No Free Lunch for Noise Prediction

No-free-lunch theorems have shown that learning algorithms cannot be universally good. We show that no free funch exists for noise prediction as well. We show that when the noise is additive and the prior over target functions is uniform, a prior on the noise distribution cannot be updated, in the Bayesian sense, from any finite data set. We emphasize the importance of a prior over the target function in order to justify superior performance for learning systems

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Interactive correction of mislabeled training data

In this paper, we develop a visual analysis method for interactively improving the quality of labeled data, which is essential to the success of supervised and semi-supervised learning. The quality improvement is achieved through the use of user-selected trusted items. We employ a bi-level optimization model to accurately match the labels of the trusted items and to minimize the training loss. Based on this model, a scalable data correction algorithm is developed to handle tens of thousands of labeled data efficiently. The selection of the trusted items is facilitated by an incremental tSNE with improved computational efficiency and layout stability to ensure a smooth transition between different levels. We evaluated our method on real-world datasets through quantitative evaluation and case studies, and the results were generally favorable

Interactive correction of mislabeled training data

In this paper, we develop a visual analysis method for interactively improving the quality of labeled data, which is essential to the success of supervised and semi-supervised learning. The quality improvement is achieved through the use of user-selected trusted items. We employ a bi-level optimization model to accurately match the labels of the trusted items and to minimize the training loss. Based on this model, a scalable data correction algorithm is developed to handle tens of thousands of labeled data efficiently. The selection of the trusted items is facilitated by an incremental tSNE with improved computational efficiency and layout stability to ensure a smooth transition between different levels. We evaluated our method on real-world datasets through quantitative evaluation and case studies, and the results were generally favorable

Determinants for successful deployment of clinical prediction models : a design science research in the Dutch healthcare sector

Whereas the promises of (predictive) analytics in healthcare are clear and extensively reported, the executive practicalities are not. Mapping the factors that have a hand in the implementation and continuation (i.e. deployment) of such projects improves the execution of prediction models and hence improves diagnostic and prognostic healthcare for patients. This research takes a design science approach to create an artifact aimed at successful deployment of clinical prediction models (CPMs). Through a literature review, various factors that play a role in the deployment of CPMs are categorized. Interviews with an extensive expert panel lead to the development of the CRISP-DM Deployment Extension for CPMs. Next to opinions on the importance of each factor, new in-sights are collected on related topics. A case study at a Dutch hospital allows for the testing of the artifact. A gap analysis is conducted, leading to a practical advice in terms of successful deployment. The research concludes with a proposed deployment strategy and a list of eight recommendations that can be considered the determinants for successful deployment of clinical prediction models

DEVELOPING INNOVATIVE SPECTRAL AND MACHINE LEARNING METHODS FOR MINERAL AND LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATASETS

The sustainable exploration of mineral resources plays a significant role in the economic development of any nation. The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification. Multi-sensor datasets such as Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and Digital Elevation Model (DEM) were utilized. The study mapped the hydrothermal alteration minerals derived from Spectral Mapping Methods (SMMs), including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and SIDSAMtan using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski area (India). The SIDSAMtan outperforms SID and SAM in mineral mapping. A spectral similarity matrix of target and non-target classes based optimum threshold selection was developed to implement the SMMs successfully. Three new effective SMMs such as Dice Spectral Similarity Coefficient (DSSC), Kumar-Johnson Spectral Similarity Coefficient (KJSSC), and their hybrid, i.e., KJDSSCtan has been proposed, which outperforms the existing SMMs (i.e., SAM, SID, and SIDSAMtan) in spectral discrimination of spectrally similar minerals. The developed optimum threshold selection and proposed SMMs are recommended for accurate mineral mapping using hyperspectral data. An integrated spectral enhancement and ML methods have been developed to perform automated lithological classification using AVIRIS-NG hyperspectral data. The Support Vector Machine (SVM) outperforms the Random Forest (RF) and Linear Discriminant Analysis (LDA) in lithological classification. The performance of SVM also shows the least sensitivity to the number and uncertainty of training datasets. This study proposed a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks for accurate lithological classification using ML models. Different input features, such as (a) spectral, (b) spectral and transformed spectral, (c) spectral and morphological, (d) spectral and textural, and (e) optimum hybrid, were evaluated for lithological classification. The developed approach has been assessed in the Chattarpur area (India) consists of similar spectral characteristics and poorly exposed rocks, weathered, and partially vegetated terrain. The optimal hybrid input features outperform other input features to accurately classify different rock types using the SVM and RF models, which is ~15% higher than as obtained using spectral input features alone. The developed integrated approach of spectral enhancement and ML algorithms, and a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks, are recommended for accurate lithological classification. The developed methods can be effectively utilized in other remote sensing applications, such as vegetation/forest mapping and soil classification

Classification de décès neurologique par traitement automatique de l’image

Le diagnostic de mort cérébrale est une étape complexe et chronophage lors de l'évaluation des patients en soins intensifs soupçonnés d'être en décès neurologique. Bien que les critères neurologiques cliniques qui déterminent la mort cérébrale soient largement acceptés dans le monde, le diagnostic reste imparfait et l'utilisation de tests auxiliaires tels que la perfusion tomographique cérébrale (CTP) est souvent nécessaire pour le confirmer. L'objectif principal de ce travail était d'explorer la faisabilité de classer la mort cérébrale à partir de scans CTP par le traitement automatique de l’image. Les scans CTP de l'étude prospective canadienne multicentrique de validation du CTP pour le diagnostic de décès neurologique ont été regroupées à partir de 11 sites participants (INDex-CTP, ClinicalTrials.gov, NCT03098511). Des caractéristiques spatiales et temporelles ont été extraites en utilisant une combinaison de deux modules de convolution et utilisées pour prédire la mort neurologique. Les performances du modèle ont également été évaluées sur différentes catégories de blessures cérébrales. Les études de 217 patients ont été utilisées pour entraîner le modèle. Nous rapportons une AUC de 0,79 (IC95 % 0,76-0,82), un score F1 de 0,82 (IC95 % 0,80-0,83), une précision de 0,92 (IC95 % 0,91-0,93), un rappel de 0,76 (CI95 % 0,72-0,79) ainsi qu'une valeur prédictive négative de 0,49 (CI95 % 0,45-0,53). En raison de la petite taille d'échantillon, nous n'avons pas effectué de tests statistiques sur des sous-ensembles de lésions cérébrales, mais avons signalé une valeur prédictive négative du modèle présumé plus élevée sur des blessures cérébrales anoxiques avec 0,82 (CI95 % 0,77-0,87). Ce modèle montre des preuves préliminaires soutenant la faisabilité de développer un réseau neuronal profond pour classer les patients comateux comme étant neurologiquement décédés ou non. Des recherches supplémentaires sont nécessaires pour valider et améliorer le modèle en utilisant des ensembles de données plus vastes et diversifiés.The diagnostic of brain death is a complex and chronophage step when evaluating patients in critical care suspected of being neurologically deceased. Although the clinical neurological criteria that determine brain death are mostly accepted worldwide, the diagnosis remains imperfect and often the use of ancillary tests such as brain computed tomography perfusion (CTP) are required to confirm it. The main objective of this work was to explore the feasibility of classifying brain death from CTP scans using deep learning. CTP studies from a multicenter prospective diagnostic cohort study with the primary objective of evaluating the diagnostic accuracy of neurological death using CTP were pooled from 11 participating sites (INDex-CTP, ClinicalTrials.gov, NCT03098511). Spatial and temporal features were extracted using a combination of two convolution modules and used to predict neurological death. The performance of the model was also evaluated on subsets of cerebral injuries. 217 patients' studies were used to train the model. We report an AUC of 0.79 (IC95% 0.76-0.82), a F1 score of 0.82 (IC95% 0.80-0.83), a precision of 0.92 (IC95% 0.91-0.93), a recall of 0.76 (CI95% 0.72-0.79) as well as a negative predictive value of 0.49 (CI95% 0.45-0.53). Due to a lack of sample size, we did not perform statistical tests on subsets of cerebral injury, but report suspected higher model negative predictive value on anoxic cerebral injury with 0.82 (CI95% 0.77-0.87). This model shows preliminary evidence supporting the feasibility of developing a deep neural network to classify comatose patients as neurologically deceased or not. Additional research is needed to validate and refine the model by employing larger and more diverse datasets

Análisis de la técnica Transfer Learning en Machine Learning a través de un caso de estudio: La clasificación de productos en el Banco Alimentario de La Plata

La presente investigación propone analizar la técnica de transfer learning y la comparación de distintos modelos pre-entrenados, para determinar si estos pueden ser utilizados efectivamente en el caso de estudio, la clasificación de productos para el Banco Alimentario de La Plata, a partir del reconocimiento de imágenes.Facultad de Informátic