Prediction Scores as a Window into Classifier Behavior

Most multi-class classifiers make their prediction for a test sample by scoring the classes and selecting the one with the highest score. Analyzing these prediction scores is useful to understand the classifier behavior and to assess its reliability. We present an interactive visualization that facilitates per-class analysis of these scores. Our system, called Classilist, enables relating these scores to the classification correctness and to the underlying samples and their features. We illustrate how such analysis reveals varying behavior of different classifiers. Classilist is available for use online, along with source code, video tutorials, and plugins for R, RapidMiner, and KNIME at https://katehara.github.io/classilist-site/.Comment: Presented at NIPS 2017 Symposium on Interpretable Machine Learnin

IOT system for Bluetooth based Origin-Destination studies

Designing and modelling a transportation system is a complicated, yet crucial task that demands comprehensive study of public needs. An important aspect of the process is specifying the characteristics of traffic schemes, which include vehicle classification, origin/destination (O/D), travel time (TT), and vehicle occupancy, in addition to other factors. A more thorough understanding of these factors will lead to improved transportation planning. This thesis proposes the development of an Internet of Things (IoT) system that integrates two systems, namely Bluetooth (BT) identification and vehicle classification, for monitoring route choices per vehicle class. The extant system consists of one BT identification/vehicle classification unit deployed at an Oklahoma port of entry, along with a number of BT identification stations deployed at various locations across Oklahoma’s roadways. As vehicles travel over magnetometer nodes, sensors measure changes in magnetic field (i.e., vehicle magnetic signature) for defining each vehicle’s time of arrival and time of departure. Stated times will be used to estimate magnetic length of a passing vehicle for the purpose of classifying the vehicle. During this process, the BT ID of detected BT mobile devices in the vehicle is captured using BT identification stations. Algorithms were developed to associate detected BT addresses to the corresponding vehicles with exceptional accuracy. BT addresses are planned to be sent alongside the vehicle group to a server where they will be matched by multiple stations as the vehicle travels on observed roadways. Hence, active monitoring of route choice and TT per vehicle class is achieved using only inexpensive BT stations

Identifying COPD in routinely collected electronic health records: a systematic scoping review

Although routinely collected electronic health records (EHRs) are widely used to examine outcomes related to COPD, consensus regarding the identification of cases from electronic healthcare databases is lacking. We systematically examine and summarise approaches from the recent literature. MEDLINE via EBSCOhost was searched for COPD-related studies using EHRs published from January 1, 2018 to November 30, 2019. Data were extracted relating to the case definition of COPD and determination of COPD severity and phenotypes. From 185 eligible studies, we found widespread variation in the definitions used to identify people with COPD in terms of code sets used (with 20 different code sets in use based on the ICD-10 classification alone) and requirement of additional criteria (relating to age (n=139), medication (n=31), multiplicity of events (n=21), spirometry (n=19) and smoking status (n=9)). Only seven studies used a case definition which had been validated against a reference standard in the same dataset. Various proxies of disease severity were used since spirometry results and patient-reported outcomes were not often available. To enable the research community to draw reliable insights from EHRs and aid comparability between studies, clear reporting and greater consistency of the definitions used to identify COPD and related outcome measures is key

Bias Mitigation Framework for Intersectional Subgroups in Neural Networks

We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes. Prior research has primarily focused on mitigating one kind of bias by incorporating complex fairness-driven constraints into optimization objectives or designing additional layers that focus on specific protected attributes. We introduce a simple and generic bias mitigation approach that prevents models from learning relationships between protected attributes and output variable by reducing mutual information between them. We demonstrate that our approach is effective in reducing bias with little or no drop in accuracy. We also show that the models trained with our learning framework become causally fair and insensitive to the values of protected attributes. Finally, we validate our approach by studying feature interactions between protected and non-protected attributes. We demonstrate that these interactions are significantly reduced when applying our bias mitigation

Defining clinical subtypes of adult asthma using electronic health records : analysis of a large UK primary care database with external validation

Acknowledgments EMFH was supported by a Medical Research Council PhD Studentship (eHERC/Farr). This work is carried out with the support of the Asthma UK Centre for Applied Research [AUKAC-2012-01] and Health Data Research UK which receives its funding from HDR UK Ltd (HDR-5012) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and the Wellcome Trust. The funders had no role in the study and the decision to submit this work to be considered for publication. This Project is based in part/wholly on Data from the Optimum Patient Care Research Database (opcrd.co.uk) obtained under licence from Optimum Patient Care Limited and its execution is approved by recognised experts affiliated to the Respiratory Effectiveness Group. However, the interpretation and conclusion contained in this report are those of the author/s alone. This study makes use of anonymised data held in the Secure Anonymised Information Linkage (SAIL) Databank. We would like to acknowledge all the data providers who make anonymised data available for research. SAIL is not responsible for the interpretation of these data.Peer reviewedPublisher PD