A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees

International Conference Image Analysis and Recognition (ICIAR 2018, Póvoa de Varzim, Portugal

Data mining for prediction of length of stay of cardiovascular accident inpatients

The healthcare sector generates large amounts of data on a daily basis. This data holds valuable knowledge that, beyond supporting a wide range of medical and healthcare functions such as clinical decision support, can be used for improving profits and cutting down on wasted overhead. The evaluation and analysis of stored clinical data may lead to the discovery of trends and patterns that can significantly enhance overall understanding of disease progression and clinical management. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a data mining project approach to predict the hospitalization period of cardiovascular accident patients. This provides an effective tool for the hospital cost containment and management efficiency. The data used for this project contains information about patients hospitalized in Cardiovascular Accident’s unit in 2016 for having suffered a stroke. The Weka software was used as the machine learning toolkit.Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2013

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

Agent-Based Modelling as a Foundation for Big Data

In this article we propose a process-based definition of big data, as opposed to the size - and technology-based definitions. We argue that big data should be perceived as a continu- ous, unstructured and unprocessed dynamics of primitives, rather than as points (snapshots) or summaries (aggregates) of an underlying phenomenon. Given this, we show that big data can be generated through agent-based models but not by equation-based models. Though statistical and machine learning tools can be used to analyse big data, they do not constitute a big data-generation mechanism. Furthermore, agent-based models can aid in evaluating the quality (interpreted as information aggregation efficiency) of big data. Based on this, we argue that agent-based modelling can serve as a possible foundation for big data. We substantiate this interpretation through some pioneering studies from the 1980s on swarm intelligence and several prototypical agent-based models developed around the 2000s

Gazi Husrev-Begova Sahat-Kula i Muvekithana i Način Mjerenja.

Donated by Klaus KreiserReprinted from in : Anali Gazi Husrev Begove Biblioteke, 2-3. 1974

Interpretable Deep Learning Model for Prostate Cancer Detection

Prostate cancer is the second leading cause of cancer death in American men, behind only lung cancer. Detecting prostate cancer early and accurately are key factors in preventing these deaths. Progress has been made in creating deep learning systems that are able to detect prostate cancer with a high degree of accuracy. However, an indispensable problem with these systems is while the performance can be exceptionally accurate, the classification outputs are non-interpretable. This non-interpretable characteristic significantly inhibits these models from being implemented in medical settings. We address this problem of interpretability of deep learning systems in the domain of prostate cancer detection. We develop a deep convolutional neural network based on the VGG16 architecture for the classification of prostate cancer lesions using T2 weighted magnetic resonance images. Our model achieves high level performance with an AUC of 0.86, sensitivity of 0.88, and specificity of 0.88. We use saliency maps for interpretation by calculating how much each individual pixel contributes to the overall class scores. We show the clusters of pixels that contribute the most to the prediction thus showing the reasoning behind the classification. We then show the interpretation caliber to demonstrate the exactness of the interpretation. This work demonstrates the potential to use saliency maps to interpret classifications of deep learning prostate cancer detection systems