Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care.

The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient "data fingerprint" of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians' standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5-7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5-10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5-7 cm H2O and 53.6% more frequently PEEP levels of 7-9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50-55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients

Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes: A Medical-in-Silico Approach Combining In Vitro Experiments and Machine Learning (vol 9, 393, 2018)

A Corrigendum on Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes: A Medical-In silico Approach Combining In vitro Experiments and Machine Learning by Zechendorf E, Vaßen P, Zhang J, Hallawa A, Martincuks A, Krenkel O, Müller-Newen G, Schuerholz T, Simon T-P, Marx G, Ascheid G, Schmeink A, Dartmann G, Thiemermann C and Martin L (2018). Front. Immunol. 9:393. doi: 10.3389/fimmu.2018.00393 In the original article, there was an error in the Author Contributions section. The wording used to declare the contribution of Elisabeth Zechendorf was not clear. The new Author Contributions section appears below. Conception and design: EZ, LM, GD, AS, and CT. In vitro experiments and data analyses: EZ, LM, TS, T-PS, AM, GM-N, OK, GM, and PV. Medical in silico experiments and data analyses: EZ, PV, JZ, GD, AS, LM, AH, and GA. EZ wrote the manuscript. Correction of the manuscript: EZ, PV, LM, CT, GM, GD, T-PS, and AS. All the authors reviewed and finally approved the manuscript. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated

A framework for knowledge integrated evolutionary algorithms

\u3cp\u3eOne of the main reasons for the success of Evolutionary Algorithms (EAs) is their general-purposeness, i.e. the fact that they can be applied in a straight forward manner to a broad range of optimization problems, without any specific prior knowledge. On the other hand, it has been shown that incorporating a priori knowledge, such as expert knowledge or empirical findings, can significantly improve the performance of an EA. However, integrating knowledge in EAs poses numerous challenges. It is often the case that the features of the search space are unknown, hence any knowledge associated with the search space properties can be hardly used. In addition, a priori knowledge is typically problem-specific and hard to generalize. In this paper, we propose a framework, called Knowledge Integrated Evolutionary Algorithm (KIEA), which facilitates the integration of existing knowledge into EAs. Notably, the KIEA framework is EA-agnostic, i.e. it works with any evolutionary algorithm, problem-independent, i.e. it is not dedicated to a specific type of problems and expandable, i.e. its knowledge base can grow over time. Furthermore, the framework integrates knowledge while the EA is running, thus optimizing the consumption of computational power. In the preliminary experiments shown here, we observe that the KIEA framework produces in the worst case an 80% improvement on the converge time, w.r.t. the corresponding “knowledge-free” EA counterpart.\u3c/p\u3

Presenting the ECO:evolutionary computation ontology

\u3cp\u3eA well-established notion in Evolutionary Computation (EC) is the importance of the balance between exploration and exploitation. Data structures (e.g. for solution encoding), evolutionary operators, selection and fitness evaluation facilitate this balance. Furthermore, the ability of an Evolutionary Algorithm (EA) to provide efficient solutions typically depends on the specific type of problem. In order to obtain the most efficient search, it is often needed to incorporate any available knowledge (both at algorithmic and domain level) into the EA. In this work, we develop an ontology to formally represent knowledge in EAs. Our approach makes use of knowledge in the EC literature, and can be used for suggesting efficient strategies for solving problems by means of EC.We call our ontology “Evolutionary Computation Ontology” (ECO). In this contribution, we show one possible use of it, i.e. to establish a link between algorithm settings and problem types. We also show that the ECO can be used as an alternative to the available parameter selection methods and as a supporting tool for algorithmic design.\u3c/p\u3

Presenting the ECO: evolutionary computation ontology

A well-established notion in Evolutionary Computation (EC) is the importance of the balance between exploration and exploitation. Data structures (e.g. for solution encoding), evolutionary operators, selection and fitness evaluation facilitate this balance. Furthermore, the ability of an Evolutionary Algorithm (EA) to provide efficient solutions typically depends on the specific type of problem. In order to obtain the most efficient search, it is often needed to incorporate any available knowledge (both at algorithmic and domain level) into the EA. In this work, we develop an ontology to formally represent knowledge in EAs. Our approach makes use of knowledge in the EC literature, and can be used for suggesting efficient strategies for solving problems by means of EC.We call our ontology “Evolutionary Computation Ontology” (ECO). In this contribution, we show one possible use of it, i.e. to establish a link between algorithm settings and problem types. We also show that the ECO can be used as an alternative to the available parameter selection methods and as a supporting tool for algorithmic design

Evolving hardware instinctive behaviors in resource-scarce agent swarms exploring hard-to-reach environments

© 2018 Companion, Kyoto, Japan This work introduces a novel adaptation framework to energy-eciently adapt small-sized circuits operating under scarce resources in dynamic environments, as autonomous swarm of sensory agents. This framework makes it possible to optimally congure the circuit based on three key mechanisms: (a) an o-line optimization phase relying on R2 indicator based Evolutionary Multi-objective Optimization Algorithm (EMOA), (b) an on-line phase based on hardware instincts and (c) the possibility to include the environment in the optimization loop. Specically, the evolutionary algorithm is able to simultaneously determine an optimal combination of static settings and dynamic instinct for the hardware, considering highly dynamic environments. The instinct is then run on-line with minimal on-chip resources so that the circuit eciently react to environmental changes. This framework is demonstrated on an ultrasonic communication system between energy-scarce wireless nodes. The proposed approach is environment-adaptive and enables power savings up to 45% for the same performance on the considered case studies.status: publishe