This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in arterial blood, SaO2. In order to accomplish the task the proposed approach makes use of different artificial intelligence techniques and reasoning processes being able to deal with imprecise data. These reasoning processes are based on fuzzy logic and on temporal analysis of the information. The developed approach also takes into account the possibility of artifacts in the monitored signals. Detection and characterization of signal artifacts allows detection of false positives. Identification of relevant diagnostic patterns and temporal correlation of events is performed through the implementation of temporal constraints.
Artificial Intelligence in Medicine; Decision Support Systems; Fuzzy Logic; Intelligent Monitoring; Signal Processing; Sleep Apneas; Temporal Reasoning.
Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata.
This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS.
Cloud computing; data integration; DICOM; medical imaging; PACS and XDS-I.
Vital signs in our emergency department information system were entered into free-text fields for heart rate, respiratory rate, blood pressure, temperature and oxygen saturation.
We sought to convert these text entries into a more useful form, for research and QA purposes, upon entry into a data warehouse.
We derived a series of rules and assigned quality scores to the transformed values, conforming to physiologic parameters for vital signs across the age range and spectrum of illness seen in the emergency department.
Validating these entries revealed that 98% of free-text data had perfect quality scores, conforming to established vital sign parameters. Average vital signs varied as expected by age. Degradations in quality scores were most commonly attributed logging temperature in Fahrenheit instead of Celsius; vital signs with this error could still be transformed for use. Errors occurred more frequently during periods of high triage, though error rates did not correlate with triage volume.
In developing a method for importing free-text vital sign data from our emergency department information system, we now have a data warehouse with a broad array of quality-checked vital signs, permitting analysis and correlation with demographics and outcomes.
Data warehouse; electronic health records; emergency medicine; hospital information systems; text mining; user computer interface; vital signs.
As health providers move towards higher levels of information technology (IT) integration, they become increasingly dependent on the availability of the electronic health record (EHR). Current solutions of individually managed storage by each healthcare provider focus on efforts to ensure data security, availability and redundancy. Such models, however, scale poorly to a future of a planet-wide public health-care network (PWPHN). Our aim was to review the research literature on distributed storage systems and propose methods that may aid the implementation of a PWPHN.
A systematic review was carried out of the research dealing with distributed storage systems and EHR. A literature search was conducted on five electronic databases: Pubmed/Medline, Cinalh, EMBASE, Web of Science (ISI) and Google Scholar and then expanded to include non-authoritative sources.
The English National Health Service Spine represents the most established country-wide PHN but is limited in deployment and remains underused. Other, literature identified and established distributed EHR attempts are more limited in scope. We discuss the currently available distributed file storage solutions and propose a schema of how one of these technologies can be used to deploy a distributed storage of EHR with benefits in terms of enhanced fault tolerance and global availability within the PWPHN.
We conclude that a PWPHN distributed health care record storage system is technically feasible over current Internet infrastructure. Nonetheless, the socioeconomic viability of PWPHN implementations remains to be determined.
Electronic health record; distributed storage healthcare; public health care network; peer-to-peer networking.
Isotonic Design using Normalized Equivalent Toxicity Score (ID-NETS) is a novel Phase I design that integrates the novel toxicity scoring system originally proposed by Chen et al.  and the original Isotonic Design proposed by Leung et al. . ID-NETS has substantially improved the accuracy of maximum tolerated dose (MTD) estimation and trial efficiency in the Phase I clinical trial setting by fully utilizing all toxicities experienced by each patient and treating toxicity response as a quasi-continuous variable instead of a binary indicator of dose limiting toxicity (DLT). To facilitate the incorporation of the ID-NETS method into the design and conduct of Phase I clinical trials, we have designed and developed a user-friendly software, ID-NETS©TM, which has two functions: 1) Calculating the recommended dose for the subsequent patient cohort using available completed data; and 2) Performing simulations to obtain the operating characteristics of a trial designed with ID-NETS. Currently, ID-NETS©TMv1.0 is available for free download at http://winshipbbisr.emory.edu/IDNETS.html.
Isotonic design; normalized equivalent toxicity score; maximum tolerated dose; dose limiting toxicity; cancer phase I clinical trial; software.
In this paper, we present the design and implementation of a novel web portal for the cancer phase I clinical trial design method Escalation with Overdose Control (EWOC). The web portal has two major components: a web-based dose finding calculator; and a standalone and downloadable dose finding software which can be installed on Windows operating systems. The web-based dose finding calculator uses industry standards and is a database-driven and distributed computing platform for designing and conducting dose finding in cancer phase I clinical trials utilizing EWOC methodology. The web portal is developed using open source software: PHP, JQuery, R and OpenBUGS. It supports any standard browsers with internet connection. The web portal can be accessed at: http://biostatistics.csmc.edu.
EWOC; Bayesian method; cancer phase I clinical trial; maximum tolerated dose; open source.
The Centre for Addiction and Mental Health (CAMH) is a 500 bed freestanding psychiatric hospital in Canada. We are in the process of preparing for an integrated commercial clinical information system, which will have computerized physician order entry (CPOE) functionality.
As a preparation for CPOE, we developed inpatient order sets (OSs). Development teams from individual clinical programs created and sent their OSs to an OS Working Group for initial endorsement, and then to Pharmacy & Therapeutics and Medical Advisory committees subsequent approvals.
In twelve months we created and introduced 22 behavioral health OSs across eight clinical programs in our hybrid information system with an excellent adoption rate (>97%) by clinicians.
The development and implementation temporarily contributed to a multifactorial flow problem in the emergency department (ED), which was addressed by substantially simplifying the General Admission via the ED OS. Also, as the OSs were developed and sent for approval the project identified areas where local clinical practice can improve. Our electronic-paper hybrid set of clinical systems was a major factor impacting the effort.
Order sets; behavioral health; EMR.
Tele-ICU has an off-site command center in which a critical care team (intensivists and critical care nurses) is connected with patients in distance intensive care units (ICUs) through a real-time audio, visual and electronic means and health information is exchanged. The aim of this paper is to review literature to explore the available studies related to efficacy and cost effectiveness of Tele-ICU applications and to study the possible barriers to broader adoption. While studies draw conclusions on cost based on the mortality and Length of Stay (LOS), actual cost was not reported. Another problem in the studies was the lack of consistent measurement, reporting and adjustment for patient severity. From the data available, Tele-ICU seems to be a promising path, especially in the United States where there is a limited number of board-certified intensivists.
Cost-effectiveness; critical care; Telehealth.
In this paper, we present the design and implementation of the integrated proactive surveillance system for prostate cancer (PASS-PC). The integrated PASS-PC is a multi-institutional web-based system aimed at collecting a variety of data on prostate cancer patients in a standardized and efficient way. The integrated PASS-PC was commissioned by the Prostate Cancer Foundation (PCF) and built through the joint of efforts by a group of experts in medical oncology, genetics, pathology, nutrition, and cancer research informatics. Their main goal is facilitating the efficient and uniform collection of critical demographic, lifestyle, nutritional, dietary and clinical information to be used in developing new strategies in diagnosing, preventing and treating prostate cancer.
The integrated PASS-PC is designed based on common industry standards – a three tiered architecture and a Service- Oriented Architecture (SOA). It utilizes open source software and programming languages such as HTML, PHP, CSS, JQuery, Drupal and MySQL. We also use a commercial database management system – Oracle 11g. The integrated PASS-PC project uses a “confederation model” that encourages participation of any interested center, irrespective of its size or location. The integrated PASS-PC utilizes a standardized approach to data collection and reporting, and uses extensive validation procedures to prevent entering erroneous data. The integrated PASS-PC controlled vocabulary is harmonized with the National Cancer Institute (NCI) Thesaurus. Currently, two cancer centers in the USA are participating in the integrated PASS-PC project.
The final system has three main components: 1. National Prostate Surveillance Network (NPSN) website; 2. NPSN myConnect portal; 3. Proactive Surveillance System for Prostate Cancer (PASS-PC). PASS-PC is a cancer Biomedical Informatics Grid (caBIG) compatible product. The integrated PASS-PC provides a foundation for collaborative prostate cancer research. It has been built to meet the short term goal of gathering prostate cancer related data, but also with the prerequisites in place for future evolution into a cancer research informatics platform. In the future this will be vital for successful prostate cancer studies, care and treatment.
Cancer research informatics; service-oriented architecture; prostate cancer; proactive surveillance; multi-center clinical data database; caBIG.
The psychological influence of food (PFS) and perceived barriers to lifestyle change (PBLC) were considered as predictors of body mass index and website tool utilization (TU) in an online weight loss program.
Materials and Methodology:
An archival analysis of all (N = 1361) overweight/obese (BMI M = 31.6 + 6.24 kg/m2), adult (M = 42.0 + 10.72 years) users (82.4% female) of an evidence-based, multidisciplinary Internet weight loss program was performed. Predictor variables included: PFS and PBLC, age, and longest maintained weight loss in relation to 1) BMI 2) TU.
Both PBLC and PFS were correlated with baseline BMI and TU. Regression analyses indicated that only PFS independently predicted BMI (p = .0001) and TU (p = .001) when the model included all predictor variables. One-way ANOVA indicated gender differences on both PBLC and PFS scores (p = .001). Subsequent regression analyses separated by gender showed that in females PFS predicted BMI (p = .0001) and TU (p = .005). For males no variable significantly predicted BMI (p’s > .05) however PBLC did predict TU (p = .008).
Our findings suggest that when developing online weight loss programs clinical characteristics of the user could inform website algorithms to maximize website utilization. Gender differences indicated that for women it may be important to understand how factors related to the psychological influence of food impact utilization of online weight loss programs, however, for men broader barriers to lifestyle change is an important consideration.
Adherence; information architecture; Internet; obesity; self-help; utilization; web-based; weight loss.
This work describes a hysteroscopy surgery management application that was designed based on the medical information standard SNOMED. We describe how the application fulfils the needs of this procedure and the way in which existing handwritten medical information is effectively transmitted to the application’s database.
Conceptual database design; hysteroscopy; interoperability; medical information management; SNOMED.
Communication of critical results from diagnostic procedures between caregivers is a Joint Commission national patient safety goal. Evaluating critical result communication often requires manual analysis of voluminous data, especially when reviewing unstructured textual results of radiologic findings. Information retrieval (IR) tools can facilitate this process by enabling automated retrieval of radiology reports that cite critical imaging findings. However, IR tools that have been developed for one disease or imaging modality often need substantial reconfiguration before they can be utilized for another disease entity.
This paper: 1) describes the process of customizing two Natural Language Processing (NLP) and Information Retrieval/Extraction applications – an open-source toolkit, A Nearly New Information Extraction system (ANNIE); and an application developed in-house, Information for Searching Content with an Ontology-Utilizing Toolkit (iSCOUT) – to illustrate the varying levels of customization required for different disease entities and; 2) evaluates each application’s performance in identifying and retrieving radiology reports citing critical imaging findings for three distinct diseases, pulmonary nodule, pneumothorax, and pulmonary embolus.
Both applications can be utilized for retrieval. iSCOUT and ANNIE had precision values between 0.90-0.98 and recall values between 0.79 and 0.94. ANNIE had consistently higher precision but required more customization.
Understanding the customizations involved in utilizing NLP applications for various diseases will enable users to select the most suitable tool for specific tasks.
Critical imaging findings; critical test results; document retrieval; radiology report retrieval.
Diagnostic test interpretation remains a challenge in clinical practice. Most physicians receive training in the
use of Bayes’ rule, which specifies how the sensitivity and specificity of a test for a given disease combine with the pre-test
probability to quantify the change in disease probability incurred by a new test result. However, multiple studies
demonstrate physicians’ deficiencies in probabilistic reasoning, especially with unexpected test results. Information
theory, a branch of probability theory dealing explicitly with the quantification of uncertainty, has been proposed as an
alternative framework for diagnostic test interpretation, but is even less familiar to physicians. We have previously
addressed one key challenge in the practical application of Bayes theorem: the handling of uncertainty in the critical first
step of estimating the pre-test probability of disease. This essay aims to present the essential concepts of information
theory to physicians in an accessible manner, and to extend previous work regarding uncertainty in pre-test probability
estimation by placing this type of uncertainty within a principled information theoretic framework. We address several
obstacles hindering physicians’ application of information theoretic concepts to diagnostic test interpretation. These
include issues of terminology (mathematical meanings of certain information theoretic terms differ from clinical or
common parlance) as well as the underlying mathematical assumptions. Finally, we illustrate how, in information
theoretic terms, one can understand the effect on diagnostic uncertainty of considering ranges instead of simple point
estimates of pre-test probability.
Bayes’ rule; diagnosis; information; probability; uncertainty.
The study objective was to evaluate the usefulness of Classification and Regression Trees (CART), to classify clinical responders to antidepressant and placebo treatment, utilizing symptom severity and quantitative EEG (QEEG) data. Patients included 51 adults with unipolar depression who completed treatment trials using either fluoxetine, venlafaxine or placebo. Hamilton Depression Rating Scale (HAM-D) and single electrodes data were recorded at baseline, 2, 7, 14, 28 and 56 days. Patients were classified as medication and placebo responders or non-responders. CART analysis of HAM-D scores showed that patients with HAM-D scores lower than 13 by day 7 were more likely to be treatment responders to fluoxetine or venlafaxine compared to non-responders (p=0.001). Youden’s index γ revealed that CART models using QEEG measures were more accurate than HAM-D-based models. For patients given fluoxetine, patients with a decrease at day 2 in θ cordance at AF2 were classified by CART as treatment responders (p=0.02). For those receiving venlafaxine, CART identified a decrease in δ absolute power at day 7 at the PO2 region as characterizing treatment responders (p=0.01). Using all patients receiving medication, CART identified a decrease in δ absolute power at day 2 in the FP1 region as characteristic of nonresponse to medication (p=0.003). Optimal trees from the QEEG CART analysis primarily utilized cordance values, but also incorporated some δ absolute power values. The results of our study suggest that CART may be a useful method for identifying potential outcome predictors in the treatment of major depression.
Quantitative EEG; antidepressant; placebo treatment; CART.
Glycomics is a discipline of biology that deals with the structure and function of glycans (or carbohydrates). Analytical techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are having a significant impact on the field of glycomics. However, effective progress in glycomics research requires collaboration between laboratories to share experimental data, structural information of glycans, and simulation results. Herein we report the development of a web-based data management system that can incorporate large volumes of data from disparate sources and organize them into a uniform format for users to store and access. This system enables participating laboratories to set up a shared data repository which members of interdisciplinary teams can access. The system is able to manage and share raw MS data and structural information of glycans.
The database is available at http://www.glycomics.bcf.ku.edu
Functional glycomics; web-based data management system; mass spectrometry data; glycan structure.
Contrasted with other information carriers, such as speech and text, images contains larger amount of information, especially in sequential images, that is waiting to be exploited, in particular the dynamic information of correlation, difference, and temporal relationship between different frames. This dynamic information contributes a great deal in analysis of 4D images. This paper proposes a method for detecting dynamic information from sequential images, based on the rebuilding of their gray (position)~time function on direction lines, an approach that has been analyzed and studied extensively on the setting of various direction lines. This method is based on motion that is presented on sequential images. In particular, the method, Omni directional M-mode Echocardiography system, which we have studied extensively, will be described leading to a robust way of diagnosing heart diseases.
Rebuilding gray (position) ~ time function; tracking moving object on any directional line; echocardiography; omnidirectional m-mode echocardiography system ; motion information.
Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice.
We define applicability to clinical practice by having recently demonstrated the CBIR system on one of the CAD demonstration workshops held at international conferences, such as SPIE Medical Imaging, CARS, SIIM, RSNA, and IEEE ISBI. From 2009 to 2011, the programs of CADdemo@CARS and the CAD Demonstration Workshop at SPIE Medical Imaging were sought for the key word “retrieval” in the title. The systems identified were analyzed and compared according to the hierarchy of gaps for CBIR systems.
In total, 70 software demonstrations were analyzed. 5 systems were identified meeting the criterions. The fields of application are (i) bone age assessment, (ii) bone fractures, (iii) interstitial lung diseases, and (iv) mammography. Bridging the particular gaps of semantics, feature extraction, feature structure, and evaluation have been addressed most frequently.
In specific application domains, CBIR technology is available for clinical practice. While system development has mainly focused on bridging content and feature gaps, performance and usability have become increasingly important. The evaluation must be based on a larger set of reference data, and workflow integration must be achieved before CBIR-CAD is really established in clinical practice.
Content-based image retrieval; medical image retrieval; diagnosis aid; prototypes.
Recently, robotic systems have been introduced as a useful method for surgical procedures. But in the field of vascular interventional therapy, the development of robotic system is slower.
The purpose of the study is to verify the reliability and safety of vascular interventional robotic system used in angiography, by the way of in vitro preliminary experiments and animal experiments.
The approach is to employ a proprietary vascular interventional robot system to complete glass vessel models and animal angiogram experiments. This robot system consists of a console port (remote steering system), an assistant port (propelled and rotation system) and a hydraulic fixing device, upon which surgeons control remotely to make go forward and rotate in the glass vessel models and animal vessels, on the 3D operation interface. Consequently, the operation time and success rate are counted and evaluated.
In the glass vessel model experiments, the Catheter can enter various kinds of vessel models with inside diameter length greater than 3mm and angle less than 90o. In the animal (adult dogs) experiments, surgeons can accomplish smoothly the angiogram of the renal artery, the vertebral renal and the arteria carotis communis, without any complications of surgery.
The angiogram by using vascular interventional robot system is safe and reliable. Surgeons can finish the angiogram in part by remote operation, and the result of angiogram can meet a number of simple expectations. However without wire control and force feedback systems, the applicability of this kind of robot system is not flexible enough and need to be improved in the future.
Robotic; vascular interventional; remote steering; animal experiments.
The current imaging methods have a limited ability to visualize microstructures of biological soft tissues. Small lesions cannot be detected at the early stage of the disease. Phase contrast imaging (PCI) is a novel non-invasive imaging technique that can provide high contrast images of soft tissues by the use of X-ray phase shift. It is a new choice in terms of non-invasively revealing soft tissue details. In this study, the lung and hepatic fibrosis models of mice and rats were used to investigate the ability of PCI in microstructures observation of soft tissues. Our results demonstrated that different liver fibrosis stages could be distinguished non-invasively by PCI. The three-dimensional morphology of a segment of blood vessel was constructed. Noteworthy, the blood clot inside the vessel was visualized in three dimensions which provided a precise description of vessel stenosis. Furthermore, the whole lung airways including the alveoli were obtained. We had specifically highlighted its use in the visualization and assessment of the alveoli. To our knowledge, this was the first time for non-invasive alveoli imaging using PCI. This finding may offer a new perspective on the diagnosis of respiratory disease. All the results confirmed that PCI will be a valuable tool in biological soft tissues imaging.
Phase contrast imaging (PCI); diffraction enhanced imaging (DEI); in-line X-ray phase contrast imaging (IL-XPCI); hepatic fibrosis; microvessel; alveoli.
Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.
Breast ultrasonic images; fully automatic; region of interest; Normalized Cut; Affinity Propagation clustering.
Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable when new tools for data mining and new media for data storing become available. This paper provides a summative account on the creation of medical imaging tomography, the development of image archiving systems and the innovation from the existing acquired image data pools. The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical images with variety of domains and different dimensions. In terms of novelties, the facilities of CBIR for 3D images coupled with image annotation in a fully automatic fashion have been developed and implemented in the system, resonating with future versatile, flexible and sustainable medical image databases that can reap new innovations.
3D image retrieval; CBIR; medical imaging techniques; texture-based retrieval; PACS; e-learning.
In this article we have assembled the experience obtained from our participation in the imageCLEF evaluation task over the past two years. Exploitation on the use of linear combinations for image retrieval has been attempted by combining visual and textual sources of images. From our experiments we conclude that a mixed retrieval technique that applies both textual and visual retrieval in an interchangeably repeated manner improves the performance while overcoming the scalability limitations of visual retrieval. In particular, the mean average precision (MAP) has increased from 0.01 to 0.15 and 0.087 for 2009 and 2010 data, respectively, when content-based image retrieval (CBIR) is performed on the top 1000 results from textual retrieval based on natural language processing (NLP).
Information storage and retrieval; data fusion; content based image retrieval; digital libraries.
An increasing number of people search for health information online. During the last 10 years various researchers have determined the requirements for an ideal consumer health information system. The aim of this study was to figure out, whether medical laymen can find a more accurate diagnosis for a given anamnesis via the developed prototype health information system than via ordinary internet search.
In a randomized controlled trial, the prototype information system was evaluated by the assessment of two sample cases. Participants had to determine the diagnosis of a patient with a headache via information found searching the web. A patient’s history sheet and a computer with internet access were provided to the participants and they were guided through the study by an especially designed study website. The intervention group used the prototype information system; the control group used common search engines and portals. The numbers of correct diagnoses in each group were compared.
A total of 140 (60/80) participants took part in two study sections. In the first case, which determined a common diagnosis, both groups did equally well. In the second section, which determined a less common and more complex case, the intervention group did significantly better (P=0.031) due to the tailored information supply.
Using medical expert systems in combination with a portal searching meta-search engine represents a feasible strategy to provide reliable patient-tailored information and can ultimately contribute to patient safety with respect to information found via the internet.
Information supply; internet; expert system; meta-search; tailoring; headaches.