251,181 research outputs found
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum
The introduction of electronic personal health records (EHR) enables
nationwide information exchange and curation among different health care
systems. However, the current EHR systems do not provide transparent means for
diagnosis support, medical research or can utilize the omnipresent data
produced by the personal medical devices. Besides, the EHR systems are
centrally orchestrated, which could potentially lead to a single point of
failure. Therefore, in this article, we explore novel approaches for
decentralizing machine learning over distributed ledgers to create intelligent
EHR systems that can utilize information from personal medical devices for
improved knowledge extraction. Consequently, we proposed and evaluated a
conceptual EHR to enable anonymous predictive analysis across multiple medical
institutions. The evaluation results indicate that the decentralized EHR can be
deployed over the computing continuum with reduced machine learning time of up
to 60% and consensus latency of below 8 seconds
A Secure Grid Medical Data Manager Interfaced to the gLite Middleware
International audienceThe medical community is producing and manipulating a tremendous volume of digital data for which computerized archiving, processing and analysis is needed. Grid infrastructures are promising for dealing with challenges arising in computerized medicine but the manipulation of medical data on such infrastructures faces both the problem of interconnecting medical information systems to Grid middlewares and of preserving patients' privacy in a wide and distributed multi-user system. These constraints are often limiting the use of Grids for manipulating sensitive medical data. This paper describes our design of a medical data management system taking advantage of the advanced gLite data management services, developed in the context of the EGEE project, to fulfill the stringent needs of the medical community. It ensures medical data protection through strict data access control, anonymization and encryption. The multi-level access control provides the flexibility needed for imple! menting complex medical use-cases. Data anonymization prevents the exposure of most sensitive data to unauthorized users, and data encryption guarantees data protection even when it is stored at remote sites. Moreover, the developed prototype provides a Grid storage resource manager (SRM) interface to standard medical DICOM servers thereby enabling transparent access to medical data without interfering with medical practice
Service-oriented subscription management of medical decision data in the intensive care unit
Objectives: This paper addresses the design of a platform for the management of medical decision data in the ICU. Whenever new medical data from laboratories of monitors is available or at fixed times, the appropriate medical support services are activated and generate a medical alert or suggestion to the bedside terminal, the physician's PDA, smart phone or mailbox. Since future ICU systems will rely ever more on medical decision support, a generic and flexible subscription platform is of high importance.
Methods: Our platform is designed based on the principles of service-oriented architectures, and is fundamental for service deployment since the medical support services only need to implement their algorithm and can rely on the platform for general functionalities. A secure communication and execution environment are also provided.
Results: A prototype, where medical support services can be easily plugged in, has been implemented using Web service technology and is currently being evaluated by the Department of Intensive Cafe of the Ghent University Hospital. To illustrate the platform operation and performance, two prototype medical support services are used, showing that the extra response time introduced by the platform is less than 150 ms.
Conclusions: The platform allows for easy integration with hospital information systems. The platform is generic and offers user-friendly patient/service subscription, transparent data and service resource management and priority-based filtering of messages. The performance has been evaluated and it was shown that the response time of platform components is negligible compared to the execution time of the medical support services
Konzeption und Realisierung einer Web-Anwendung zur Unterstützung von Ärzten bei der Notfallbehandlung von Patienten
In the past the manpower in German hospitals has risen and therefore the costs. Furthermore, many hospitals use incompatible information management systems making the exchange of data difficult. We look at different approaches to relieve the current healthcare system with the implementation of e-Health. Our focus is the Estonian healthcare system, as one of the most advanced in the European Union. Moreover, we look at systems to help first responders and paramedics as well as systems helping hospital staff with an app. To make our application as accessible as possible we discuss the benefits and disadvantages of QR Codes and Data Matrix codes. Based on the insights gained we propose an application combining the approaches. This application functions as a central database for all medical workers. Our goal was to make the application as transparent as possible and therefore the patient can see all the documents about them. We describe how the application could be used by medical staff and doctors too. Our application provides easy and quick access for medical personnel to the files of patients, reducing time spend for administrative tasks
Accessing and managing open medical resources in Africa over the Internet
Recent commentaries have proposed the advantages of using open exchange of data and informatics resources for improving health-related policies and patient care in Africa. Yet, in many African regions, both private medical and public health information systems are still unaffordable. Open exchange over the social Web 2.0 could encourage more altruistic support of medical initiatives. We have carried out some experiments to demonstrate the feasibility of using this approach to disseminate open data and informatics resources in Africa. After the experiments we developed the AFRICA BUILD Portal, the first Social Network for African biomedical researchers. Through the AFRICA BUILD Portal users can access in a transparent way to several resources. Currently, over 600 researchers are using distributed and open resources through this platform committed to low connections
Data Mining Technique to Interpret Lung Nodule for Computer Aided Diagnosis
Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagnosis (CAD) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Though increasing the accuracy, these CAD systems rarely offer useful descriptions of the suspected nodule or their decision criteria, mainly due to lack of nodule data. In this paper, we present a framework for mapping image features to radiologist-defined diagnostic criteria based on the newly available data). Using data mining, we found promising mappings to clinically relevant, human-interpretable nodule characteristics such as malignancy, margin, spiculation, subtlety, and texture. Bridging the semantic gap between computed image features and radiologist defined diagnostic criteria allows CAD systems to offer not only a second opinion but also decision-support criteria usable by radiologists. Presenting transparent decisions will improve the clinical acceptance of CAD
Deficiencies in the transfer and availability of clinical trials evidence: A review of existing systems and standards
Background: Decisions concerning drug safety and efficacy are generally based on pivotal evidence provided by clinical trials. Unfortunately, finding the relevant clinical trials is difficult and their results are only available in text-based reports. Systematic reviews aim to provide a comprehensive overview of the evidence in a specific area, but may not provide the data required for decision making. Methods: We review and analyze the existing information systems and standards for aggregate level clinical trials information from the perspective of systematic review and evidence-based decision making. Results: The technology currently used has major shortcomings, which cause deficiencies in the transfer, traceability and availability of clinical trials information. Specifically, data available to decision makers is insufficiently structured, and consequently the decisions cannot be properly traced back to the underlying evidence. Regulatory submission, trial publication, trial registration, and systematic review produce unstructured datasets that are insufficient for supporting evidence-based decision making. Conclusions: The current situation is a hindrance to policy decision makers as it prevents fully transparent decision making and the development of more advanced decision support systems. Addressing the identified deficiencies would enable more efficient, informed, and transparent evidence-based medical decision making
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions
Machine learning is expected to fuel significant improvements in medical
care. To ensure that fundamental principles such as beneficence, respect for
human autonomy, prevention of harm, justice, privacy, and transparency are
respected, medical machine learning systems must be developed responsibly. Many
high-level declarations of ethical principles have been put forth for this
purpose, but there is a severe lack of technical guidelines explicating the
practical consequences for medical machine learning. Similarly, there is
currently considerable uncertainty regarding the exact regulatory requirements
placed upon medical machine learning systems. This survey provides an overview
of the technical and procedural challenges involved in creating medical machine
learning systems responsibly and in conformity with existing regulations, as
well as possible solutions to address these challenges. First, a brief review
of existing regulations affecting medical machine learning is provided, showing
that properties such as safety, robustness, reliability, privacy, security,
transparency, explainability, and nondiscrimination are all demanded already by
existing law and regulations - albeit, in many cases, to an uncertain degree.
Next, the key technical obstacles to achieving these desirable properties are
discussed, as well as important techniques to overcome these obstacles in the
medical context. We notice that distribution shift, spurious correlations,
model underspecification, uncertainty quantification, and data scarcity
represent severe challenges in the medical context. Promising solution
approaches include the use of large and representative datasets and federated
learning as a means to that end, the careful exploitation of domain knowledge,
the use of inherently transparent models, comprehensive out-of-distribution
model testing and verification, as well as algorithmic impact assessments
Indivo: a personally controlled health record for health information exchange and communication
<p>Abstract</p> <p>Background</p> <p>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).</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p
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