Log Analysis to Understand Medical Professionals&#x27; Image Searching Behaviour

Technology Health & Medicine

This document summarizes a study analyzing log files from a medical image search system to understand how medical professionals search for images. The analysis found that searches most commonly involved conditions, body parts, and imaging modalities. Queries often involved modifying search terms between queries. The most frequent search terms identified topics that could be useful for an image retrieval benchmark. In conclusion, log file analysis provides insights into user search behavior that can help improve image search systems.

Log Analysis to Understand Medical
Professionals' Image Searching
Behaviour
Theodora Tsikrika
Henning Müller
Charles E. Kahn

Overview
• Medical image retrieval
• Motivation of our work
• Methods
• Log file analysis
• Search strategies
• Frequent information needs
• Use as topics for a retrieval benchmark
• Conclusions

2

Medical image retrieval
• Medical professionals frequently and
increasingly search for visual information
(images, videos)
• Particularly radiologists often search for
images
• Internet search increasingly replaces search in
reference books and discussions with colleagues
• Images are important for differential diagnosis,
finding explications for unclear visual patterns
• Different types of image search systems
• Text-based search for images
3

Motivation
• Knowing search tasks, goals and formulations
of user groups for information retrieval is
important
• To build new IR systems or benchmark existing ones
• Several surveys have been performed
• Log file analyses were done as well
• MedLine log files, not really for images
• HONmedia search, less focused as not radiologists, but
rather general public, health professionals
• Image search on the Internet for radiologists
has increased strongly
4

Log file analysis
• Session level, query level, term level
• Search logs have received much attention to
learn more on user behavior
• Bad example: release of AOL log, privacy!!
• Amount of information differs, IP addresses, time
stamps
• Session level is interesting as much is
learned on behavior, query modifications,
even satisfaction
• Terms added, removed, changed?
• Query and term level often focus on 5

Methods
• ARRS Goldminer made a log file available
• 25’000 consecutive searches of medical
professionals
• Search system is very popular with radiologists
• Allows search terms, selection of gender, age and
modality

• Search term normalization
• All lower case, removing special characters, quotes
• Manual work: “xray”, “x-ray”, “x ray” all equals “xray”
• Removal of identical consecutive queries
• No time stamps available, no IP address 6

Results of the analysis
• 23’033 queries after preprocessing, 14’413 of
these are unique queries (63%)
• Query length 2.24 words, 2.46 for unique
queries
• Similar to web search, one term less than MedLine
• Imaging modalities:
• MRI (586), CT (425), ultrasound (199), xray (139),
PET (34), PET/CT (13), angiography (13), echo (11),
radiography (10), tomography (6), fMRI (3), PET/MRI
(1)
• This despite the possibility to filter for modalities
7

Query modification
• 5713 consecutive query pairs sharing at
least one term, assumed to be single
session

9

Use of terms for topics in
ImageCLEF
• ImageCLEF, image retrieval benchmark
• Using images and text as queries, 17 groups
participated in 2012
• Taking most frequent searches, at least two
terms
• Radiologist ranked these search terms by
usefulness in radiology
• Most useful terms were checked to find
whether documents in PubMedCentral fulfill
the need
• 30 most useful, most frequent, available
10

Conclusions
• Analysis of log files can help understand
user behavior
• Help build better systems based on user models
and analyze current approaches, also
shortcomings
• Time stamps and user identification are
important for query session analysis
• We used implicit knowledge for this
• People do not know all details of systems
• Search for modalities in text and through filters
• Depending on results, users change terms
11

Questions?
• More information can be found at
• http://www.khresmoi.eu/
• http://medgift.hevs.ch/
• http://publications.hevs.ch/

• Contact:
• Henning.mueller@hevs.ch
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A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence ...

We propose a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter-variations of the feature space dimensions. The obtained Riesz-covariance descriptors lie on a manifold governed by Riemannian geometry requiring specific geodesic metrics to locally approximate scalar products. The latter are used to construct a kernel for support vector machines (SVM). The effectiveness of the presented models is evaluated on a dataset of 92 patients with non-small cell lung carcinoma (NSCLC) and cancer recurrence information. Disease recurrence within a timeframe of 12 months could be predicted with an accuracy above 80, and highlighted the importance of covariance-based texture aggregation. At the end of the talk, computer tools will be presented to easily extract 3D radiomics quantitative features from PET-CT images.

Challenges in medical imaging and the VISCERAL model

This document discusses challenges in medical imaging and the VISCERAL model. It provides an overview of systematic evaluations of medical image retrieval since the 1960s. It describes the ImageCLEF benchmark which has run medical image retrieval tasks since 2003. It discusses open science initiatives to share data and tools. It introduces the VISCERAL model which brings algorithms to medical image data stored in the cloud to enable large-scale challenges. The document concludes that open science has potential advantages but the medical domain poses complications regarding data protection, and that challenges will be part of the ecosystem for sharing medical image analysis tools.

NOSE: une approche Smart-City pour les zones périphériques et extra-urbaines

How to detect soft falls on devices

At the Knime Berlin summit 2016, Prof. Dr. Dominique Genoud presented a novel way to implement a KNIME workflow that perform machine learning and signal processing on an Android platform. The use case was to detect soft falls (not from a standing position) using an Android watch. This application has a big impact on how we can detect automatically when elderly people fall from their bed of their chair. This work was originally based on the Master Thesis in Business Administration realized by Vincent Cuendet in 2015 at the HES-SO with the help of the FST (Fédération Suisse pour les Téléthèses), an organization that helps disabled and elderly people to keep their autonomy.

FUNDAMENTALS OF TEXTURE PROCESSING FOR BIOMEDICAL IMAGE ANALYSIS

Presented by Adrien Depeursinge, PhD, at MICCAI 2015 Tutorial on Biomedical Texture Analysis (BTA), Munich, Oct 5 2015. Texture-based imaging biomarkers complement focal, invasive biopsy based biomarkers by providing information on tissue structure over broad regions, non-invasively, and repeatedly across multiple time points. Texture has been used to predict patient survival, tissue function, disease subtypes and genomics (imagenomics and radiogenomics). Nevertheless, several challenges remain, such as: the lack of an appropriate framework for multi-scale, multi-spectral analysis in 2D and 3D; localization uncertainty of texture operators; validation; and, translation to routine clinical applications.

MOBILE COLLECTION AND DISSEMINATION OF SENIORS’ SKILLS

Mocodis is a web application facilitating the transfer of skills between senior and junior associates. It can be used in companies, institutions to capitalize on the experience of older employees, or can be used to train employees top down. Mocodis automatically generates dynamic micro-courses combining text, audio and video resources, and uses an algorithm to analyze user satisfaction to produce better courses at the next request.

Enhanced Students Laboratory The GET project

The GET project aims to analyze learning characteristics of new generations of students in order to develop models based on surveys and prototype applications. This will help evolve teaching methods. The project created Google Glass Enhanced TextBooks to improve course materials by enriching paper resources with video accessed through Google Glass. A trial with students provided mostly positive feedback, liking the multimedia resources and links between text and media, though some found the glasses difficult to use and navigation between resources perturbing. Future work will evaluate the impact of different types of video on learning.

Solar production prediction based on non linear meteo source adaptation

This work presents a data-intensive solution to predict Photovoltaïque energy (PV) production. PV and other renewable sources have widely spread in recent years. Although those sources provide an environmentally-friendly solution, their integration is a real challenge in terms of power management as it depends on meteorological conditions. The ability to predict those variable sources considering meteorological uncertainty plays a key role in the management of the energy supply needs and reserves. This paper presents an easy-to-use methodology to predict PV production using time series analyses and sampling algorithms. The aim is to provide a forecasting model to set the day-ahead grid electricity need. This information is useful for power dispatching plans and grid charge control. The main novelties of our approach is to provide an easy implemented and flexible solution that combines classification algorithms to predict the PV plant efficiency considering weather conditions and nonlinear regression to predict weather forecasted errors in order to improve prediction results. The results are based on the data collected in the Techno-pôle’s microgrid in Sierre (Switzerland) described further in the paper. The best experimental results have been obtained using hourly historical weather measures (radiation and temperature) and PV production as training inputs and weather forecasted parameters as prediction inputs. Considering a 10 month dataset and despite the presence of 17 missing days, we achieve a Percentage Mean Absolute Deviation (PMAD) of 20% in August and 21% in September. Better results can be obtained with a larger dataset but as more historical data were not available, other months have not been tested.

Exploring the New Trends of Chinese Tourists in Switzerland

Switzerland is one of the most desirable European destinations for Chinese tourists, a better understanding of Chinese tourists is essential for successful business practices. In China, the largest and leading social media platform – Sina Weibo, has more than 600 million users. Weibo’s great market penetration suggests that tourism operators and markets need to understand how to build effective and sustainable communications on Chinese social media platforms. The goal of this research is to understand Chinese tourists’ behaviors and patterns in Switzerland by adopting a linked data approach on Sina Weibo, and to design a decision support system based on the findings.

Social Media Data analyzis and Semantics for Tourism Understanding