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1.  Design and implementation of the mobility assessment tool: software description 
Background
In previous work, we described the development of an 81-item video-animated tool for assessing mobility. In response to criticism levied during a pilot study of this tool, we sought to develop a new version built upon a flexible framework for designing and administering the instrument.
Results
Rather than constructing a self-contained software application with a hard-coded instrument, we designed an XML schema capable of describing a variety of psychometric instruments. The new version of our video-animated assessment tool was then defined fully within the context of a compliant XML document. Two software applications—one built in Java, the other in Objective-C for the Apple iPad—were then built that could present the instrument described in the XML document and collect participants’ responses. Separating the instrument’s definition from the software application implementing it allowed for rapid iteration and easy, reliable definition of variations.
Conclusions
Defining instruments in a software-independent XML document simplifies the process of defining instruments and variations and allows a single instrument to be deployed on as many platforms as there are software applications capable of interpreting the instrument, thereby broadening the potential target audience for the instrument. Continued work will be done to further specify and refine this type of instrument specification with a focus on spurring adoption by researchers in gerontology and geriatric medicine.
doi:10.1186/1472-6947-13-73
PMCID: PMC3729603  PMID: 23879716
2.  Applying representational state transfer (REST) architecture to archetype-based electronic health record systems 
Background
The openEHR project and the closely related ISO 13606 standard have defined structures supporting the content of Electronic Health Records (EHRs). However, there is not yet any finalized openEHR specification of a service interface to aid application developers in creating, accessing, and storing the EHR content.
The aim of this paper is to explore how the Representational State Transfer (REST) architectural style can be used as a basis for a platform-independent, HTTP-based openEHR service interface. Associated benefits and tradeoffs of such a design are also explored.
Results
The main contribution is the formalization of the openEHR storage, retrieval, and version-handling semantics and related services into an implementable HTTP-based service interface. The modular design makes it possible to prototype, test, replicate, distribute, cache, and load-balance the system using ordinary web technology. Other contributions are approaches to query and retrieval of the EHR content that takes caching, logging, and distribution into account. Triggering on EHR change events is also explored.
A final contribution is an open source openEHR implementation using the above-mentioned approaches to create LiU EEE, an educational EHR environment intended to help newcomers and developers experiment with and learn about the archetype-based EHR approach and enable rapid prototyping.
Conclusions
Using REST addressed many architectural concerns in a successful way, but an additional messaging component was needed to address some architectural aspects. Many of our approaches are likely of value to other archetype-based EHR implementations and may contribute to associated service model specifications.
doi:10.1186/1472-6947-13-57
PMCID: PMC3694512  PMID: 23656624
3.  Accessing the public MIMIC-II intensive care relational database for clinical research 
Background
The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database is a free, public resource for intensive care research. The database was officially released in 2006, and has attracted a growing number of researchers in academia and industry. We present the two major software tools that facilitate accessing the relational database: the web-based QueryBuilder and a downloadable virtual machine (VM) image.
Results
QueryBuilder and the MIMIC-II VM have been developed successfully and are freely available to MIMIC-II users. Simple example SQL queries and the resulting data are presented. Clinical studies pertaining to acute kidney injury and prediction of fluid requirements in the intensive care unit are shown as typical examples of research performed with MIMIC-II. In addition, MIMIC-II has also provided data for annual PhysioNet/Computing in Cardiology Challenges, including the 2012 Challenge “Predicting mortality of ICU Patients”.
Conclusions
QueryBuilder is a web-based tool that provides easy access to MIMIC-II. For more computationally intensive queries, one can locally install a complete copy of MIMIC-II in a VM. Both publicly available tools provide the MIMIC-II research community with convenient querying interfaces and complement the value of the MIMIC-II relational database.
doi:10.1186/1472-6947-13-9
PMCID: PMC3598967  PMID: 23302652
MIMIC-II; Relational database; Intensive care; Clinical research; SQL; Virtual machine
4.  A user-friendly tool to transform large scale administrative data into wide table format using a mapreduce program with a pig latin based script 
Background
Secondary use of large scale administrative data is increasingly popular in health services and clinical research, where a user-friendly tool for data management is in great demand. MapReduce technology such as Hadoop is a promising tool for this purpose, though its use has been limited by the lack of user-friendly functions for transforming large scale data into wide table format, where each subject is represented by one row, for use in health services and clinical research. Since the original specification of Pig provides very few functions for column field management, we have developed a novel system called GroupFilterFormat to handle the definition of field and data content based on a Pig Latin script. We have also developed, as an open-source project, several user-defined functions to transform the table format using GroupFilterFormat and to deal with processing that considers date conditions.
Results
Having prepared dummy discharge summary data for 2.3 million inpatients and medical activity log data for 950 million events, we used the Elastic Compute Cloud environment provided by Amazon Inc. to execute processing speed and scaling benchmarks. In the speed benchmark test, the response time was significantly reduced and a linear relationship was observed between the quantity of data and processing time in both a small and a very large dataset. The scaling benchmark test showed clear scalability. In our system, doubling the number of nodes resulted in a 47% decrease in processing time.
Conclusions
Our newly developed system is widely accessible as an open resource. This system is very simple and easy to use for researchers who are accustomed to using declarative command syntax for commercial statistical software and Structured Query Language. Although our system needs further sophistication to allow more flexibility in scripts and to improve efficiency in data processing, it shows promise in facilitating the application of MapReduce technology to efficient data processing with large scale administrative data in health services and clinical research.
doi:10.1186/1472-6947-12-151
PMCID: PMC3545829  PMID: 23259862
MapReduce; Pig Latin; Large scale administrative data; User-defined functions
5.  The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks 
Background
Several generic methods have been proposed to estimate transmission parameters during an outbreak, especially the reproduction number. However, as of today, no dedicated software exists that implements these methods and allow comparisons.
Results
A review of generic methods used to estimate transmissibility parameters during outbreaks was carried out. Most methods used the epidemic curve and the generation time distribution. Two categories of methods were available: those estimating the initial reproduction number, and those estimating a time dependent reproduction number. We implemented five methods as an R library, developed sensitivity analysis tools for each method and provided numerical illustrations of their use. A comparison of the performance of the different methods on simulated datasets is reported.
Conclusions
This software package allows a standardized and extensible approach to the estimation of the reproduction number and generation interval distribution from epidemic curves.
doi:10.1186/1472-6947-12-147
PMCID: PMC3582628  PMID: 23249562
Epidemics; Software; Reproduction number; Estimation; R package
6.  The Computer-based Health Evaluation Software (CHES): a software for electronic patient-reported outcome monitoring 
Background
Patient-reported Outcomes (PROs) capturing e.g., quality of life, fatigue, depression, medication side-effects or disease symptoms, have become important outcome parameters in medical research and daily clinical practice. Electronic PRO data capture (ePRO) with software packages to administer questionnaires, storing data, and presenting results has facilitated PRO assessment in hospital settings. Compared to conventional paper-pencil versions of PRO instruments, ePRO is more economical with regard to staff resources and time, and allows immediate presentation of results to the medical staff.
The objective of our project was to develop software (CHES – Computer-based Health Evaluation System) for ePRO in hospital settings and at home with a special focus on the presentation of individual patient’s results.
Methods
Following the Extreme Programming development approach architecture was not fixed up-front, but was done in close, continuous collaboration with software end users (medical staff, researchers and patients) to meet their specific demands. Developed features include sophisticated, longitudinal charts linking patients’ PRO data to clinical characteristics and to PRO scores from reference populations, a web-interface for questionnaire administration, and a tool for convenient creating and editing of questionnaires.
Results
By 2012 CHES has been implemented at various institutions in Austria, Germany, Switzerland, and the UK and about 5000 patients participated in ePRO (with around 15000 assessments in total). Data entry is done by the patients themselves via tablet PCs with a study nurse or an intern approaching patients and supervising questionnaire completion.
Discussion
During the last decade several software packages for ePRO have emerged for different purposes. Whereas commercial products are available primarily for ePRO in clinical trials, academic projects have focused on data collection and presentation in daily clinical practice and on extending cancer registries with PRO data. CHES includes several features facilitating the use of PRO data for individualized medical decision making. With its web-interface it allows ePRO also when patients are home. Thus, it provides complete monitoring of patients‘physical and psychosocial symptom burden.
doi:10.1186/1472-6947-12-126
PMCID: PMC3529695  PMID: 23140270
7.  Adaptation of a web-based, open source electronic medical record system platform to support a large study of tuberculosis epidemiology 
Background
In 2006, we were funded by the US National Institutes of Health to implement a study of tuberculosis epidemiology in Peru. The study required a secure information system to manage data from a target goal of 16,000 subjects who needed to be followed for at least one year. With previous experience in the development and deployment of web-based medical record systems for TB treatment in Peru, we chose to use the OpenMRS open source electronic medical record system platform to develop the study information system. Supported by a core technical and management team and a large and growing worldwide community, OpenMRS is now being used in more than 40 developing countries. We adapted the OpenMRS platform to better support foreign languages. We added a new module to support double data entry, linkage to an existing laboratory information system, automatic upload of GPS data from handheld devices, and better security and auditing of data changes. We added new reports for study managers, and developed data extraction tools for research staff and statisticians. Further adaptation to handle direct entry of laboratory data occurred after the study was launched.
Results
Data collection in the OpenMRS system began in September 2009. By August 2011 a total of 9,256 participants had been enrolled, 102,274 forms and 13,829 laboratory results had been entered, and there were 208 users. The system is now entirely supported by the Peruvian study staff and programmers.
Conclusions
The information system served the study objectives well despite requiring some significant adaptations mid-stream. OpenMRS has more tools and capabilities than it did in 2008, and requires less adaptations for future projects. OpenMRS can be an effective research data system in resource poor environments, especially for organizations using or considering it for clinical care as well as research.
doi:10.1186/1472-6947-12-125
PMCID: PMC3531253  PMID: 23131180
Electronic medical record; Developing country; Multidrug-resistant tuberculosis; MDR-TB
8.  Clinical software development for the Web: lessons learned from the BOADICEA project 
Background
In the past 20 years, society has witnessed the following landmark scientific advances: (i) the sequencing of the human genome, (ii) the distribution of software by the open source movement, and (iii) the invention of the World Wide Web. Together, these advances have provided a new impetus for clinical software development: developers now translate the products of human genomic research into clinical software tools; they use open-source programs to build them; and they use the Web to deliver them. Whilst this open-source component-based approach has undoubtedly made clinical software development easier, clinical software projects are still hampered by problems that traditionally accompany the software process. This study describes the development of the BOADICEA Web Application, a computer program used by clinical geneticists to assess risks to patients with a family history of breast and ovarian cancer. The key challenge of the BOADICEA Web Application project was to deliver a program that was safe, secure and easy for healthcare professionals to use. We focus on the software process, problems faced, and lessons learned. Our key objectives are: (i) to highlight key clinical software development issues; (ii) to demonstrate how software engineering tools and techniques can facilitate clinical software development for the benefit of individuals who lack software engineering expertise; and (iii) to provide a clinical software development case report that can be used as a basis for discussion at the start of future projects.
Results
We developed the BOADICEA Web Application using an evolutionary software process. Our approach to Web implementation was conservative and we used conventional software engineering tools and techniques. The principal software development activities were: requirements, design, implementation, testing, documentation and maintenance. The BOADICEA Web Application has now been widely adopted by clinical geneticists and researchers. BOADICEA Web Application version 1 was released for general use in November 2007. By May 2010, we had > 1200 registered users based in the UK, USA, Canada, South America, Europe, Africa, Middle East, SE Asia, Australia and New Zealand.
Conclusions
We found that an evolutionary software process was effective when we developed the BOADICEA Web Application. The key clinical software development issues identified during the BOADICEA Web Application project were: software reliability, Web security, clinical data protection and user feedback.
doi:10.1186/1472-6947-12-30
PMCID: PMC3507671  PMID: 22490389
Clinical software development; Open source; Web; BOADICEA risk model
9.  CDAPubMed: a browser extension to retrieve EHR-based biomedical literature 
Background
Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs.
Results
We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination.
Conclusions
CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.
doi:10.1186/1472-6947-12-29
PMCID: PMC3366875  PMID: 22480327
10.  Evaluation of the use of decision-support software in carcino-embryonic antigen (CEA)-based follow-up of patients with colorectal cancer 
Background
The present paper is a first evaluation of the use of "CEAwatch", a clinical support software system for surgeons for the follow-up of colorectal cancer (CRC) patients. This system gathers Carcino-Embryonic Antigen (CEA) values and automatically returns a recommendation based on the latest values.
Methods
Consecutive patients receiving follow-up care for CRC fulfilling our in- and exclusion criteria were identified to participate in this study. From August 2008, when the software was introduced, patients were asked to undergo the software-supported follow-up. Safety of the follow-up, experiences of working with the software, and technical issues were analyzed.
Results
245 patients were identified. The software-supported group contained 184 patients; the control group contained 61 patients. The software was safe in finding the same amount of recurrent disease with fewer outpatient visits, and revealed few technical problems. Clinicians experienced a decrease in follow-up workload of up to 50% with high adherence to the follow-up scheme.
Conclusion
CEAwatch is an efficient software tool helping clinicians working with large numbers of follow-up patients. The number of outpatient visits can safely be reduced, thus significantly decreasing workload for clinicians.
doi:10.1186/1472-6947-12-14
PMCID: PMC3330015  PMID: 22390356
follow-up; colorectal cancer; workflow automation
11.  Web-browser encryption of personal health information 
Background
Electronic health records provide access to an unprecedented amount of clinical data for research that can accelerate the development of effective medical practices. However it is important to protect patient confidentiality, as many medical conditions are stigmatized and disclosure could result in personal and/or financial loss.
Results
We describe a system for remote data entry that allows the data that would identify the patient to be encrypted in the web browser of the person entering the data. These data cannot be decrypted on the server by the staff at the data center but can be decrypted by the person entering the data or their delegate. We developed this system to solve a problem that arose in the context of clinical research, but it is applicable in a range of situations where sensitive information is stored and updated in a database and it is necessary to ensure that it cannot be viewed by any except those intentionally given access.
Conclusion
By developing this system, we are able to centralize the collection of some patient data while minimizing the risk that protected health information be made available to study personnel who are not authorized to use it.
doi:10.1186/1472-6947-11-70
PMCID: PMC3276430  PMID: 22073940
12.  Harmonisation of variables names prior to conducting statistical analyses with multiple datasets: an automated approach 
Background
Data requirements by governments, donors and the international community to measure health and development achievements have increased in the last decade. Datasets produced in surveys conducted in several countries and years are often combined to analyse time trends and geographical patterns of demographic and health related indicators. However, since not all datasets have the same structure, variables definitions and codes, they have to be harmonised prior to submitting them to the statistical analyses. Manually searching, renaming and recoding variables are extremely tedious and prone to errors tasks, overall when the number of datasets and variables are large. This article presents an automated approach to harmonise variables names across several datasets, which optimises the search of variables, minimises manual inputs and reduces the risk of error.
Results
Three consecutive algorithms are applied iteratively to search for each variable of interest for the analyses in all datasets. The first search (A) captures particular cases that could not be solved in an automated way in the search iterations; the second search (B) is run if search A produced no hits and identifies variables the labels of which contain certain key terms defined by the user. If this search produces no hits, a third one (C) is run to retrieve variables which have been identified in other surveys, as an illustration. For each variable of interest, the outputs of these engines can be (O1) a single best matching variable is found, (O2) more than one matching variable is found or (O3) not matching variables are found. Output O2 is solved by user judgement. Examples using four variables are presented showing that the searches have a 100% sensitivity and specificity after a second iteration.
Conclusion
Efficient and tested automated algorithms should be used to support the harmonisation process needed to analyse multiple datasets. This is especially relevant when the numbers of datasets or variables to be included are large.
doi:10.1186/1472-6947-11-33
PMCID: PMC3123542  PMID: 21595905
13.  PKreport: report generation for checking population pharmacokinetic model assumptions 
Background
Graphics play an important and unique role in population pharmacokinetic (PopPK) model building by exploring hidden structure among data before modeling, evaluating model fit, and validating results after modeling.
Results
The work described in this paper is about a new R package called PKreport, which is able to generate a collection of plots and statistics for testing model assumptions, visualizing data and diagnosing models. The metric system is utilized as the currency for communicating between data sets and the package to generate special-purpose plots. It provides ways to match output from diverse software such as NONMEM, Monolix, R nlme package, etc. The package is implemented with S4 class hierarchy, and offers an efficient way to access the output from NONMEM 7. The final reports take advantage of the web browser as user interface to manage and visualize plots.
Conclusions
PKreport provides 1) a flexible and efficient R class to store and retrieve NONMEM 7 output, 2) automate plots for users to visualize data and models, 3) automatically generated R scripts that are used to create the plots; 4) an archive-oriented management tool for users to store, retrieve and modify figures, 5) high-quality graphs based on the R packages, lattice and ggplot2. The general architecture, running environment and statistical methods can be readily extended with R class hierarchy. PKreport is free to download at http://cran.r-project.org/web/packages/PKreport/index.html.
doi:10.1186/1472-6947-11-31
PMCID: PMC3121579  PMID: 21575245
14.  GenDrux: A biomedical literature search system to identify gene expression-based drug sensitivity in breast cancer 
Background
This paper describes the development of a web-based tool, GenDrux, which extracts and presents (over the Internet) information related to the disease-gene-drug nexus. This information is archived from the relevant biomedical literature using automated methods. GenDrux is designed to alleviate the difficulties of manually processing the vast biomedical literature to identify disease-gene-drug relationships. GenDrux will evolve with the literature without additional algorithmic modifications.
Results
GenDrux, a pilot system, is developed in the domain of breast cancer and can be accessed at http://www.microarray.uab.edu/drug_gene.pl. GenDrux can be queried based on drug, gene and/or disease name. From over 8,000 relevant abstracts from the biomedical literature related to breast cancer, we have archived a corpus of more than 4,000 articles that depict gene expression-drug activity relationships for breast cancer and related cancers. The archiving process has been automated.
Conclusions
The successful development, implementation, and evaluation of this and similar systems when created may provide clinicians with a tool for literature management, clinical decision making, thus setting the platform for personalized therapy in the future.
doi:10.1186/1472-6947-11-28
PMCID: PMC3116456  PMID: 21545721
15.  SciReader enables reading of medical content with instantaneous definitions 
Background
A major problem patients encounter when reading about health related issues is document interpretation, which limits reading comprehension and therefore negatively impacts health care. Currently, searching for medical definitions from an external source is time consuming, distracting, and negatively impacts reading comprehension and memory of the material.
Methods
SciReader was built as a Java application with a Flex-based front-end client. The dictionary used by SciReader was built by consolidating data from several sources and generating new definitions with a standardized syntax. The application was evaluated by measuring the percentage of words defined in different documents. A survey was used to test the perceived effect of SciReader on reading time and comprehension.
Results
We present SciReader, a web-application that simplifies document interpretation by allowing users to instantaneously view medical, English, and scientific definitions as they read any document. This tool reveals the definitions of any selected word in a small frame at the top of the application. SciReader relies on a dictionary of ~750,000 unique Biomedical and English word definitions. Evaluation of the application shows that it maps ~98% of words in several different types of documents and that most users tested in a survey indicate that the application decreases reading time and increases comprehension.
Conclusions
SciReader is a web application useful for reading medical and scientific documents. The program makes jargon-laden content more accessible to patients, educators, health care professionals, and the general public.
doi:10.1186/1472-6947-11-4
PMCID: PMC3038137  PMID: 21266060
16.  Development and formative evaluation of the e-Health Implementation Toolkit (e-HIT) 
Background
The use of Information and Communication Technology (ICT) or e-Health is seen as essential for a modern, cost-effective health service. However, there are well documented problems with implementation of e-Health initiatives, despite the existence of a great deal of research into how best to implement e-Health (an example of the gap between research and practice). This paper reports on the development and formative evaluation of an e-Health Implementation Toolkit (e-HIT) which aims to summarise and synthesise new and existing research on implementation of e-Health initiatives, and present it to senior managers in a user-friendly format.
Results
The content of the e-HIT was derived by combining data from a systematic review of reviews of barriers and facilitators to implementation of e-Health initiatives with qualitative data derived from interviews of "implementers", that is people who had been charged with implementing an e-Health initiative. These data were summarised, synthesised and combined with the constructs from the Normalisation Process Model. The software for the toolkit was developed by a commercial company (RocketScience). Formative evaluation was undertaken by obtaining user feedback.
There are three components to the toolkit - a section on background and instructions for use aimed at novice users; the toolkit itself; and the report generated by completing the toolkit. It is available to download from http://www.ucl.ac.uk/pcph/research/ehealth/documents/e-HIT.xls
Conclusions
The e-HIT shows potential as a tool for enhancing future e-Health implementations. Further work is needed to make it fully web-enabled, and to determine its predictive potential for future implementations.
doi:10.1186/1472-6947-10-61
PMCID: PMC2967499  PMID: 20955594
17.  GliomaPredict: a clinically useful tool for assigning glioma patients to specific molecular subtypes 
Background
Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine.
Results
We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA)-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework.
Conclusions
GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decision-making. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas.
doi:10.1186/1472-6947-10-38
PMCID: PMC2912783  PMID: 20633285
18.  CASE: a framework for computer supported outbreak detection 
Background
In computer supported outbreak detection, a statistical method is applied to a collection of cases to detect any excess cases for a particular disease. Whether a detected aberration is a true outbreak is decided by a human expert. We present a technical framework designed and implemented at the Swedish Institute for Infectious Disease Control for computer supported outbreak detection, where a database of case reports for a large number of infectious diseases can be processed using one or more statistical methods selected by the user.
Results
Based on case information, such as diagnosis and date, different statistical algorithms for detecting outbreaks can be applied, both on the disease level and the subtype level. The parameter settings for the algorithms can be configured independently for different diagnoses using the provided graphical interface. Input generators and output parsers are also provided for all supported algorithms. If an outbreak signal is detected, an email notification is sent to the persons listed as receivers for that particular disease.
Conclusions
The framework is available as open source software, licensed under GNU General Public License Version 3. By making the code open source, we wish to encourage others to contribute to the future development of computer supported outbreak detection systems, and in particular to the development of the CASE framework.
doi:10.1186/1472-6947-10-14
PMCID: PMC2845547  PMID: 20226035
19.  FluDetWeb: an interactive web-based system for the early detection of the onset of influenza epidemics 
Background
The early identification of influenza outbreaks has became a priority in public health practice. A large variety of statistical algorithms for the automated monitoring of influenza surveillance have been proposed, but most of them require not only a lot of computational effort but also operation of sometimes not-so-friendly software.
Results
In this paper, we introduce FluDetWeb, an implementation of a prospective influenza surveillance methodology based on a client-server architecture with a thin (web-based) client application design. Users can introduce and edit their own data consisting of a series of weekly influenza incidence rates. The system returns the probability of being in an epidemic phase (via e-mail if desired). When the probability is greater than 0.5, it also returns the probability of an increase in the incidence rate during the following week. The system also provides two complementary graphs. This system has been implemented using statistical free-software (ℝ and WinBUGS), a web server environment for Java code (Tomcat) and a software module created by us (Rdp) responsible for managing internal tasks; the software package MySQL has been used to construct the database management system. The implementation is available on-line from: http://www.geeitema.org/meviepi/fludetweb/.
Conclusion
The ease of use of FluDetWeb and its on-line availability can make it a valuable tool for public health practitioners who want to obtain information about the probability that their system is in an epidemic phase. Moreover, the architecture described can also be useful for developers of systems based on computationally intensive methods.
doi:10.1186/1472-6947-9-36
PMCID: PMC2732617  PMID: 19640304
20.  BioSunMS: a plug-in-based software for the management of patients information and the analysis of peptide profiles from mass spectrometry 
Background
With wide applications of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), statistical comparison of serum peptide profiles and management of patients information play an important role in clinical studies, such as early diagnosis, personalized medicine and biomarker discovery. However, current available software tools mainly focused on data analysis rather than providing a flexible platform for both the management of patients information and mass spectrometry (MS) data analysis.
Results
Here we presented a plug-in-based software, BioSunMS, for both the management of patients information and serum peptide profiles-based statistical analysis. By integrating all functions into a user-friendly desktop application, BioSunMS provided a comprehensive solution for clinical researchers without any knowledge in programming, as well as a plug-in architecture platform with the possibility for developers to add or modify functions without need to recompile the entire application.
Conclusion
BioSunMS provides a plug-in-based solution for managing, analyzing, and sharing high volumes of MALDI-TOF or SELDI-TOF MS data. The software is freely distributed under GNU General Public License (GPL) and can be downloaded from http://sourceforge.net/projects/biosunms/.
doi:10.1186/1472-6947-9-13
PMCID: PMC2654546  PMID: 19220920
21.  The cancer translational research informatics platform 
Background
Despite the pressing need for the creation of applications that facilitate the aggregation of clinical and molecular data, most current applications are proprietary and lack the necessary compliance with standards that would allow for cross-institutional data exchange. In line with its mission of accelerating research discoveries and improving patient outcomes by linking networks of researchers, physicians, and patients focused on cancer research, caBIG (cancer Biomedical Informatics Grid™) has sponsored the creation of the caTRIP (Cancer Translational Research Informatics Platform) tool, with the purpose of aggregating clinical and molecular data in a repository that is user-friendly, easily accessible, as well as compliant with regulatory requirements of privacy and security.
Results
caTRIP has been developed as an N-tier architecture, with three primary tiers: domain services, the distributed query engine, and the graphical user interface, primarily making use of the caGrid infrastructure to ensure compatibility with other tools currently developed by caBIG. The application interface was designed so that users can construct queries using either the Simple Interface via drop-down menus or the Advanced Interface for more sophisticated searching strategies to using drag-and-drop. Furthermore, the application addresses the security concerns of authentication, authorization, and delegation, as well as an automated honest broker service for deidentifying data.
Conclusion
Currently being deployed at Duke University and a few other centers, we expect that caTRIP will make a significant contribution to further the development of translational research through the facilitation of its data exchange and storage processes.
doi:10.1186/1472-6947-8-60
PMCID: PMC2626590  PMID: 19108734
22.  Deafness mutation mining using regular expression based pattern matching 
Background
While keyword based queries of databases such as Pubmed are frequently of great utility, the ability to use regular expressions in place of a keyword can often improve the results output by such databases. Regular expressions can allow for the identification of element types that cannot be readily specified by a single keyword and can allow for different words with similar character sequences to be distinguished.
Results
A Perl based utility was developed to allow the use of regular expressions in Pubmed searches, thereby improving the accuracy of the searches.
Conclusion
This utility was then utilized to create a comprehensive listing of all DFN deafness mutations discussed in Pubmed records containing the keywords "human ear".
doi:10.1186/1472-6947-7-32
PMCID: PMC2180167  PMID: 17961241
23.  Online detection and quantification of epidemics 
Background
Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses.
Results
We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at . The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea).
Conclusion
The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.
doi:10.1186/1472-6947-7-29
PMCID: PMC2151935  PMID: 17937786
24.  Indivo: a personally controlled health record for health information exchange and communication 
Background
Personally controlled health records (PCHRs), a subset of personal health records (PHRs), enable a patient to assemble, maintain and manage a secure copy of his or her medical data. Indivo (formerly PING) is an open source, open standards PCHR with an open application programming interface (API).
Results
We describe how the PCHR platform can provide standard building blocks for networked PHR applications. Indivo allows the ready integration of diverse sources of medical data under a patient's control through the use of standards-based communication protocols and APIs for connecting PCHRs to existing and future health information systems.
Conclusion
The strict and transparent personal control model is designed to encourage widespread participation by patients, healthcare providers and institutions, thus creating the ecosystem for development of innovative, consumer-focused healthcare applications.
doi:10.1186/1472-6947-7-25
PMCID: PMC2048946  PMID: 17850667
25.  Clinical decision modeling system 
Background
Decision analysis techniques can be applied in complex situations involving uncertainty and the consideration of multiple objectives. Classical decision modeling techniques require elicitation of too many parameter estimates and their conditional (joint) probabilities, and have not therefore been applied to the problem of identifying high-performance, cost-effective combinations of clinical options for diagnosis or treatments where many of the objectives are unknown or even unspecified.
Methods
We designed a Java-based software resource, the Clinical Decision Modeling System (CDMS), to implement Naïve Decision Modeling, and provide a use case based on published performance evaluation measures of various strategies for breast and lung cancer detection. Because cost estimates for many of the newer methods are not yet available, we assume equal cost. Our use case reveals numerous potentially high-performance combinations of clinical options for the detection of breast and lung cancer.
Results
Naïve Decision Modeling is a highly practical applied strategy which guides investigators through the process of establishing evidence-based integrative translational clinical research priorities. CDMS is not designed for clinical decision support. Inputs include performance evaluation measures and costs of various clinical options. The software finds trees with expected emergent performance characteristics and average cost per patient that meet stated filtering criteria. Key to the utility of the software is sophisticated graphical elements, including a tree browser, a receiver-operator characteristic surface plot, and a histogram of expected average cost per patient. The analysis pinpoints the potentially most relevant pairs of clinical options ('critical pairs') for which empirical estimates of conditional dependence may be critical. The assumption of independence can be tested with retrospective studies prior to the initiation of clinical trials designed to estimate clinical impact. High-performance combinations of clinical options may exist for breast and lung cancer detection.
Conclusion
The software could be found useful in simplifying the objective-driven planning of complex integrative clinical studies without requiring a multi-attribute utility function, and it could lead to efficient integrative translational clinical study designs that move beyond simple pair wise competitive studies. Collaborators, who traditionally might compete to prioritize their own individual clinical options, can use the software as a common framework and guide to work together to produce increased understanding on the benefits of using alternative clinical combinations to affect strategic and cost-effective clinical workflows.
doi:10.1186/1472-6947-7-23
PMCID: PMC2131745  PMID: 17697328

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