Breast Cancer Early Detection Comparison with Deep Learning and Machine Learning Models: A Case of Study

Breast cancer is one of the most widespread in the female population, being able to predict its developments and capturing the inputs of the onset of the disease is one of the main objectives that science is pursuing. Clinical Decision Support Systems (CDSS) in recent decades are extensively using these technological tools, such as Machine Learning (ML) and Deep Learning (DL). In this paper, two of the main methods of these subset of AI are compared: an ensemble-type algorithm, XGBoost (or Extreme Gradient Boosting) and a deep neural network (DNN) are applied to the data of a study conducted on an Indonesian population. The results obtained are very interesting as despite being tabular, binary categorical and multiclass data, the DNN model achieves performance and results much higher than the well-known XGB used in literature for data of this type

Explainable clinical decision support system: opening black-box meta-learner algorithm expert's based

Mathematical optimization methods are the basic mathematical tools of all artificial intelligence theory. In the field of machine learning and deep learning the examples with which algorithms learn (training data) are used by sophisticated cost functions which can have solutions in closed form or through approximations. The interpretability of the models used and the relative transparency, opposed to the opacity of the black-boxes, is related to how the algorithm learns and this occurs through the optimization and minimization of the errors that the machine makes in the learning process. In particular in the present work is introduced a new method for the determination of the weights in an ensemble model, supervised and unsupervised, based on the well known Analytic Hierarchy Process method (AHP). This method is based on the concept that behind the choice of different and possible algorithms to be used in a machine learning problem, there is an expert who controls the decisionmaking process. The expert assigns a complexity score to each algorithm (based on the concept of complexity-interpretability trade-off) through which the weight with which each model contributes to the training and prediction phase is determined. In addition, different methods are presented to evaluate the performance of these algorithms and explain how each feature in the model contributes to the prediction of the outputs. The interpretability techniques used in machine learning are also combined with the method introduced based on AHP in the context of clinical decision support systems in order to make the algorithms (black-box) and the results interpretable and explainable, so that clinical-decision-makers can take controlled decisions together with the concept of "right to explanation" introduced by the legislator, because the decision-makers have a civil and legal responsibility of their choices in the clinical field based on systems that make use of artificial intelligence. No less, the central point is the interaction between the expert who controls the algorithm construction process and the domain expert, in this case the clinical one. Three applications on real data are implemented with the methods known in the literature and with those proposed in this work: one application concerns cervical cancer, another the problem related to diabetes and the last one focuses on a specific pathology developed by HIV-infected individuals. All applications are supported by plots, tables and explanations of the results, implemented through Python libraries. The main case study of this thesis regarding HIV-infected individuals concerns an unsupervised ensemble-type problem, in which a series of clustering algorithms are used on a set of features and which in turn produce an output used again as a set of meta-features to provide a set of labels for each given cluster. The meta-features and labels obtained by choosing the best algorithm are used to train a Logistic regression meta-learner, which in turn is used through some explainability methods to provide the value of the contribution that each algorithm has had in the training phase. The use of Logistic regression as a meta-learner classifier is motivated by the fact that it provides appreciable results and also because of the easy explainability of the estimated coefficients

Cervical cancer risk prediction with robust ensemble and explainable black boxes method

Clinical decision support systems (CDSS) that make use of algorithms based on intelligent systems, such as machine learning or deep learning, they sufer from the fact that often the methods used are hard to interpret and difcult to understand on how some decisions are made; the opacity ofsome methods, sometimes voluntary due to problems such as data privacy or the techniques used to protect intellectual property, makes these systems very complicated. Besides this series of problems, the results obtained also sufer from the poor possibility of being interpreted; in the clinical context therefore it is required that the methods used are as accurate as possible, transparent techniques and explainable results. In this work the problem of the development of cervical cancer is treated, a disease that mainly afects the female population. In order to introduce advanced machine learning techniques in a clinical decision support system that can be transparent and explainable, a robust, accurate ensemble method is presented, in terms of error and sensitivity linked to the classifcation of possible development of the aforementioned pathology and advanced techniques are also presented of explainability and interpretability (Explanaible Machine Learning) applied to the context of CDSS such as Lime and Shapley. The results obtained, as well as being interesting, are understandable and can be implemented in the treatment of this type of problem

Diaphragm Pacing in Patients with Spinal Cord Injury:A European Experience

BACKGROUND: Patients with high spinal cord injury (SCI) are unable to breathe on their own and require mechanical ventilation (MV). The long-term use of MV is associated with increased morbidity and mortality. In patients with intact phrenic nerve function, patients can be partially or completely removed from MV by directly stimulating the diaphragm motor points with a diaphragm pacing system (DPS). OBJECTIVES: We describe our multicenter European experience using DPS in SCI patients who required MV. METHODS: We conducted a retrospective study of patients who were evaluated for the implantation of DPS. Patients evaluated for DPS who met the prospectively defined criteria of being at least 1 year of age, and having cervical injury resulting in a complete or partial dependency on MV were included. Patients who received DPS implants were followed for up to 1 year for device usage and safety. RESULTS: Across 3 centers, 47 patients with high SCI were evaluated for DPS, and 34 were implanted. Twenty-one patients had 12 months of follow-up data with a median DPS use of 15 h/day (interquartile range 4, 24). Eight patients (38.1%) achieved complete MV weaning using DPS 24 h/day. Two DPS-related complications were surgical device revision and a wire eruption. No other major complications were associated with DPS use. CONCLUSIONS: Diaphragm pacing represents an attractive alternative stand-alone treatment or adjunctive therapy compared to MV in patients with high SCI. After a period of acclimation, the patients were able to reduce the daily use of MV, and many could be completely removed from MV

nanoparticles production and inclusion in s aureus incubated with polyurethane an electron microscopy analysis

This study shows that submicron/nanoparticles found in bacterial cells (S. aureus) incubated with polyurethane (a material commonly used for prostheses in odontostomatology) are a consequence of biodestruction. The presence of polyurethane nanoparticles into bacterial vesicles suggests that the internalization process occurs through endocytosis. TEM and FIB/SEM are a suitable set of correlated instruments and techniques for this multi facet investigation: polyurethane particles influence the properties of S. aureus from the morpho-functional standpoint that may have undesirable effects on the human body. S. aureus and C. albicans are symbiotic microorganisms; it was observed that C. albicans has a similar interaction with polyurethane and an increment of the biodestruction capacity is expected by its mutual work with S. aureus

Does Pilocarpine-Induced Epilepsy in Adult Rats Require Status epilepticus?

Pilocarpine-induced seizures in rats provide a widely animal model of temporal lobe epilepsy. Some evidences reported in the literature suggest that at least 1 h of status epilepticus (SE) is required to produce subsequent chronic phase, due to the SE-related acute neuronal damage. However, recent data seems to indicate that neuro-inflammation plays a crucial role in epileptogenesis, modulating secondarily a neuronal insult. For this reason, we decided to test the following hypotheses: a) whether pilocarpine-injected rats that did not develop SE can exhibit long-term chronic spontaneous recurrent seizures (SRS) and b) whether acute neurodegeneration is mandatory to obtain chronic epilepsy. Therefore, we compared animals injected with the same dose of pilocarpine that developed or did not SE, and saline treated rats. We used telemetric acquisition of EEG as long-term monitoring system to evaluate the occurrence of seizures in non-SE pilocarpineinjected animals. Furthermore, histology and MRI analysis were applied in order to detect neuronal injury and neuropathological signs. Our observations indicate that non-SE rats exhibit SRS almost 8 (+/22) months after pilocarpine-injection, independently to the absence of initial acute neuronal injury. This is the first time reported that pilocarpine injected rats without developing SE, can experience SRS after a long latency period resembling human pathology. Thus, we strongly emphasize the important meaning of including these animals to model human epileptogenesis in pilocarpine induced epilepsy

Features and explainable methods for cytokines analysis of Dry Eye Disease in HIV infected patients

Clinical Decision Support Systems (CDSS) that use machine learning techniques and their broadest sense of artificial intelligence (AI) must be interpretable and transparent. The lack of transparency instead of providing support could instead become a factor of indecision and obstacle. In this work, a very complex and important problem from a clinical point of view is tackled, namely the pathology known as Dry Eye Disease (DED), starting from a case-control study on an HIV-positive population and a healthy part of it. The case study is faced on two fronts, the first in which an ensemble-based clustering algorithm is built. Secondly, this algorithm is broken down to analyze each component, making the analysis method transparent and interpretable. Specifically, an ensemble of clustering algorithms is presented, such as k-means, agglomerative, spectral, and birch, which are combined and used in two levels: in the first, the labels are obtained from each clusterizer to recognize significant patterns of the two populations affected by the DED pathology, in the presence of HIV and not. Subsequently, the labels obtained at the first level are used as inputs on which the clusterizers are used again, whose outputs in the final phase serve as a training data set for a supervised method (i.e., logistic regression, decision trees, neural network, etc.), to evaluate every single component separately, through the use of features importance techniques (i.e., decision trees, LASSO regression, Gini Importance (GI), Variable Importance (VI), etc.). In this way, each clustering algorithm used at the first level can be considered a new feature in the next one and evaluate its individual contribution. Furthermore, each characteristic is interpreted through specific methods of the relevance of the characteristics to make the decision support tool as complete as possible. The performance of the methods used in training, both supervised and unsupervised, are evaluated through appropriate metrics, such as the well-known measures of precision, recall, accuracy, and homogeneity. Clustering methods provide results on the groups created and on the influence of features (cytokines) in the two populations examined. The experimental results obtained concerning the association between the development of the DED pathology and the presence or absence of HIV in these patients, and the influence that certain factors have on this problem, are interpreted with methods that are part of that branch known as Explainable AI (i.e., Local Interpretable Model-agnostic Explanations (LIME), Shapley, Individual Conditional Expectation (ICE), etc.). Besides explaining the influence exerted by certain features, the methods used provide both a global and local view on how each factor influences the final probability associated with the possible development of the pathology. The practical implications in using this method can be of support to the clinical diagnoses carried out on the patients examined to evaluate how each factor can be responsible for the possible development of the disease and therefore taken individually in the treatment. To date, the analytical techniques used in the study of this pathology have always provided generalized results, while breaking down the problem and isolating the components could provide valuable information to clinical operators

Unsupervised Hybrid Algorithm to Detect Anomalies for Predicting Terrorists Attacks

This work presents a hybrid approach for unsupervised algorithms (UHA), in order to extract information and patterns from data concerning terrorist attacks. The reference data are those of the Global Terrorism Database. The work presents an approach based on autoencoders and k-modes type clustering. The results obtained are examined through some metrics presented in the article and it is also considered methodologically how to determine a robust threshold for anomaly detection problems

Investigations on Arthropods Associated with Decay Stages of Buried Animals in Italy

Burial could be used by criminals to conceal the bodies of victims, interfering with the succession of sarcosaprophagous fauna and with the evaluation of post-mortem interval. In Italy, no experimental investigation on arthropods associated with buried remains has been conducted to date. A first experimental study on arthropods associated with buried carcasses was carried out in a rural area of Arcavacata di Rende (Cosenza), Southern Italy, from November 2017 to May 2018. Six pig carcasses (Susscrofa Linnaeus) were used, five of which were buried in 60-cm deep pits, leaving about 25-cm of soil above each carcass, and one was left above ground. One of the buried carcasses was periodically exhumed to evaluate the effects of disturbance on decay processes and on arthropod fauna. The other four carcasses were exhumed only once, respectively after 43, 82, 133, and 171 days. As expected, the decay rate was different among carcasses. Differences in taxa and colonization of arthropod fauna were also detected in the above ground and periodically exhumed carcasses. In carcasses exhumed only once, no arthropod colonization was detected. The results showed that a burial at about 25 cm depth could be sufficient to prevent colonization by sarcosaprophagous taxa and these data could be relevant in forensic cases involving buried corpses