3,252 research outputs found

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    PANCREATODUODENECTOMY FOR MALIGNANCY: FACTORS INFLUENCING SURGICAL AND ONCOLOGICAL OUTCOMES

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    Introduction: Fit patients with a resectable pancreatic head adenocarcinoma (PDAC), ampullary adenocarcinoma (AA) or distal cholangiocarcinoma (CC) may be offered pancreatoduodenectomy (PD) with curative-intent. However, perioperative morbidity and cancer recurrence rates are high. This thesis aimed to explore the factors influencing PD outcomes. A focus was placed on nutrition, postoperative complications, and recurrence in AA patients. It is hoped the findings will guide patient selection/consenting and have implications for patient management. Methods: A retrospective cohort study of patients who underwent PD for histologically-confirmed malignancy was carried out (2012-2015). Twenty-nine centres from eight countries were involved. Data on the following were collected: preoperative comorbidities and investigations, neoadjuvant treatment, operative details, postoperative complications, histology, adjuvant treatment, cancer recurrence, palliative treatment, and overall survival (OS). Results: In total, 1484 patients were included; 885 (59.6%), 394 (26.5%) and 205 (13.8%) had PDAC, AA and CC, respectively. Overall morbidity, major morbidity (Clavien-Dindo grade 11 ≥III) and 90-day mortality rates were 53.4%, 16.9% and 3.8%, respectively. A high body mass index (BMI), an American Society of Anesthesiologists (ASA) grade >II and a classic Whipple approach all correlated with morbidity. Additionally, ASA grade >II patients were at increased risk of major morbidity and a raised BMI correlated with a greater risk of pancreatic leak. Almost half of the cohort received nutritional support (NS). Of these, 55.6% received parenteral nutrition (PN). In total, 19.6% of the patients who had an uneventful postoperative recovery received PN. Among the PDAC cohort, commencing adjuvant chemotherapy (AC) correlated with improved OS, and those who experienced major morbidity commenced AC less frequently. Among the AA cohort, 176 patients (44.7%) developed recurrence and the median time-to-recurrence was 14 months. Local only, local and distant, and distant only recurrence affected 34, 41 and 94 patients, respectively (site unknown: 7). A higher number of resected nodes, histological T stage >II, lymphatic invasion, perineural invasion (PNI), peripancreatic fat invasion (PPFI) and ≥1 positive resection margin all correlated with AA recurrence. Further, ≥1 positive margin, PPFI and PNI were associated with reduced time-to-recurrence. Conclusions: A considerable number of the patients that had an uneventful recovery received PN. Patients with a high BMI or ASA grade had worse perioperative outcomes. Those who experienced major morbidity commenced AC less frequently. Numerous histopathological predictors of AA recurrence and reduced time-to-recurrence were identified

    Clinical, immunological and genetic features of histiocytic disorders

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    Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

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    Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice

    Advanced glycation end products and age-related diseases in the general population

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    In this thesis, epidemiological, nutritional, and gut microbiome related studies are presented to illustrate the relation of advanced glycation end products (AGEs) with age-related diseases. The studies are embedded in the Rotterdam Study, a cohort of the Dutch general population of middle-aged and elderly adults. The amount of skin AGEs measured as SAF was used as a representative of the long-term AGE burden. Chapter 1 gives an overview of the whole thesis (Section 1.1) and gives a brief introduction to AGEs and their implications in disease pathophysiology. Chapter 2 focuses on the interplay of AGEs in the skin and clinical and lifestyle factors, and Chapter 3 concerns the link of skin and dietary AGEs with age-related diseases. Chapter 4 discusses the interpretations and implications of the findings, major methodological considerations, and pressing questions for future research

    Analysis and monitoring of single HaCaT cells using volumetric Raman mapping and machine learning

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    No explorer reached a pole without a map, no chef served a meal without tasting, and no surgeon implants untested devices. Higher accuracy maps, more sensitive taste buds, and more rigorous tests increase confidence in positive outcomes. Biomedical manufacturing necessitates rigour, whether developing drugs or creating bioengineered tissues [1]–[4]. By designing a dynamic environment that supports mammalian cells during experiments within a Raman spectroscope, this project provides a platform that more closely replicates in vivo conditions. The platform also adds the opportunity to automate the adaptation of the cell culture environment, alongside spectral monitoring of cells with machine learning and three-dimensional Raman mapping, called volumetric Raman mapping (VRM). Previous research highlighted key areas for refinement, like a structured approach for shading Raman maps [5], [6], and the collection of VRM [7]. Refining VRM shading and collection was the initial focus, k-means directed shading for vibrational spectroscopy map shading was developed in Chapter 3 and exploration of depth distortion and VRM calibration (Chapter 4). “Cage” scaffolds, designed using the findings from Chapter 4 were then utilised to influence cell behaviour by varying the number of cage beams to change the scaffold porosity. Altering the porosity facilitated spectroscopy investigation into previously observed changes in cell biology alteration in response to porous scaffolds [8]. VRM visualised changed single human keratinocyte (HaCaT) cell morphology, providing a complementary technique for machine learning classification. Increased technical rigour justified progression onto in-situ flow chamber for Raman spectroscopy development in Chapter 6, using a Psoriasis (dithranol-HaCaT) model on unfixed cells. K-means-directed shading and principal component analysis (PCA) revealed HaCaT cell adaptations aligning with previous publications [5] and earlier thesis sections. The k-means-directed Raman maps and PCA score plots verified the drug-supplying capacity of the flow chamber, justifying future investigation into VRM and machine learning for monitoring single cells within the flow chamber

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Functional Nanomaterials and Polymer Nanocomposites: Current Uses and Potential Applications

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    This book covers a broad range of subjects, from smart nanoparticles and polymer nanocomposite synthesis and the study of their fundamental properties to the fabrication and characterization of devices and emerging technologies with smart nanoparticles and polymer integration

    Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

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    The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
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