101 research outputs found
Medical analysis and diagnosis by neural networks
In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic systems. Then, paradigm of neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the neuro-fuzzy approach is presented. Finally, as case study of neural rule based diagnosis septic shock diagnosis is described, on one hand by a growing neural network and on the other hand by a rule based system. Keywords: Statistical Classification, Adaptive Prediction, Neural Networks, Neurofuzzy, Medical System
On the intelligent management of sepsis in the intensive care unit
The management of the Intensive Care Unit (ICU) in a hospital has its own, very specific requirements that involve, amongst
others, issues of risk-adjusted mortality and average length of stay; nurse turnover and communication with physicians; technical
quality of care; the ability to meet patient's family needs; and avoid medical error due rapidly changing circumstances and work
overload. In the end, good ICU management should lead to an improvement in patient outcomes.
Decision making at the ICU environment is a real-time challenge that works according to very tight guidelines, which relate to
often complex and sensitive research ethics issues. Clinicians in this context must act upon as much available information as
possible, and could therefore, in general, benefit from at least partially automated computer-based decision support based on
qualitative and quantitative information. Those taking executive decisions at ICUs will require methods that are not only reliable,
but also, and this is a key issue, readily interpretable. Otherwise, any decision tool, regardless its sophistication and accuracy,
risks being rendered useless.
This thesis addresses this through the design and development of computer based decision making tools to assist clinicians at
the ICU. It focuses on one of the main problems that they must face: the management of the Sepsis pathology. Sepsis is one of
the main causes of death for non-coronary ICU patients. Its mortality rate can reach almost up to one out of two patients for
septic shock, its most acute manifestation. It is a transversal condition affecting people of all ages. Surprisingly, its definition has
only been standardized two decades ago as a systemic inflammatory response syndrome with confirmed infection.
The research reported in this document deals with the problem of Sepsis data analysis in general and, more specifically, with the
problem of survival prediction for patients affected with Severe Sepsis. The tools at the core of the investigated data analysis
procedures stem from the fields of multivariate and algebraic statistics, algebraic geometry, machine learning and computational
intelligence.
Beyond data analysis itself, the current thesis makes contributions from a clinical point of view, as it provides substantial
evidence to the debate about the impact of the preadmission use of statin drugs in the ICU outcome. It also sheds light into the
dependence between Septic Shock and Multi Organic Dysfunction Syndrome. Moreover, it defines a latent set of Sepsis
descriptors to be used as prognostic factors for the prediction of mortality and achieves an improvement on predictive capability
over indicators currently in use.La gestió d'una Unitat de Cures Intensives (UCI) hospitalària presenta uns requisits força específics incloent, entre altres, la disminució de la taxa de mortalitat, la durada de l'ingrès, la rotació d'infermeres i la comunicació entre metges amb al finalitad de donar una atenció de qualitat atenent als requisits tant dels malalts com dels familiars. També és força important controlar i minimitzar els error mèdics deguts a canvis sobtats i a la presa ràpida de deicisions assistencials. Al cap i a la fi, la bona gestió de la UCI hauria de resultar en una reducció de la mortalitat i durada d'estada.
La presa de decisions en un entorn de crítics suposa un repte de presa de decisions en temps real d'acord a unes guies clíniques molt restrictives i que, pel que fa a la recerca, poden resultar en problemes ètics força sensibles i complexos. Per tant, el personal sanitari que ha de prendre decisions sobre la gestió de malalts crítics no només requereix eines de suport a la decisió que siguin fiables sinó que, a més a més, han de ser interpretables. Altrament qualsevol eina de decisió que no presenti aquests trets no és considerarà d'utilitat clínica.
Aquesta tesi doctoral adreça aquests requisits mitjançant el desenvolupament d'eines de suport a la decisió per als intensivistes i
es focalitza en un dels principals problemes als que s'han denfrontar: el maneig del malalt sèptic. La Sèpsia és una de les principals causes de mortalitats a les UCIS no-coronàries i la seva taxa de mortalitat pot arribar fins a la meitat dels malalts amb xoc sèptic, la seva manifestació més severa. La Sèpsia és un síndrome transversal, que afecta a persones de totes les edats. Sorprenentment, la seva definició ha estat estandaritzada, fa només vint anys, com a la resposta inflamatòria sistèmica a una infecció corfimada.
La recerca presentada en aquest document fa referència a l'anàlisi de dades de la Sèpsia en general i, de forma més específica, al problema de la predicció de la supervivència de malalts afectats amb Sèpsia Greu. Les eines i mètodes que formen la clau de bòveda d'aquest treball provenen de diversos camps com l'estadística multivariant i algebràica, geometria algebraica, aprenentatge automàtic i inteligència computacional.
Més enllà de l'anàlisi per-se, aquesta tesi també presenta una contribució des de el punt de vista clínic atès que presenta evidència substancial en el debat sobre l'impacte de l'administració d'estatines previ a l'ingrès a la UCI en els malalts sèptics. També s'aclareix la forta dependència entre el xoc sèptic i el Síndrome de Disfunció Multiorgànica. Finalment, també es defineix un conjunt de descriptors latents de la Sèpsia com a factors de pronòstic per a la predicció de la mortalitat, que millora sobre els mètodes actualment més utilitzats en la UCI
Severe community-acquired pneumonia in the intensive care unit
Introduction Community-acquired pneumonia remains a common condition worldwide. It is associated with significant morbidity and mortality. The aim of this study was to evaluate conditions that could predict a poor outcome
Fuzzy set covering as a new paradigm for the induction of fuzzy classification rules
In 1965 Lofti A. Zadeh proposed fuzzy sets as a generalization of crisp (or classic) sets to address the incapability of crisp sets to model uncertainty and vagueness inherent in the real world. Initially, fuzzy sets did not receive a very warm welcome as many academics stood skeptical towards a theory of imprecise'' mathematics. In the middle to late 1980's the success of fuzzy controllers brought fuzzy sets into the limelight, and many applications using fuzzy sets started appearing. In the early 1970's the first machine learning algorithms started appearing. The AQ family of algorithms pioneered by Ryszard S. Michalski is a good example of the family of set covering algorithms. This class of learning algorithm induces concept descriptions by a greedy construction of rules that describe (or cover) positive training examples but not negative training examples. The learning process is iterative, and in each iteration one rule is induced and the positive examples covered by the rule removed from the set of positive training examples. Because positive instances are separated from negative instances, the term separate-and-conquer has been used to contrast the learning strategy against decision tree induction that use a divide-and-conquer learning strategy. This dissertation proposes fuzzy set covering as a powerful rule induction strategy. We survey existing fuzzy learning algorithms, and conclude that very few fuzzy learning algorithms follow a greedy rule construction strategy and no publications to date made the link between fuzzy sets and set covering explicit. We first develop the theoretical aspects of fuzzy set covering, and then apply these in proposing the first fuzzy learning algorithm that apply set covering and make explicit use of a partial order for fuzzy classification rule induction. We also investigate several strategies to improve upon the basic algorithm, such as better search heuristics and different rule evaluation metrics. We then continue by proposing a general unifying framework for fuzzy set covering algorithms. We demonstrate the benefits of the framework and propose several further fuzzy set covering algorithms that fit within the framework. We compare fuzzy and crisp rule induction, and provide arguments in favour of fuzzy set covering as a rule induction strategy. We also show that our learning algorithms outperform other fuzzy rule learners on real world data. We further explore the idea of simultaneous concept learning in the fuzzy case, and continue to propose the first fuzzy decision list induction algorithm. Finally, we propose a first strategy for encoding the rule sets generated by our fuzzy set covering algorithms inside an equivalent neural network
Fuzzy set covering as a new paradigm for the induction of fuzzy classification rules
In 1965 Lofti A. Zadeh proposed fuzzy sets as a generalization of crisp (or classic) sets to address the incapability of crisp sets to model uncertainty and vagueness inherent in the real world. Initially, fuzzy sets did not receive a very warm welcome as many academics stood skeptical towards a theory of imprecise'' mathematics. In the middle to late 1980's the success of fuzzy controllers brought fuzzy sets into the limelight, and many applications using fuzzy sets started appearing. In the early 1970's the first machine learning algorithms started appearing. The AQ family of algorithms pioneered by Ryszard S. Michalski is a good example of the family of set covering algorithms. This class of learning algorithm induces concept descriptions by a greedy construction of rules that describe (or cover) positive training examples but not negative training examples. The learning process is iterative, and in each iteration one rule is induced and the positive examples covered by the rule removed from the set of positive training examples. Because positive instances are separated from negative instances, the term separate-and-conquer has been used to contrast the learning strategy against decision tree induction that use a divide-and-conquer learning strategy. This dissertation proposes fuzzy set covering as a powerful rule induction strategy. We survey existing fuzzy learning algorithms, and conclude that very few fuzzy learning algorithms follow a greedy rule construction strategy and no publications to date made the link between fuzzy sets and set covering explicit. We first develop the theoretical aspects of fuzzy set covering, and then apply these in proposing the first fuzzy learning algorithm that apply set covering and make explicit use of a partial order for fuzzy classification rule induction. We also investigate several strategies to improve upon the basic algorithm, such as better search heuristics and different rule evaluation metrics. We then continue by proposing a general unifying framework for fuzzy set covering algorithms. We demonstrate the benefits of the framework and propose several further fuzzy set covering algorithms that fit within the framework. We compare fuzzy and crisp rule induction, and provide arguments in favour of fuzzy set covering as a rule induction strategy. We also show that our learning algorithms outperform other fuzzy rule learners on real world data. We further explore the idea of simultaneous concept learning in the fuzzy case, and continue to propose the first fuzzy decision list induction algorithm. Finally, we propose a first strategy for encoding the rule sets generated by our fuzzy set covering algorithms inside an equivalent neural network
Designing Hydrogel-Based Bone-On-Chips for Personalized Medicine
The recent development of bone-on-chips (BOCs) holds the main advantage of requiring a low quantity of cells and material, compared to traditional In Vitro models. By incorporating hydrogels within BOCs, the culture system moved to a three dimensional culture environment for cells which is more representative of bone tissue matrix and function. The fundamental components of hydrogel-based BOCs, namely the cellular sources, the hydrogel and the culture chamber, have been tuned to mimic the hematopoietic niche in the bone aspirate marrow, cancer bone metastasis and osteo/chondrogenic differentiation. In this review, we examine the entire process of developing hydrogel-based BOCs to model In Vitro a patient specific situation. First, we provide bone biological understanding for BOCs design and then how hydrogel structural and mechanical properties can be tuned to meet those requirements. This is followed by a review on hydrogel-based BOCs, developed in the last 10 years, in terms of culture chamber design, hydrogel and cell source used. Finally, we provide guidelines for the definition of personalized pathological and physiological bone microenvironments. This review covers the information on bone, hydrogel and BOC that are required to develop personalized therapies for bone disease, by recreating clinically relevant scenarii in miniaturized device
Melatonin, sleep and circadian rhythms in critical care patients.
Critical care patients commonly experience sleep fragmentation, in which sleep
quality is poor and distributed throughout the 24 hour cycle. This irregular sleep
wake pattern is a form of circadian rhythm sleep disorder. The causes of sleep
disturbances are multifactorial and contribute to patient morbidity. Conventional
hypnotic treatment is often ineffective and, indeed, may cause delirium and reduced
sleep quality.
Administration of exogenous melatonin has been shown to re-enforce circadian
rhythm disorders and improve sleep in other patient groups.
An open evaluation of 5 mg oral melatonin was undertaken in a group of 12 critical
care patients exhibiting sleep disturbances resistant to conventional hypnotics.
Melatonin significantly increased observed sleep quantity by night 3, compared to
baseline.
An oral solution of melatonin was formulated to allow administration by enteral
feeding tubes. It was shown to have a 1 year shelf life when refrigerated and
protected from light.
A randomised controlled trial was undertaken in 24 critical care patients weaning
from mechanical ventilation. Melatonin 10 mg orally increased nocturnal bispectral
index sleep quantity over nights 3 and 4 compared to placebo. Agreement of the
other sleep measurement techniques with the bispectral index was poor. Actigraphy
was not a useful measure of sleep in critical care patients and nurse observation
overestimated sleep quantity.
The clearance of melatonin appeared to be decreased in critical care patients
compared to that in healthy subjects. Doses of 1-2 mg should be used in future
critical care studies.
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Acute administration of melatonin did not have a significant effect over placebo on
rest-activity rhythms, which remained delayed, fragmented and reduced. Similar
disturbances were present in plasma melatonin and cortisol rhythms, which were no
longer phase locked.
Melatonin therapy may prove beneficial in the treatment of sleep and circadian
rhythms in critical care patients, and further larger studies should be pursued
2023 Medical Student Research Day Abstracts
Medical student research day is designed to highlight the breadth of research and scholarly activity that medical students have accomplished during their education at The GW School of Medicine and Health Sciences. All medical students are invited to present research regardless of the area of focus. Abstract submissions represent a broad range of research interests and disciplines, including basic and translational science, clinical research, health policy and public health research, and education-related research
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