Automated Home Oxygen Delivery for Patients with COPD and Respiratory Failure: A New Approach

Long-term oxygen therapy (LTOT) has become standard care for the treatment of patients with chronic obstructive pulmonary disease (COPD) and other severe hypoxemic lung diseases. The use of new portable O-2 concentrators (POC) in LTOT is being expanded. However, the issue of oxygen titration is not always properly addressed, since POCs rely on proper use by patients. The robustness of algorithms and the limited reliability of current oximetry sensors are hindering the effectiveness of new approaches to closed-loop POCs based on the feedback of blood oxygen saturation. In this study, a novel intelligent portable oxygen concentrator (iPOC) is described. The presented iPOC is capable of adjusting the O-2 flow automatically by real-time classifying the intensity of a patient's physical activity (PA). It was designed with a group of patients with COPD and stable chronic respiratory failure. The technical pilot test showed a weighted accuracy of 91.1% in updating the O-2 flow automatically according to medical prescriptions, and a general improvement in oxygenation compared to conventional POCs. In addition, the usability achieved was high, which indicated a significant degree of user satisfaction. This iPOC may have important benefits, including improved oxygenation, increased compliance with therapy recommendations, and the promotion of PA

Estudio de la pausa espiratoria en pacientes con enfermedades obstructivas en proceso de desconexión de la ventilación mecánica

In this work, the flow signal Expiratory Pause (EP) temporal analysis is used in 18 patients with obstructive lung diseases going through spontaneous breathing trial at weaning process. The main objective was to identify the patients who were successfully disconnected (success group: 9 patients), and those who were not (failure and reintubated group: 9 patients). A variable selection stage was done by mean group comparison and step wise variable inclusion, leading to a 3 parameters set: EP time median; cycle time mean; and median absolute deviation of the EP maxima local number. Next, this set was used in a classifier based on linear discriminant analysis, which results in 17 patients (94.4%) correctly classified, with 88.9% of specificity (Sp) and 100% of sensitivity (Se). Finally, applying the leave-one-out cross validation method, results were 88.9% of correctly classified patients (Sp=77.8% and Se=100%). In conclusion, the proposed parameters showed a good performance and could be used to help therapists to wean patients with obstructive diseases.Postprint (published version

Artificial Intelligence for the prediction of weaning readiness outcome in a multi-centrical clinical cohort of mechanically ventilated patients

Quando un paziente soffre di insufficienza respiratoria acuta, viene praticata la ventilazione meccanica (VM) finché questa non riesce a respirare di nuovo in autonomia. Il medico di Terapia Intensiva verifica ogni giorno se la VM può essere interrotta. Questo screening consiste in una prima fase, il Readiness Test (RT), che è composta da vari parametri clinici. Se questo test ha esito positivo, si sottopone il paziente a 30 minuti di respirazione spontanea (SBT). Se anche l'SBT viene superato con successo, la VM viene interrotta. Al contrario, se l’RT o l’SBT falliscono, il paziente rimane in VM e verrà rivalutato il giorno successivo. Quindi ogni giorno possono verificarsi tre scenari mutuamente esclusivi: l’SBT non verrà tentato, l’SBT fallirà o l’SBT avrà successo (portando quindi all’estubazione del paziente). Il modello di intelligenza artificiale sviluppato, è progettato per dedurre fin dalle prime ore del mattino quale dei tre scenari si verificherà probabilmente nel corso della giornata, partendo dai dati clinici del paziente, dalle informazioni raccolte nel diario clinico dei giorni precedenti e dall'intera storia di registrazione minuto-per-minuto dei vari parametri del ventilatore meccanico, provenienti da uno studio osservazionale retrospettivo multicentrico, condotto in Italia nel corso di 27 mesi. Questi dati vengono elaborati con un approccio di Deep Learning, attraverso una topologia di rete neurale multi-sorgente, alimentata da architetture ricorrenti multiple. Gli iper-parametri sono ottimizzati per selezionare il modello desiderato attraverso la convalida incrociata, riservando 36 pazienti su 182 per testare le prestazioni finali del modello su una serie di metriche, tra cui uno score personalizzato progettato per evidenziare l'impatto clinico. Il modello di intelligenza artificiale finale mostra un'accuratezza del 79% [74, 83%], uno score personalizzato di 0,01 [-0,04, 0,05], un MCC di 0,28 [0,17, 0,39], ottenendo un punteggio migliore rispetto agli altri modelli di confronto, tra cui XG Boost, addestrato solo sui dati clinici giornalieri del giorno precedente, che ha avuto un'accuratezza del 61% [56%, 66%], un MCC di 0,14 [0,06, 0,2] e uno score personalizzato di -0,05 [-0,08, -0,01]. Complessivamente, il modello di intelligenza artificiale è in grado di approssimare bene l'attuale gestione clinica giorno per giorno, fornendo suggerimenti al mattino presto. Inoltre, c'è ancora spazio per migliorare l'utilità clinica del modello considerando ulteriori dati di addestramento personalizzati.When someone suffers from acute respiratory failure, mechanical ventilation (MV) is performed until they can breathe on their own again. The doctor checks every day whether the MV can be stopped. This screening consists of a first phase, the Readiness Testing (RT), which includes various clinical parameters. If this test is successful, 30 minutes of spontaneous breathing (SBT) is attempted. If also the SBT is passed successfully, the VM is stopped. On the contrary, if RT or SBT fails, the patient will be re-evaluated the next day. So, every day three mutually exclusive scenarios may happen: SBT will not be attempted, SBT will fail, or SBT will succeed. Our artificial intelligence model is designed to infer early in the morning which of the three scenarios will probably occur during the day, starting from the patient's clinical data, from the information collected in the previous day’s clinical diary, and from whole minute-by-minute recording history of the various parameters of the mechanical ventilator, coming from a retrospective observational multi-centrical study, conducted in Italy over a course of 27 months. Those data are processed with a deep learning approach, through a multi-source neural network topology, powered by multiple recurrent architectures. Hyper-parameters are optimized to select the purposed model through cross-validation, setting aside 36 out of 182 patients for testing final model performance over a variety of metrics, including a custom score designed to highlight clinical impact. The final AI model had an accuracy of 79% [74, 83%], a custom score of 0.01 [-0.04, 0.05], a MCC of 0.28 [0.17, 0.39], scoring better than the other comparison models, including XG Boost that was trained on daily and baseline clinical data of the previous day only, which had an accuracy of 61% [56%, 66%], a MCC of 0.14 [0.06, 0.2] and a custom score of -0.05 [-0.08, -0.01]. Overall, AI model could approximate well what is the current clinical management throughout day-by-day providing suggestions early in the morning. Moreover, there are still space to improve the model clinical utility considering additional tailored training data

Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO

Intelligent classification algorithms in enhancing the performance of support vector machine

Performing feature subset and tuning support vector machine (SVM) parameter processes in parallel with the aim to increase the classification accuracy is the current research direction in SVM. Common methods associated in tuning SVM parameters will discretize the continuous value of these parameters which will result in low classification performance. This paper presents two intelligent algorithms that hybridized between ant colony optimization (ACO) and SVM for tuning SVM parameters and selecting feature subset without having to discretize the continuous values. This can be achieved by simultaneously executing the selection of feature subset and tuning SVM parameters simultaneously. The algorithms are called ACOMVSVM and IACOMV-SVM. The difference between the algorithms is the size of the solution archive. The size of the archive in ACOMV is fixed while in IACOMV, the size of solution archive increases as the optimization procedure progress. Eight benchmark datasets from UCI were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy. The average classification accuracies for the proposed ACOMV–SVM and IACOMV-SVM algorithms are 97.28 and 97.91 respectively. The work in this paper also contributes to a new direction for ACO that can deal with mixed variable ACO

Operations Research & Statistical Learning Methods to Monitor the Progression of Glaucoma and Chronic Diseases

This thesis focuses on advancing operations research and statistical learning methods for medical decision making to improve the care of patients diagnosed with chronic conditions. Because the National Center for Disease Prevention (2020) estimates chronic conditions affect approximately 60% of the US adult population, improving the care of patients with chronic conditions will improve the lives of most Americans. Patients diagnosed with chronic conditions face lifestyle changes, rising treatment costs, and frequently reductions in quality of life. To improve the way in which clinicians treat patients with chronic conditions, treatment decisions can be supplemented by evidenced-based, data driven algorithmic decision-making methods. This thesis provides data-driven methodologies of a general nature that are instantiated for several medical decision-making problems. In chapter two we proactively identify the time of a patient’s primary open angle glaucoma (POAG) progression under high measurement error conditions using a soft voting ensemble classification model. When medical tests have low residual variability (e.g., empirical difference between the patient's true and recorded value is small) they can effectively, without the use of sophisticated methods, identify the patient's current disease phase; however, when medical tests have moderate to high residual variability this may not be the case. We present a solution to the latter case. We find rapid progression disease phases can be proactively identified with the combination of denoising and supervised classification methods. In chapter three, we determine the optimal time to next follow-up appointment for patients with the chronic condition of ocular hypertension (OHTN). Patients with OHTN are at increased risk of developing glaucoma and should be observed over their lifetime. Follow-up appointment schedules that are chosen poorly can result in, at minimum, delay in the detection of a patient’s progression to glaucoma, and at worse, yield poor patient outcomes. To this end, we present a personalized decision support algorithm that uses the fitted Q-iteration reinforcement learning algorithm to recommend personalized time-to-next follow-up schedules that are based on a patient’s medical state. We find personalized follow-up appointments schedules produced by reinforcement learning methods are superior to both 1-year and 2-year fixed interval follow-up appointment schedules. In chapters four and five, we examine and compare several criteria for determining progression from OHTN to POAG and evaluate the use of a collective POAG conversion rule in predicting future occurrences of patients' POAG conversion. We find age, race, and sex are statistically significant determinants in progression for all compared criteria. However, there exists broad conversion discordance between the criteria, as demonstrated by statistically different survival curves and the limited overlap in eyes that progressed by multiple criteria. Ultimately, to permit machine learning models to predict conversion from OHTN to POAG, it is essential to have quantitative reference standards for POAG conversion for researchers to use. Additionally, using the collective POAG conversion rule, we find machine learning models can successfully predict future OHTN conversion events to POAG. This research was conducted in collaboration with clinical disease/domain experts. All the medical decision-making research herein addresses real world healthcare issues, that, if solved, have the potential to improve vision care if implemented. While these methodologies primarily focus on chronic conditions affecting the eyes (e.g., OHTN and POAG), it is important to note that much of the work produced offers methods applicable to other chronic diseases.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169926/1/isaacaj_1.pd

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

Deciphering the mechanisms underlying the role of interleukin-10 in cognitive function

Dissertação de mestrado em Ciências da SaúdeA função cognitiva refere-se aos processos mentais internos críticos para as atividades quotidianas, como a memória. A imunovigilância do cérebro é crucial para a cognição. A ausência de células T e níveis aumentados de citocinas pró-inflamatórias têm sido associados a comprometimentos cognitivos. No entanto, o papel das citocinas anti-inflamatórias, como a interleucina-10 (IL-10), tem sido pouco estudado. Aqui, o efeito da ausência de IL-10 na função cognitiva foi investigado em murganhos fêmeas BALB/c jovens-adultas IL-10 knockout (KO) e irmãs de tipo selvagem (WT). A ausência de IL-10 prejudicou a memória de referência espacial dependente do hipocampo no Barnes-maze test. Curiosamemente, neste teste, os murganhos IL-10 KO mostraram uma redução nas estratégias dependentes do hipocampo principalmente durante as fases de metestro e diestro do ciclo estríco. Não foram observados problemas locomotores nos murganhos IL-10 KO, no entanto, a deficiência de IL-10 comprometeu a exploração no open-field test. Embora a ausência de IL-10 tivesse aumentado os níveis basais de corticosterona e de expressão genética de marcadores pró-inflamatórios no cólon, estes parâmetros não se correlacionaram com o desempenho comportamental. Usando as variáveis comportamentais analisadas, o genótipo (WT vs IL-10 KO) foi classificado por support vector machine models com uma precisão de até 89,3%. Adicionalmente, a ausência de IL-10 diminui o número de neurónios e volume do hipocampo dorsal, mas não do ventral. Além disso, no hipocampo, a deficiênciade IL-10 modulou negativamente a dinâmica das espinhas dendríticas e diminuiu a arborização dendrítica dos neurónios granulares do giro dentado dorsal e ventral e piramidais do cornu ammonis-1 e -3, que são conhecidos por suportar a aprendizagem e memória. Ademais, análises por citometria de fluxo mostraram que a ausência de IL-10 influenciou o perfil leucocitário no sangue pelo aumento do número total de neutrófilos e da sua percentagem dentro dos leucócitos e diminuição da percentagem dentro dos leucócitos de eosinófilos, células natural-killer, células B e células T. Além disso, no sangue, a deficiência de IL-10 aumentou a percentagem de células T CD4+ efetoras de memória, que foram previamente associadas a uma pior função cognitiva em idosos saudáveis. A ausência de IL-10 também aumentou o número total de leucócitos nos nódulos linfáticos cervicais profundos, sugerindo um aumento do recrutamento de células para o sistema linfático do cérebro. Por fim, através de um tratamento antibiótico, um protocolo para a depleção do microbioma intestinal de murganhos IL-10 KO foi otimizado. Após 3 dias de tratamento, os antibióticos reduziram os níveis de expressão genética de16s nas fezessem proliferação fúngica, proporcionando uma etapa inicial para explorar o papel do microbioma intestinal na função cognitiva de murganhos IL-10 KO. No geral, estes resultados não só suportaram que a ausência de IL-10 impactou as habilidades cognitivas, mas também destacaram potenciais mecanismos subjacentes à ação dessa citocina anti-inflamatória, que podem ser contribuintes importantes para o desenvolvimento de novas terapias para comprometimentos cognitivas baseadas em IL-10.Cognitive function refers to internal mental processes critical for daily life activities, such as memory. Brain immune surveillance has proven to be crucial for cognitive function. T cell absence and increased levels of pro-inflammatory cytokines have been associated with impaired cognition. However, the role of anti-inflammatory cytokines, such as interleukin-10 (IL-10), has been poorly studied. Here, the effect of IL-10 absence in cognitive function was investigated in young-adult female BALB/c IL-10 knockout (KO) and wild-type (WT) littermate mice. IL-10 absence impaired the hippocampal-dependent spatial reference memory in the Barnes-maze test. Interestingly, in this test, IL-10 KO mice showed a reduction in hippocampal-dependent strategies mainly during the metestrus and diestrus phases of the estrous cycle. No locomotor disabilities were observed in IL-10 KO mice however IL-10 deficiency impaired exploration in the open-field test. Although IL-10 absence has led to higher basal levels of corticosterone and increased gene expression levels of pro-inflammatory markers in the colon, these parameters did not correlate with the behavioral performance. Using the behavioral variables analyzed, the genotype (WT vsIL-10 KO) was classified by support vector machine models with an accuracy of up to 89.3%. Moreover, IL-10 absence led to a decreased number of neurons and volumetric atrophy of the dorsal, but not of the ventral hippocampus. Additionally, in the hippocampus, IL-10 deficiency negatively modulated the dendritic spine dynamics and decreased the dendritic arborization of dorsal and ventral dentate gyrus granule neurons and cornu ammonis-1 and -3 pyramidal neurons, which are known to support learning and memory. Furthermore, flow cytometry analysis showed that IL-10 absence impacted the leukocyte profile in the blood by increasing the total number of neutrophils and its percentage within leukocytes and decreasing the percentage of eosinophils, natural-killer cells, B cells, and T cells within leukocytes. Also, in the blood, IL-10 deficiency increased the percentage of effector memory CD4+ T cells, which were previously associated with a worst cognitive function of healthy aged individuals. IL-10 absence also increased the total number of leukocytes in the deep cervical lymph nodes, suggesting an increased cell recruitment to the lymphatic system of the brain. Lastly, through an antibiotic treatment, a protocol for gut microbiome depletion of IL-10 KO mice was optimized. After 3 days of treatment, antibiotics reduced the gene expression levels of 16s in the feces without fungal overgrowth, providing an initial step to explore the role of the gut microbiome in the cognitive function of IL-10 KO mice. Overall, these results not only supported that IL-10 absence impacted cognitive abilities but also highlighted the potential mechanisms underlying the action of this anti-inflammatory cytokine, which may be important contributors to the development of new IL-10-based therapies for cognitive impairments.E, por fim, ao Programa Operacional Regional do Norte de Portugal – NORTE 2020 no âmbito dos projetos NORTE-01-0145-FEDER-000013 e NORTE-01-0145-FEDER-000023; à Plataforma de Microscopia Científica do ICVS, membro da infraestrutura nacional da Plataforma Portuguesa de Bioimagem – PPBI no contexto do projeto PPBI-POCI-01-0145-FEDER-022122, ambos ao abrigo do Acordo de Parceria – Portugal 2020, através do Fundo Europeu de Desenvolvimento Regional; e à Fundação para a Ciência e Tecnologia mediante fundos nacionais vinculados aos projetos UIDB/50026/2020 e UIDP/50026/2020 pelo suporte financeiro

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers