseline estimations, SVM and HMM models on the continuous time series data for the given patients, using MAP (mean arterial pressure), HR (heart rate), and RR (respiratory rate) are employed. Single channel patterns based HMM (SCP-HMM) and multi-channel patterns based coupled HMM (MCP-HMM) are compared against baseline models using 5-fold cross validation accuracies over multiple rounds. Particularly, the results of MCP-HMM are statistically significant having a p-value of 0.0014, in comparison to baseline models. Our experiments demonstrate a strong competitive accuracy in the prediction of septic shock, especially when the interactions between the multiple variables are coupled by the learning model. Conclusions It can be concluded that the novelty of the approach, stems from the integration of sequence-based physiological pattern markers with the sequential CHMM model to learn dynamic physiological behavior, as well as from the coupling of such patterns to build powerful risk stratification models for septic shock patients

Prediction of disease severity in patients with febrile illnesses in resource-limited settings

Febrile illnesses present unique challenges for health systems with scarce resources. Large volumes of mostly self-limiting diseases accompanied by high case-fatality rates for the small proportion of serious infections mean that risk stratification tools must have high sensitivities and/or specificities according to their proposed contexts of use. Unfortunately, accuracy and reliability of existing tools are sub-optimal and they are often impractical for deployment in resource-limited settings. This thesis explores the development and application of prediction tools for the management of febrile illnesses across a range of resource-constrained settings in South and Southeast Asia. It aims to combine the best prediction model science with a pragmatic field-based reality to address locally-important health issues. Recognising that evaluation of prediction tools should be set in their intended contexts of use, each analysis is framed in a particular clinical use-case, yet draws upon approaches to allow exploration of the generalisability of the findings. Using a variety of research methodologies, this thesis identifies prognostic factors amongst febrile children presenting to different levels of the health system, and uses these data to externally validate existing severity scores and develop new clinical prediction models suitable for resource-limited settings. Prospective work evaluates the relative contributions of clinical and biomarker-based approaches for the referral of children with respiratory infections from a resource-limited community setting on the Thailand-Myanmar border, whilst retrospective work develops a prognostic model for critically ill children on admission to a paediatric intensive care unit in northern Cambodia. Finally, in response to the arrival of the SARS-CoV-2 pandemic, this thesis reports the development and external validation of prognostic models at two sites in India to support the safe outpatient management of patients presenting with moderate Covid-19. Important challenges and potential solutions to developing prediction tools in resource-limited settings are discussed, including application of the classical prediction paradigm to the assessment of disease severity, comparative analyses of the clinical utility of different models, and the differential importance of various predictors for identifying both patients who are sick at the time of clinical assessment and those whose illnesses will progress later in their disease course

AI Enabled Drug Design and Side Effect Prediction Powered by Multi-Objective Evolutionary Algorithms & Transformer Models

Due to the large search space and conflicting objectives, drug design and discovery is a difficult problem for which new machine learning (ML) approaches are required. Here, the problem is to invent a method by which new, therapeutically useful, compounds can be discovered; and to simultaneously avoid compounds which will fail clinical trials or pass unwanted effects onto the end patient. By extending current technologies as well as adding new ones, more design criteria can be included, and more promising novel drugs can be discovered. This work advances the field of computational drug design by (1) developing MOEA-DT, a non-deep learning application for multi-objective molecular optimization, which generates new molecules with high performance in a variety of design criteria; and (2) developing SEMTL-BERT, a side effect prediction algorithm which leverages the latest ML techniques and datasets to accomplish its task. Experiments performed show that MOEA-DT either matches or outperforms other similar methods, and that SEMTL-BERT can enhance predictive ability

The European guideline on management of major bleeding and coagulopathy following trauma: sixth edition

Background: Severe trauma represents a major global public health burden and the management of post-traumatic bleeding continues to challenge healthcare systems around the world. Post-traumatic bleeding and associated traumatic coagulopathy remain leading causes of potentially preventable multiorgan failure and death if not diagnosed and managed in an appropriate and timely manner. This sixth edition of the European guideline on the management of major bleeding and coagulopathy following traumatic injury aims to advise clinicians who care for the bleeding trauma patient during the initial diagnostic and therapeutic phases of patient management. Methods: The pan-European, multidisciplinary Task Force for Advanced Bleeding Care in Trauma included representatives from six European professional societies and convened to assess and update the previous version of this guideline using a structured, evidence-based consensus approach. Structured literature searches covered the period since the last edition of the guideline, but considered evidence cited previously. The format of this edition has been adjusted to reflect the trend towards concise guideline documents that cite only the highest-quality studies and most relevant literature rather than attempting to provide a comprehensive literature review to accompany each recommendation. Results: This guideline comprises 39 clinical practice recommendations that follow an approximate temporal path for management of the bleeding trauma patient, with recommendations grouped behind key decision points. While approximately one-third of patients who have experienced severe trauma arrive in hospital in a coagulopathic state, a systematic diagnostic and therapeutic approach has been shown to reduce the number of preventable deaths attributable to traumatic injury. Conclusion: A multidisciplinary approach and adherence to evidence-based guidelines are pillars of best practice in the management of severely injured trauma patients. Further improvement in outcomes will be achieved by optimising and standardising trauma care in line with the available evidence across Europe and beyond