Analysis and Modeling of Quality Improvement on Clinical Fitness Landscapes

Widespread unexplained variations in clinical practices and patient outcomes, together with rapidly growing availability of data, suggest major opportunities for improving the quality of medical care. One way that healthcare practitioners try to do that is by participating in organized healthcare quality improvement collaboratives (QICs). In QICs, teams of practitioners from different hospitals exchange information on clinical practices, with the aim of improving health outcomes at their own institutions. However, what works in one hospital may not work in others with different local contexts, due to non-linear interactions among various demographics, treatments, and practices. I.e., the clinical landscape is a complex socio-technical system that is difficult to search. In this dissertation we develop methods for analysis and modeling of complex systems, and apply them to the problem of healthcare improvement. Searching clinical landscapes is a multi-objective dynamic problem, as hospitals simultaneously optimize for multiple patient outcomes. We first discuss a general method we developed for finding which changes in features may be associated with various changes in outcomes at different points in time with different delays in affect. This method correctly inferred interactions on synthetic data, however the complexity and incompleteness of the real hospital dataset available to us limited the usefulness of this approach. We then discuss an agent-based model (ABM) of QICs to show that teams comprising individuals from similar institutions outperform those from more diverse institutions, under nearly all conditions, and that this advantage increases with the complexity of the landscape and the level of noise in assessing performance. We present data from a network of real hospitals that provides encouraging evidence of a high degree of similarity in clinical practices among hospitals working together in QIC teams. Based on model outcomes, we propose a secure virtual collaboration system that would allow hospitals to efficiently identify potentially better practices in use at other institutions similar to theirs, without any institutions having to sacrifice the privacy of their own data. To model the search for quality improvement in clinical fitness landscapes, we need benchmark landscapes with tunable feature interactions. NK landscapes have been the classic benchmarks for modeling landscapes with epistatic interactions, but the ruggedness is only tunable in discrete jumps. Walsh polynomials are more finely tunable than NK landscapes, but are only defined on binary alphabets and, in general, have unknown global maximum and minimum. We define a different subset of interaction models that we dub as NM landscapes. NM landscapes are shown to have smoothly tunable ruggedness and difficulty and known location and value of global maxima. With additional constraints, we can also determine the location and value of the global minima. The proposed NM landscapes can be used with alphabets of any arity, from binary to real-valued, without changing the complexity of the landscape. NM landscapes are thus useful models for simulating clinical landscapes with binary or real decision variables and varying number of interactions. NM landscapes permit proper normalization of fitnesses so that search results can be fairly averaged over different random landscapes with the same parameters, and fairly compared between landscapes with different parameters. In future work we plan to use NM landscapes as benchmarks for testing various algorithms that can discover epistatic interactions in real world datasets

Teoria da complexidade e paisagens de adaptação: aplicações em estratégia

En este artículo, se tiene como objetivo investigar la dinámica de la posición estratégica de empresas, según el punto de vista de la teoría de la complejidad. Por medio de la aplicación del concepto de paisaje adaptativo, se desarrolla un algoritmo basado en el modelo NK(C) de Kauffman, que posibilita, a partir de una analogía con la evolución en biología, evaluar cómo elementos asociados a las complejidades organizacionales afectan la estructura competitiva de una industria. En este estudio, se simulan varias combinaciones de escenarios, en los que variables relevantes de las organizaciones son interdependientes internamente, así como dependientes de variables externas. Los resultados sugieren que: cuando hay alta complejidad interna, ventajas competitivas sustentables pueden ocurrir, en función de la habilidad de gestión de competencias y recursos; cuando hay complejidad externa, la dificultad de optimización en un paisaje adaptativo accidentado puede implicar la necesidad de adopción de una estrategia de integración vertical; cuando las barreras de entrada son altas, la industria está caracterizada por carga genética elevada, lo que implica gran diversidad estratégica y baja eficacia; y la posibilidad de reestructuración puede evitar inercia, llevando a que, en ambientes complejos, se alcancen puntos de mayor desempeño.Neste artigo, tem-se por objetivo investigar a dinâmica do posicionamento estratégico de empresas, segundo a ótica da teoria da complexidade. Por meio da aplicação do conceito de paisagens de adaptação, é desenvolvido um algoritmo baseado no modelo NK(C) de Kauffman, que possibilita, a partir de uma analogia com a evolução em biologia, avaliar como elementos associados às complexidades organizacionais podem afetar a estrutura competitiva de uma indústria. No estudo, são simuladas várias combinações de cenários, nos quais variáveis relevantes das organizações são interdependentes internamente, assim como dependentes de variáveis externas. Os resultados sugerem que: quando há alta complexidade interna, vantagens competitivas sustentáveis podem formar-se, em função da habilidade de gestão de competências e recursos; quando há complexidade externa, a dificuldade de otimização em uma paisagem de adaptação acidentada pode implicar a necessidade de adoção de estratégia de integração vertical; quando as barreiras de entrada são altas, a indústria é caracterizada por carga genética elevada, implicando alta diversidade estratégica e baixa eficiência; a possibilidade de reestruturação pode evitar inércia, fazendo com que, em ambientes complexos, pontos de maior desempenho sejam atingidos.This article aims to investigate the dynamics of the strategic positioning of companies from a complexity theory approach. Through the application of the concept of adaptation landscapes, the authors develop an algorithm based on Kauffmans's NK(C) model. This enables them, using an analogy with biological evolution as their starting point, to evaluate how organizational complexity elements can influence the competitive structure of an industry. In this study, the authors simulate combinations of scenarios in which relevant variables of organizations are internally interdependent as well as dependent on external variables. The results suggest that: when there is high internal complexity, sustainable competitive advantages may develop, due to skills in managing capabilities and resources; when there is external complexity, the difficulty of optimization in a rugged adaptation landscape may imply a need for adopting a vertical integration strategy; when entrance barriers are too restrictive, the industry is characterized by a high genetic load, implying a high number of strategies and low performance efficiency; and the possibility of restructuring may avoid inertia and, in complex environments, industry may achieve higher performance strategies

Semantic and Syntactic Transfer of Fitness Landscape Models to the Analysis of Collective and Public Decision-Making Processes

We set out to develop a method and research technique that could unite both modelling and case-based observations in order to analyse collective decision-making processes. Following Abbott’s (2001) recommendation regarding social processes, we have defined collective decision-making as an uninterrupted and non-directional process that is structured in sequences or lineages of events. To structure these processes, we re-modelled the basic components of Kauffman’s (1993) NK-model. We converted N to ‘problem and solution definitions’ (PSDs) and K to ‘connectedness’ between actors (c_score). An important modification is that we consider NK to be a dependent configuration; i.e., K entails both content and process. Fitness is defined as the probability of an actor achieving (elements of) its PSD as a result of its adaptive moves in relation to the adaptive moves of others. The model is put to the test in four different studies: (1) 20 years of decision-making in planning, building and servicing HSL-Zuid high-speed railways in the Netherlands; (2) the strategic search process of villages and cantons in the Gotthard region of Switzerland; (3) the redevelopment of a football stadium and the surrounding area in south Rotterdam, the Netherlands; and (4) the rise and fall of the Airport RailLink in Bangkok, Thailand. From these studies, we derived six archetypes in collection decision-making, subdivided into actor archetypes and interaction archetypes. For the actor archetypes, behavioural consistency is not just a trait for the actor but also affects the space of possibilities and/or behaviours of other actors. The interactions of individual actors combine to produce self-propagating dynamics that drive the further evolution of the collective decision-making process. The fitness field model enables researchers to investigate the various dimensions of the collective decision-making process – ranging from individual strategies and actions to variation, selection and retention of contents, from interactions to fitness gains and losses, and back again

Bayesian network structure learning using characteristic properties of permutation representations with applications to prostate cancer treatment.

Over the last decades, Bayesian Networks (BNs) have become an increasingly popular technique to model data under presence of uncertainty. BNs are probabilistic models that represent relationships between variables by means of a node structure and a set of parameters. Learning efficiently the structure that models a particular dataset is a NP-hard task that requires substantial computational efforts to be successful. Although there exist many families of techniques for this purpose, this thesis focuses on the study and improvement of search and score methods such as Evolutionary Algorithms (EAs). In the domain of BN structure learning, previous work has investigated the use of permutations to represent variable orderings within EAs. In this thesis, the characteristic properties of permutation representations are analysed and used in order to enhance BN structure learning. The thesis assesses well-established algorithms to provide a detailed analysis of the difficulty of learning BN structures using permutation representations. Using selected benchmarks, rugged and plateaued fitness landscapes are identified that result in a loss of population diversity throughout the search. The thesis proposes two approaches to handle the loss of diversity. First, the benefits of introducing the Island Model (IM) paradigm are studied, showing that diversity loss can be significantly reduced. Second, a novel agent-based metaheuristic is presented in which evolution is based on the use of several mutation operators and the definition of a distance metric in permutation spaces. The latter approach shows that diversity can be maintained throughout the search while exploring efficiently the solution space. In addition, the use of IM is investigated in the context of distributed data, a common property of real-world problems. Experiments prove that privacy can be preserved while learning BNs of high quality. Finally, using UK-wide data related to prostate cancer patients, the thesis assesses the general suitability of BNs alongside the proposed learning approaches for medical data modeling. Following comparisons with tools currently used in clinical settings and with alternative classifiers, it is shown that BNs can improve the predictive power of prostate cancer staging tools, a major concern in the field of urology

From effects-based operations to effects-based force : on causality, complex adaptive system, and the biology of war

The author addresses a recent force employment concept called effects-based operations, which first appeared during the 1991 war against Iraq. The attributes of effects-based operations can be grouped around three common, but interrelated elements such as effects focus, advanced technology, and systems thinking. However, the characteristics upon which the common elements are built, such as causality/deduction for effects focus, intangibles/control for advanced technology, and categorisation/analysis for systems thinking bear dangerous simplifications regarding the nature of war. These characterictics are in sharp contrast with war__s frictional nature as outlined by Clausewitz, who stated that effects in war cannot be traced back to single causes, as several concurrent causes are normally at work. Novelty must always be expected in war as friction dims expectations in terms of causality and the ability to achieve desired effects. The author suggests an organic approach to address the challenge posed by war. According to him the emphasis must shift towards learning and adaptation, instead of planning for desired effects. Friction indicates that often it is more important in war how we do things than what things we do, which has a clear practical limitation for the concept of effects-based operations.LEI Universiteit LeidenPolitieke Instituties: Ontwerp, functioneren, effecte

Correlation Analysis of Coupled Fitness Landscapes

The correlation structure of fitness landscapes is a much used measure to characterize and classify various types of landscapes. However, analyzing the correlation structure of fitness landscapes has so far been restricted to static landscapes only. Here, we investigate the correlation structure of coupled, or dynamic, fitness landscapes. Using the NKC model of coevolution, we apply a correlation analysis on various instances of this model and present the results. One of the main goals of this paper is thus to show that a previously introduced correlation analysis can be successfully extended to coupled fitness landscapes. Furthermore, our analysis shows that this provides meaningful and interesting results that can contribute to a better understanding of coevolution in general