A dynamic time-to-event model for prediction of acute graft-versus-host disease in patients after allogeneic hematopoietic stem cell transplantation.

BACKGROUND: Acute graft-versus-host disease (aGvHD) is a major cause of death for patients following allogeneic hematopoietic stem cell transplantation (HSCT). Effective management of moderate to severe aGvHD remains challenging despite recent advances in HSCT, emphasizing the importance of prophylaxis and risk factor identification. METHODS: In this study, we analyzed data from 1479 adults who underwent HSCT between 2005 and 2017 to investigate the effects of aGvHD prophylaxis and time-dependent risk factors on the development of grades II-IV aGvHD within 100 days post-HSCT. RESULTS: Using a dynamic longitudinal time-to-event model, we observed a non-monotonic baseline hazard overtime with a low hazard during the first few days and a maximum hazard at day 17, described by Bateman function with a mean transit time of approximately 11 days. Multivariable analysis revealed significant time-dependent effects of white blood cell counts and cyclosporine A exposure as well as static effects of female donors for male recipients, patients with matched related donors, conditioning regimen consisting of fludarabine plus total body irradiation, and patient age in recipients of grafts from related donors on the risk to develop grades II-IV aGvHD. Additionally, we found that higher cumulative hazard on day 7 after allo-HSCT are associated with an increased incidence of grades II-IV aGvHD within 100 days indicating that an individual assessment of the cumulative hazard on day 7 could potentially serve as valuable predictor for later grades II-IV aGvHD development. Using the final model, stochastic simulations were performed to explore covariate effects on the cumulative incidence over time and to estimate risk ratios. CONCLUSION: Overall, the presented model showed good descriptive and predictive performance and provides valuable insights into the interplay of multiple static and time-dependent risk factors for the prediction of aGvHD.

Time-dependent prediction of mortality and cytomegalovirus reactivation after allogeneic hematopoietic cell transplantation using machine learning.

Allogeneic hematopoietic cell transplantation (HCT) effectively treats high-risk hematologic diseases but can entail HCT-specific complications, which may be minimized by appropriate patient management, supported by accurate, individual risk estimation. However, almost all HCT risk scores are limited to a single risk assessment before HCT without incorporation of additional data. We developed machine learning models that integrate both baseline patient data and time-dependent laboratory measurements to individually predict mortality and cytomegalovirus (CMV) reactivation after HCT at multiple time points per patient. These gradient boosting machine models provide well-calibrated, time-dependent risk predictions and achieved areas under the receiver-operating characteristic of 0.92 and 0.83 and areas under the precision-recall curve of 0.58 and 0.62 for prediction of mortality and CMV reactivation, respectively, in a 21-day time window. Both models were successfully validated in a prospective, non-interventional study and performed on par with expert hematologists in a pilot comparison.

Deep Neural Network Driven Speech Classification for Relevance Detection in Automatic Medical Documentation.

The automation of medical documentation is a highly desirable process, especially as it could avert significant temporal and monetary expenses in healthcare. With the help of complex modelling and high computational capability, Automatic Speech Recognition (ASR) and deep learning have made several promising attempts to this end. However, a factor that significantly determines the efficiency of these systems is the volume of speech that is processed in each medical examination. In the course of this study, we found that over half of the speech, recorded during follow-up examinations of patients treated with Intra-Vitreal Injections, was not relevant for medical documentation. In this paper, we evaluate the application of Convolutional and Long Short-Term Memory (LSTM) neural networks for the development of a speech classification module aimed at identifying speech relevant for medical report generation. In this regard, various topology parameters are tested and the effect of the model performance on different speaker attributes is analyzed. The results indicate that Convolutional Neural Networks (CNNs) are more successful than LSTM networks, and achieve a validation accuracy of 92.41%. Furthermore, on evaluation of the robustness of the model to gender, accent and unknown speakers, the neural network generalized satisfactorily.

Patient empowerment for cancer patients through a novel ICT infrastructure.

As a result of recent advances in cancer research and "precision medicine" approaches, i.e. the idea of treating each patient with the right drug at the right time, more and more cancer patients are being cured, or might have to cope with a life with cancer. For many people, cancer survival today means living with a complex and chronic condition. Surviving and living with or beyond cancer requires the long-term management of the disease, leading to a significant need for active rehabilitation of the patients. In this paper, we present a novel methodology employed in the iManageCancer project for cancer patient empowerment in which personal health systems, serious games, psychoemotional monitoring and other novel decision-support tools are combined into an integrated patient empowerment platform. We present in detail the ICT infrastructure developed and our evaluation with the involvement of cancer patients on two sites, a large-scale pilot for adults and a small-scale test for children. The evaluation showed mixed evidences on the improvement of patient empowerment, while ability to cope with cancer, including improvement in mood and resilience to cancer, increased for the participants of the adults' pilot.

iManageMyHealth and iSupportMyPatients: mobile decision support and health management apps for cancer patients and their doctors.

Clinical decision support systems can play a crucial role in healthcare delivery as they promise to improve health outcomes and patient safety, reduce medical errors and costs and contribute to patient satisfaction. Used in an optimal way, they increase the quality of healthcare by proposing the right information and intervention to the right person at the right time in the healthcare delivery process. This paper reports on a specific approach to integrated clinical decision support and patient guidance in the cancer domain as proposed by the H2020 iManageCancer project. This project aims at facilitating efficient self-management and management of cancer according to the latest available clinical knowledge and the local healthcare delivery model, supporting patients and their healthcare providers in making informed decisions on treatment choices and in managing the side effects of their therapy. The iManageCancer platform is a comprehensive platform of interconnected mobile tools to empower cancer patients and to support them in the management of their disease in collaboration with their doctors. The backbone of the iManageCancer platform comprises a personal health record and the central decision support unit (CDSU). The latter offers dedicated services to the end users in combination with the apps iManageMyHealth and iSupportMyPatients. The CDSU itself is composed of the so-called Care Flow Engine (CFE) and the model repository framework (MRF). The CFE executes personalised and workflow oriented formal disease management diagrams (Care Flows). In decision points of such a Care Flow, rules that operate on actual health information of the patient decide on the treatment path that the system follows. Alternatively, the system can also invoke a predictive model of the MRF to proceed with the best treatment path in the diagram. Care Flow diagrams are designed by clinical experts with a specific graphical tool that also deploys these diagrams as executable workflows in the CFE following the Business Process Model and Notation (BPMN) standard. They are exposed as services that patients or their doctors can use in their apps in order to manage certain aspects of the cancer disease like pain, fatigue or the monitoring of chemotherapies at home. The mHealth platform for cancer patients is currently being assessed in clinical pilots in Italy and Germany and in several end-user workshops.

XplOit: An Ontology-Based Data Integration Platform Supporting the Development of Predictive Models for Personalized Medicine.

Predictive models can support physicians to tailor interventions and treatments to their individual patients based on their predicted response and risk of disease and help in this way to put personalized medicine into practice. In allogeneic stem cell transplantation risk assessment is to be enhanced in order to respond to emerging viral infections and transplantation reactions. However, to develop predictive models it is necessary to harmonize and integrate high amounts of heterogeneous medical data that is stored in different health information systems. Driven by the demand for predictive instruments in allogeneic stem cell transplantation we present in this paper an ontology-based platform that supports data owners and model developers to share and harmonize their data for model development respecting data privacy.

Effects of heart rate variability biofeedback during exposure to fear-provoking stimuli within spider-fearful individuals: study protocol for a randomized controlled trial.

BACKGROUND: Specific phobias are among the most common anxiety disorders. Exposure therapy is the treatment of choice for specific phobias. However, not all patients respond equally well to it. Hence, current research focuses on therapeutic add-ons to increase and consolidate the effects of exposure therapy. One potential therapeutic add-on is biofeedback to increase heart rate variability (HRV). A recent meta-analysis shows beneficial effects of HRV biofeedback interventions on stress and anxiety symptoms. Therefore, the purpose of the current trial is to evaluate the effects of HRV biofeedback, which is practiced before and utilized during exposure, in spider-fearful individuals. Further, this trial is the first to differentiate between the effects of a HRV biofeedback intervention and those of a low-load working memory (WM) task. METHODS: Eighty spider-fearful individuals participate in the study. All participants receive a training session in which they practice two tasks (HRV biofeedback and a motor pseudo-biofeedback task or two motor pseudo-biofeedback tasks). Afterwards, they train both tasks at home for 6 days. One week later, during the exposure session, they watch 16 1-min spider video clips. Participants are divided into four groups: group 1 practices the HRV biofeedback and one motor pseudo-task before exposure and utilizes HRV biofeedback during exposure. Group 2 receives the same training, but continues the pseudo-biofeedback task during exposure. Group 3 practices two pseudo-biofeedback tasks and continues one of them during exposure. Group 4 trains in two pseudo-biofeedback tasks and has no additional task during exposure. The primary outcome is fear of spiders (measured by the Fear of Spiders Questionnaire and the Behavioral Approach Test). Secondary outcomes are physiological measures based on electrodermal activity, electrocardiogram and respiration. DISCUSSION: This RCT is the first one to investigate the effects of using a pre-trained HRV biofeedback during exposure in spider-fearful individuals. The study critically contrasts the effects of the biofeedback intervention with those of pseudo-tasks, which also require WM capacity, but which do not have a physiological base. If HRV biofeedback is effective in reducing fear of spiders, it would represent an easy-to-use tool to improve exposure-therapy outcomes. TRIAL REGISTRATION: Deutsches Register Klinischer Studien, DRKS00012278 . Registered on 23 May 2017, amendment on 5 October 2017.

Adaptive Workflows for Diabetes Management: Self-Management Assistant and Remote Treatment for Diabetes.

Diabetes self-management is a crucial element for all people with diabetes and those at risk for developing the disease. Diabetic patients should be empowered to increase their self-management skills in order to prevent or delay the complications of diabetes. This work presents the proposal and first development stages of a smartphone application focused on the empowerment of the patients with diabetes. The concept of this interventional tool is based on the personalization of the user experience from an adaptive and dynamic perspective. The segmentation of the population and the dynamical treatment of user profiles among the different experience levels is the main challenge of the implementation. The self-management assistant and remote treatment for diabetes aims to develop a platform to integrate a series of innovative models and tools rigorously tested and supported by the research literature in diabetes together the use of a proved engine to manage workflows for healthcare.

Performance characterization of a novel electronic number connection test to detect minimal hepatic encephalopathy in cirrhotic patients.

BACKGROUND AND AIM: Hepatic encephalopathy (HE) is a frequent complication of cirrhosis, characterized by cognitive deficits that negatively impact patients' quality of life. The mild, minimal hepatic encephalopathy (mHE) can only be detected by psychometric tests and early mHE detection can prevent more severe complications or even survival times. Here, we aimed to investigate the feasibility and validity of the novel-developed electronic number connection test (eNCT), which is designed as a fast and easy-to-perform mHE patient self-test. METHODS: The eNCT design was inspired by the paper-pencil number connection test version A, showing 25 numbers on the screen (1-25), in a random order. The time required to tap on all digits in the correct order was measured. A total of 238 individuals (112 patients with liver cirrhosis) were enrolled in this study and eNCT times were compared with well-established paper-pencil tests. The Psychometric Hepatic Encephalopathy Score test battery was used to detect mHE and cut-off values for mHE detection by the eNCT were defined. RESULTS: Overall, cirrhotic patients showed significantly slower test completion times compared with control participants. The eNCT performance was inversely correlated with Psychometric Hepatic Encephalopathy Score test performance in cirrhotic patients, independent of the HE status. Thirty cirrhotic patients fulfilled the mHE criteria and receiver operating characteristic curve analysis showed high sensitivity (>82%) and specificity (>85%) for mHE detection. Finally, the eNCT showed excellent test-retest reliability (intraclass correlation coefficient=0.94). CONCLUSION: The novel eNCT is a reliable HE self-test to monitor cognitive function and detect cognitive impairment in cirrhotic patients.

Designing smart analytical data services for a personal health framework.

Information in the healthcare domain and in particular personal health record information is heterogeneous by nature. Clinical, lifestyle, environmental data and personal preferences are stored and managed within such platforms. As a result, significant information from such diverse data is difficult to be delivered, especially to non-IT users like patients, physicians or managers. Another issue related to the management and analysis is the volume, which increases more and more making the need for efficient data visualization and analysis methods mandatory. The objective of this work is to present the architectural design for seamless integration and intelligent analysis of distributed and heterogeneous clinical information in the PHR context, as a result of a requirements elicitation process in iManageCancer project. This systemic approach aims to assist health-care professionals to orient themselves in the disperse information space and enhance their decision-making capabilities, to encourage patients to have an active role by managing their health information and interacting with health-care professionals.

A novel personal health system with integrated decision support and guidance for the management of chronic liver disease.

A personal health system platform for the management of patients with chronic liver disease that incorporates a novel approach to integrate decision support and guidance through care pathways for patients and their doctors is presented in this paper. The personal health system incorporates an integrated decision support engine that guides patients and doctors through the management of the disease by issuing tasks and providing recommendations to both the care team and the patient and by controlling the execution of a Care Flow Plan based on the results of tasks and the monitored health status of the patient. This Care Flow Plan represents a formal, business process based model of disease management designed off-line by domain experts on the basis of clinical guidelines, knowledge of care pathways and an organisational model for integrated, patient-centred care. In this way, remote monitoring and treatment are dynamically adapted to the patient's actual condition and clinical symptoms and allow flexible delivery of care with close integration of specialists, therapists and care-givers.

The p-medicine portal-a collaboration platform for research in personalised medicine.

The European project p-medicine creates an information technology infrastructure that facilitates the development from current medical practice to personalised medicine. The main access point to this infrastructure is the p-medicine portal that provides clinicians, patients, and researchers a platform to collaborate, share data and expertise, and use tools and services to improve personalised treatments of patients. In this document, we describe the community-based structure of the p-medicine portal and provide information about the p-medicine security framework implemented in the portal. Finally, we show the user interface and describe the p-medicine tools and services integrated in the portal.

p-BioSPRE-an information and communication technology framework for transnational biomaterial sharing and access.

Biobanks represent key resources for clinico-genomic research and are needed to pave the way to personalised medicine. To achieve this goal, it is crucial that scientists can securely access and share high-quality biomaterial and related data. Therefore, there is a growing interest in integrating biobanks into larger biomedical information and communication technology (ICT) infrastructures. The European project p-medicine is currently building an innovative ICT infrastructure to meet this need. This platform provides tools and services for conducting research and clinical trials in personalised medicine. In this paper, we describe one of its main components, the biobank access framework p-BioSPRE (p-medicine Biospecimen Search and Project Request Engine). This generic framework enables and simplifies access to existing biobanks, but also to offer own biomaterial collections to research communities, and to manage biobank specimens and related clinical data over the ObTiMA Trial Biomaterial Manager. p-BioSPRE takes into consideration all relevant ethical and legal standards, e.g., safeguarding donors' personal rights and enabling biobanks to keep control over the donated material and related data. The framework thus enables secure sharing of biomaterial within open and closed research communities, while flexibly integrating related clinical and omics data. Although the development of the framework is mainly driven by user scenarios from the cancer domain, in this case, acute lymphoblastic leukaemia and Wilms tumour, it can be extended to further disease entities.

A semantic approach for digital long-term preservation of electronic health documents.

Long-term preservation of electronic patient health information is a key issue for life-long electronic health records, however, it is poorly implemented in healthcare institutions and little attention is given to problems like obsolescence of formats and EHR applications or changing regulations, which jeopardize reusability of information after decades of preservation. We present in this paper an ontology driven approach to digital preservation and related metadata management which seems to be superior to conventional concepts of the digital library world.

The ACGT Master Ontology and its applications--towards an ontology-driven cancer research and management system.

OBJECTIVE: This paper introduces the objectives, methods and results of ontology development in the EU co-funded project Advancing Clinico-genomic Trials on Cancer-Open Grid Services for Improving Medical Knowledge Discovery (ACGT). While the available data in the life sciences has recently grown both in amount and quality, the full exploitation of it is being hindered by the use of different underlying technologies, coding systems, category schemes and reporting methods on the part of different research groups. The goal of the ACGT project is to contribute to the resolution of these problems by developing an ontology-driven, semantic grid services infrastructure that will enable efficient execution of discovery-driven scientific workflows in the context of multi-centric, post-genomic clinical trials. The focus of the present paper is the ACGT Master Ontology (MO). METHODS: ACGT project researchers undertook a systematic review of existing domain and upper-level ontologies, as well as of existing ontology design software, implementation methods, and end-user interfaces. This included the careful study of best practices, design principles and evaluation methods for ontology design, maintenance, implementation, and versioning, as well as for use on the part of domain experts and clinicians. RESULTS: To date, the results of the ACGT project include (i) the development of a master ontology (the ACGT-MO) based on clearly defined principles of ontology development and evaluation; (ii) the development of a technical infrastructure (the ACGT Platform) that implements the ACGT-MO utilizing independent tools, components and resources that have been developed based on open architectural standards, and which includes an application updating and evolving the ontology efficiently in response to end-user needs; and (iii) the development of an Ontology-based Trial Management Application (ObTiMA) that integrates the ACGT-MO into the design process of clinical trials in order to guarantee automatic semantic integration without the need to perform a separate mapping process.

The ObTiMA system - ontology-based managing of clinical trials.

Clinical Trial Management Systems promise to help researchers in managing the large amounts of data occurring in clinical trials. In such systems Case Report Forms for capturing all patient data can usually be defined freely for a given trial. But if database definitions are automatically derived from such trial-specific definitions then the collected data cannot be easily compared to or integrated into other trials. We address this interoperability issue with an approach based on ontology and semantic data mediation. This resulted in the development of the ObTiMA system which is composed of a component for setting-up clinical trials and another for handling patient data during trials. Both components offer data reusability by relying on shared concepts defined in an ontology covering the whole cancer care and research spectrum.

Ontology based data management systems for post-genomic clinical trials within a European Grid Infrastructure for Cancer Research.

Data management in post-genomic clinical trials is the process of collecting and validating clinical and genomic data with the goal to answer research questions and to preserve it for future scientific investigation. Comprehensive metadata describing the semantics of the data are needed to leverage it for further research like cross-trial analysis. Current clinical trial management systems mostly lack sufficient metadata and are not semantically interoperable. This paper outlines our approach to develop an application that allows trial chairmen to design their trial and especially the required data management system with comprehensive metadata according to their needs, integrating a clinical trial ontology into the design process. To demonstrate the built-in interoperability of data management systems developed in this way, we integrate these applications into a European biomedical Grid for cancer research in a way that the research data collected in the data management systems can be seamlessly analyzed and mined by researchers.