Data driven automatic model selection and parameter adaptation – a case study for septic shock

In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. This paper propose as model selection criterion the least complex description of the observed data by the model, the minimum description length. For the small, but important example of inflammation modeling the performance of the approach is evaluated

Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach

Mortality risk prediction can greatly improve the utilization of resources in intensive care units (ICUs). Existing schemes in ICUs today require laborious manual input of many complex parameters. In this work, we present a scheme that uses variations in vital signs over a 24-h period to make mortality risk assessments for 3-day, 7-day, and 14-day windows. We develop a hybrid neural network model that combines convolutional (CNN) layers with bidirectional long short-term memory (BiLSTM) to predict mortality from statistics describing the variation of heart rate, blood pressure, respiratory rate, blood oxygen levels, and temperature. Our scheme performs strongly compared to state-of-the-art schemes in the literature for mortality prediction, with our highest-performing model achieving an area under the receiver-operator curve of 0.884. We conclude that the use of a hybrid CNN-BiLSTM network is highly effective in determining mortality risk for the 3, 7, and 14 day windows from vital signs. As vital signs are routinely recorded, in many cases automatically, our scheme could be implemented such that highly accurate mortality risk could be predicted continuously and automatically, reducing the burden on healthcare providers and improving patient outcomes

Novel characterization method of impedance cardiography signals using time-frequency distributions

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P¿=¿0.780) and the extended modified beta distribution (P¿=¿0.765) provided similar results, higher than the rest of analyzed kernels.Peer ReviewedPostprint (published version

Frequency and Time Domain Feature Engineering and Predictive Modeling Based on ECG, SpO2, and Respiration Signals

Today, there exists a challenge in simplifying biosignals into features that are well suited for machine learning and clinician understanding. This work reports the feature engineering exercise involved with such challenge, along with the predictive modeling. We primarily tackle ECG, Respiration (Thoracic Impedance), and SpO2 (Plethsmographic) signals extracted from a proprietary dataset used by GE Healthcare. Throughout the study, we analyze biosignals while searching for general characteristics which may help describe (and even highlight) human function for a machine learning model, while maintaining clinical value. Wave Morphology Analysis in the Time Domain, Wavelet Decomposition and Fast Fourier Transforms were the main methods explored for feature engineering. Finally, results from a Convolutional Neural Network and a Random Forest model are reported, whereby the best performing model is able to predict Sepsis with 77% accuracy at least three (3) hours in advance

PROTOCOLIZED VOLUME DE-RESUSCITATION IN CRITICALLY ILL PATIENTS

While early fluid resuscitation may be a necessary component to decrease mortality in the majority of critically ill patients admitted to the intensive care unit, the benefit of continued administration after the first 24 hours is less clear. Paradoxically, a positive fluid balance secondary to intravenous fluid receipt has been associated with diverse and persistent perpetuating detriment on a multitude of organ systems. Continued clinical harm has been demonstrated on pulmonary and renal function as well as patient outcomes such as rates of mortality and length of stay. Despite the growing body of evidence supporting the potential adverse aspects of positive fluid balances, fluid overload remains common in patients during the early days of critical care admission. One approach to correcting fluid balance is shifting focus onto the post- or de- resuscitation period with appropriate fluid removal with diuresis once hemodynamic stability is achieved. However, diuresis is often ineffective due to a lack of standardization in identification of fluid-overloaded patients. Further, optimal transition times between fluid resuscitation and fluid removal are not clear and physical signs of fluid overload are delayed relative to onset of organ damage. While administration of diuretics has shown to decrease net volume and improve clinical outcomes in the critically ill, current practice does not reflect clinical trial findings. Most treatment regimens are often inadequate both by nature of time and dosing intensity. Further, as de-resuscitation occurs once the initial instability has resolved, precedence is usually given to other acute or critical needs rather than follow-up for diuresis effectiveness. Additionally, frequent apprehension for medication side effects is seen, despite the preponderance of adverse event data found only in non-critical care populations, frequently non-translatable to patients within the intensive care unit. Optimization of diuresis in critically ill patients is primed for clinical pharmacy intervention. Clinical pharmacists are experts in the delivery of pharmaceutical care, utilizing specialized therapeutic knowledge, experience, and judgment to ensure optimal patient outcomes. Pharmacist-driven protocols for other conditions have shown improved patient outcomes, reduced adverse events and improved target attainment in before and after studies. A pharmacist- driven diuresis protocol to facilitate de-resuscitation implemented within the multidisciplinary critical care team has the potential to improve patient care by optimizing pharmacotherapy selection, while potentially reducing adverse events, days on mechanical ventilation and length of intensive care unit stay. Such a protocol rightfully places pharmacist accountability on medication-related outcomes while potentially decreasing critical care resource utilization. The work within this dissertation aims to accomplish the development of a pharmacist- driven diuresis protocol for implementation in the medical intensive care unit, with national pharmacy organization sponsorship. Further, the protocol aims to be adopted as an innovative practice change for de-resuscitation of critically ill patients to improve clinical outcomes while advancing the pharmacy profession

An investigation into the effects of commencing haemodialysis in the critically ill

<b>Introduction:</b> We have aimed to describe haemodynamic changes when haemodialysis is instituted in the critically ill. 3 hypotheses are tested: 1)The initial session is associated with cardiovascular instability, 2)The initial session is associated with more cardiovascular instability compared to subsequent sessions, and 3)Looking at unstable sessions alone, there will be a greater proportion of potentially harmful changes in the initial sessions compared to subsequent ones. <b>Methods:</b> Data was collected for 209 patients, identifying 1605 dialysis sessions. Analysis was performed on hourly records, classifying sessions as stable/unstable by a cutoff of >+/-20% change in baseline physiology (HR/MAP). Data from 3 hours prior, and 4 hours after dialysis was included, and average and minimum values derived. 3 time comparisons were made (pre-HD:during, during HD:post, pre-HD:post). Initial sessions were analysed separately from subsequent sessions to derive 2 groups. If a session was identified as being unstable, then the nature of instability was examined by recording whether changes crossed defined physiological ranges. The changes seen in unstable sessions could be described as to their effects: being harmful/potentially harmful, or beneficial/potentially beneficial. <b>Results:</b> Discarding incomplete data, 181 initial and 1382 subsequent sessions were analysed. A session was deemed to be stable if there was no significant change (>+/-20%) in the time-averaged or minimum MAP/HR across time comparisons. By this definition 85/181 initial sessions were unstable (47%, 95% CI SEM 39.8-54.2). Therefore Hypothesis 1 is accepted. This compares to 44% of subsequent sessions (95% CI 41.1-46.3). Comparing these proportions and their respective CI gives a 95% CI for the standard error of the difference of -4% to 10%. Therefore Hypothesis 2 is rejected. In initial sessions there were 92/1020 harmful changes. This gives a proportion of 9.0% (95% CI SEM 7.4-10.9). In the subsequent sessions there were 712/7248 harmful changes. This gives a proportion of 9.8% (95% CI SEM 9.1-10.5). Comparing the two unpaired proportions gives a difference of -0.08% with a 95% CI of the SE of the difference of -2.5 to +1.2. Hypothesis 3 is rejected. Fisher’s exact test gives a result of p=0.68, reinforcing the lack of significant variance. <b>Conclusions:</b> Our results reject the claims that using haemodialysis is an inherently unstable choice of therapy. Although proportionally more of the initial sessions are classed as unstable, the majority of MAP and HR changes are beneficial in nature

Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers

As health information technologies continue to advance, routine collection and digitisation of patient health records in the form of electronic health records present as an ideal opportunity for data-mining and exploratory analysis of biomarkers and risk factors indicative of a potentially diverse domain of patient outcomes. Patient records have continually become more widely available through various initiatives enabling open access whilst maintaining critical patient privacy. In spite of such progress, health records remain not widely adopted within the current clinical statistical analysis domain due to challenging issues derived from such “big data”.Deep learning based temporal modelling approaches present an ideal solution to health record challenges through automated self-optimisation of representation learning, able to man-ageably compose the high-dimensional domain of patient records into data representations able to model complex data associations. Such representations can serve to condense and reduce dimensionality to emphasise feature sparsity and importance through novel embedded feature selection approaches. Accordingly, application towards patient records enable complex mod-elling and analysis of the full domain of clinical features to select biomarkers of predictive relevance.Firstly, we propose a novel entropy regularised neural network ensemble able to highlight risk factors associated with hospitalisation risk of individuals with dementia. The application of which, was able to reduce a large domain of unique medical events to a small set of relevant risk factors able to maintain hospitalisation discrimination.Following on, we continue our work on ensemble architecture approaches with a novel cas-cading LSTM ensembles to predict severe sepsis onset within critical patients in an ICU critical care centre. We demonstrate state-of-the-art performance capabilities able to outperform that of current related literature.Finally, we propose a novel embedded feature selection application dubbed 1D convolu-tion feature selection using sparsity regularisation. Said methodology was evaluated on both domains of dementia and sepsis prediction objectives to highlight model capability and generalisability. We further report a selection of potential biomarkers for the aforementioned case study objectives highlighting clinical relevance and potential novelty value for future clinical analysis.Accordingly, we demonstrate the effective capability of embedded feature selection ap-proaches through the application of temporal based deep learning architectures in the discovery of effective biomarkers across a variety of challenging clinical applications

Sepsis-assoziierte kognitive Dysfunktion: Eine Untersuchung mit stressfreien, automatisierten Verhaltenstests

Survivors of medical and surgical intensive care units (ICUs) are at high risk for long-lasting cognitive impairments. Among critical illnesses that can induce cognitive deficits, sepsis is commonly regarded as the most frequent and severe cause, considering one out of three survivors of sepsis is discharged from hospital with severe de novo cognitive impairment. Chronic neuroinflammation, diffuse cerebral damage and neuronal death are considered primary correlates in the development of long-term cognitive deficits. Recent studies have suggested sepsis can cause inflammatory activation of the microglia, which lead to microglial phagocytosis of stressed but viable neurons. To establish a mouse model of sepsis, lipopolysaccharide (LPS) at a dose of 1.5 mg/kg was injected in C57BL/6 mice and homozygous knockout and wildtype mice that are deficient for Mertk, Cd11b and Mfge8. Immediate sickness behavior and long-term cognitive functions of animals were analyzed to assess the effects of phagocytic deficiency and peptide treatments on cognitive deficits. To best meet the requirements of animal welfare by minimizing repetitive handling, water and/or food restriction and unnecessary suffering, we have also investigated the applicability of a fully automatized 8-arm radial arm maze (RAM) and machine learning-based humane endpoint determination. LPS-injected animals have displayed (a) characteristics of sickness behavior immediately following the injections, and (b) cognitive deficits after the one-month recovery period. Animals deficient for Mertk, Cd11b or Mfge8 have displayed greater learning performances during place learning, place reversal and avoidance conditioning. Treatment effects of Cilengitide or cRGD were observed in sucrose preference, avoidance conditioning and the early stage of place learning. In the meantime, using humane endpoints determined with machine learning models, mice of both stroke and sepsis model that are at higher risk of death could be detected at a high accuracy. Mice up to 18 months of age have shown efficient spatial learning in both working memory and combined working/reference memory paradigms in the automated 8-arm RAM without food and/or water restriction. With a mouse model of sepsis, alleviation of long-term cognitive deficits could be observed in phagocytic deficient animals and Cilengitide- or cRGD-treated animals, which might offer an explanation of underlying mechanisms of long-term cognitive deficits following systemic inflammation. Minimized suffering for animals and improved reproducibility of experimental outcomes were possible using machine learning-based endpoint determination and automated behavioral testing systems, respectively.Überlebende von chirurgischen und konservativen Intensivstationen haben ein hohes Risiko für lang anhaltende kognitive Defizite. Die Sepsis gilt als häufigste und schwerwiegendste der kritischen Erkrankungen, die zu kognitiven Defiziten führen können. Einer von drei Überlebenden einer Sepsis wird mit schwerer, neu aufgetretener kognitiver Dysfunktion aus dem Krankenhaus entlassen. Chronische Neuroinflammation, diffusive zerebrale Schädigungen und neuronaler Zelltod werden als die primären Korrelate in der Entwicklung lang anhaltender kognitiver Defizite angesehen. Neuere Studien deuten darauf hin, dass Sepsis eine inflammatorische Aktivierung von Mikroglia auslöst, die zu Phagozytose gestresster, aber funktionsfähiger Neurone führt. Um ein Mausmodell der Sepsis zu etablieren, wurden Lipopolysaccharide (LPS) in C57BL/6-Mäuse und homozygote Knockout- und Wildtyp-Mäuse mit Mertk-, CD11b- und Mfge8-Defizienz in einer Dosierung von 1,5mg/kg injiziert. Das akute Krankheitsverhalten und langzeitige kognitive Funktionen der Tiere wurden analysiert, um die Effekte von phagozytotischer Defizienz und Behandlung mit Peptiden auf kognitive Defizite zu untersuchen. Um die Anforderungen an das Tierwohl durch Reduzierung von Handling, Wasser- und/oder Nahrungsentzug und unnötigem Leid optimal zu erfüllen, untersuchten wir außerdem die Anwendbarkeit eines vollständig automatisierten 8-Arm-Radial Arm Mazes und von machine learning-basierter Bestimmung von Abbruchkriterien (humane endpoints). Tiere mit LPS-Injektion zeigten (a) Charakteristika von Krankheitsverhalten unmittelbar nach der Injektion und (b) kognitive Defizite nach der einmonatigen Erholungsperiode. Tiere mit Mertk-, CD11b- und Mfge8-Defizienz präsentierten bessere Lernleistungen bezüglich Place Learning, Place Reversal und Avoidance Conditioning. Behandlungseffekte von Cilengitid oder cRGD konnten bezüglich Sucrose Preference, Avoidance Conditioning und in der frühen Phase des Place Learning beobachtet werden. Bei der Nutzung von Machine Learning-Modellen, die Abbruchkriterien bestimmten, zeigte sich, dass Mäuse im Schlaganfall- und im Sepsismodell mit einem höheren Todesrisiko mit hoher Genauigkeit erkannt werden konnten. Mäuse bis zum Alter von 18 Monaten zeigten effizientes räumliches Lernen im Paradigma für das Arbeitsgedächtnis und im kombinierten Paradigma für das Arbeits- und Referenzgedächtnis im automatisierten 8-Arm-Radial Arm Maze ohne Nahrungs- oder Wasserentzug. In einem Mausmodell der Sepsis beobachteten wir eine Verminderung langzeitiger kognitiver Defizite in phagozyten-defizienten und in Cilengitid- oder cRDG-behandelten Tieren, was eine Erklärung für die Mechanismen, die langzeitigen kognitiven Defiziten zugrunde liegen, bieten könnte. Minimiertes Leid für die Tiere und verbesserte Reproduzierbarkeit von experimentellen Ergebnissen waren möglich durch die Benutzung machine learning-basierter Bestimmung von Abbruchkriterien und automatisierter Verhaltenstestung