Attribute Interactions in Medical Data Analysis

There is much empirical evidence about the success of naive Bayesian classification (NBC) in medical applications of attribute-based machine learning. NBC assumes conditional independence between attributes. In classification, such classifiers sum up the pieces of class-related evidence from individual attributes, independently of other attributes. The performance, however, deteriorates significantly when the “interactions” between attributes become critical. We propose an approach to handling attribute interactions within the framework of “voting” classifiers, such as NBC. We propose an operational test for detecting interactions in learning data and a procedure that takes the detected interactions into account while learning. This approach induces a structuring of the domain of attributes, it may lead to improved classifier’s performance and may provide useful novel information for the domain expert when interpreting the results of learning. We report on its application in data analysis and model construction for the prediction of clinical outcome in hip arthroplasty

Modeling Big Medical Survival Data Using Decision Tree Analysis with Apache Spark

In many medical studies, an outcome of interest is not only whether an event occurred, but when an event occurred; and an example of this is Alzheimer’s disease (AD). Identifying patients with Mild Cognitive Impairment (MCI) who are likely to develop Alzheimer’s disease (AD) is highly important for AD treatment. Previous studies suggest that not all MCI patients will convert to AD. Massive amounts of data from longitudinal and extensive studies on thousands of Alzheimer’s patients have been generated. Building a computational model that can predict conversion form MCI to AD can be highly beneficial for early intervention and treatment planning for AD. This work presents a big data model that contains machine-learning techniques to determine the level of AD in a participant and predict the time of conversion to AD. The proposed framework considers one of the widely used screening assessment for detecting cognitive impairment called Montreal Cognitive Assessment (MoCA). MoCA data set was collected from different centers and integrated into our large data framework storage using a Hadoop Data File System (HDFS); the data was then analyzed using an Apache Spark framework. The accuracy of the proposed framework was compared with a semi-parametric Cox survival analysis model

Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis'). The manuscript is available from following link (https://doi.org/10.1016/j.media.2019.06.005

Computer analysis of medical image data

Táto práca sa zaoberá analýzou medicínskych obrazových dát pomocou rôznych štatistických a numerických metód implementovaných v prostrediach Eclipse a Rapidminer využitím jazyku Java. Použité sú sady snímok (rezov), ktoré sú výsledkom vyšetrenia mozgu rôznych pacientov magnetickou rezonanciou. Jednotlivé segmenty v tomto 3D obraze sú podrobené výpočtu niekoľkých lokálnych príznakov, na základe ktorých sú vygenerované dátové sady pre použitie v trénovacích algoritmoch. Schopnosť týchto alogritmov úspešne identifikovať zdravé alebo choré tkanivo je následne otestovaná prakticky na dostupných dátach.This work deals with medical image analysis, using variety of statisic and numeric methods implemented in Eclipse and Rapidminer environments in Java programming language. Sets of images (slices), which are used here, are the results of magnetic resonance brain examination of several subejcts. Segments in this 3D image are analyzed and some local features are computed, based on which data sets for use in training algorythms are generated. The ability of successful identification of healthy or unhealthy tissues is then practically tested using available data.

Quality assessment of medical record as a tool for clinical risk management: a three year experience of a teaching hospital Policlinico Umberto I, Rome

Introduction: The medical record was defined by the Italian Ministry of Health in 1992 as "the information tool designed to record all relevant demographic and clinical information on a patient during a single hospitalization episode". Retrospective analysis of medical records is a tool for selecting direct and indirect indicators of critical issues (organizational, management and technical). The project’s aim being the promotion of an evaluation and self-evaluation process of medical records as a Clinical Risk Management tool to improve the quality of care within hospitals. Methods: The Authors have retrospectively analysed, using a validated grid, 1,184 medical records of patients admitted to the Teaching Hospital “Umberto I” in Rome during a three-year period (2013-2015). Statistical analysis was performed using SPSS for Windows © 19:00. All duly filled out criteria (92) were examined. “Strengths” and "Weaknesses" were identified through data analysis and Best and Bad Practice were identified based on established criteria. Conclusion: The data analysis showed marked improvements (statistically significant) in the quality of evaluated clinical documentation and indirectly upon behaviour. However, when examining some sub-criteria, critical issues emerge; these could be subject to future further corrective action