A Conformance Test Suite for Arden Syntax Compilers and Interpreters

The Arden Syntax for Medical Logic Modules is a standardized and well-established programming language to represent medical knowledge. To test the compliance level of existing compilers and interpreters no public test suite exists. This paper presents the research to transform the specification into a set of unit tests, represented in JUnit. It further reports on the utilization of the test suite testing four different Arden Syntax processors. The presented and compared results reveal the status conformance of the tested processors. How test driven development of Arden Syntax processors can help increasing the compliance with the standard is described with two examples. In the end some considerations how an open source test suite can improve the development and distribution of the Arden Syntax are presented.

Multimodal activity monitoring for home rehabilitation of geriatric fracture patients--feasibility and acceptance of sensor systems in the GAL-NATARS study

BACKGROUND

Demographic change will lead to a diminishing care workforce faced with rising numbers of older persons in need of care, suggesting meaningful use of health-enabling technologies, and home monitoring in particular, to contribute to supporting both the carers and the persons in need.

OBJECTIVES

We present and discuss the GAL-NATARS study design along with first results regarding technical feasibility of long-term home monitoring and acceptance of different sensor modalities.

METHODS

Fourteen geriatric participants with mobility-impairing fractures were recruited in three geriatric clinics. Following inpatient geriatric rehabilitation, their homes were equipped with ambient sensor components for three months. Additionally, a wearable accelerometer was employed. Technical feasibility was assessed by system and component downtimes, technology acceptance by face-to-face interviews.

RESULTS

The overall system downtime was 6%, effected by two single events, but not by software failures. Technology acceptance was rated very high by all participants at the end of the monitoring periods, and no interference with their social lives was reported.

DISCUSSION AND CONCLUSIONS

Home-monitoring technologies were well-accepted by our participants. The information content of the data still needs to be evaluated with regard to clinical outcome parameters as well as the effect on the quality of life before recommending large-scale implementations.

Information and communication technologies for promoting and sustaining quality of life, health and self-sufficiency in ageing societies--outcomes of the Lower Saxony Research Network Design of Environments for Ageing (GAL)

Many societies across the world are confronted with demographic changes, usually related to increased life expectancy and, often, relatively low birth rates. Information and communication technologies (ICT) may contribute to adequately support senior citizens in aging societies with respect to quality of life and quality and efficiency of health care processes. For investigating and for providing answers on whether new information and communication technologies can contribute to keeping, or even improving quality of life, health and self-sufficiency in ageing societies through new ways of living and new forms of care, the Lower Saxony Research Network Design of Environments for Ageing (GAL) had been established as a five years research project, running from 2008 to 2013. Ambient-assisted living (AAL) technologies in personal and home environments were especially important. In this article we report on the GAL project, and present some of its major outcomes after five years of research. We report on major challenges and lessons learned in running and organizing such a large, inter- and multidisciplinary project and discuss GAL in the context of related research projects. With respect to research outcomes, we have, for example, learned new knowledge about multimodal and speech-based human-machine-interaction mechanisms for persons with functional restrictions, and identified new methods and developed new algorithms for identifying activities of daily life and detecting acute events, particularly falls. A total of 79 apartments of senior citizens had been equipped with specific "GAL technology", providing new insights into the use of sensor data for smart homes. Major challenges we had to face were to deal constructively with GAL's highly inter- and multidisciplinary aspects, with respect to research into GAL's application scenarios, shifting from theory and lab experimentation to field tests, and the complexity of organizing and, in our view, successfully managing such a large project. Overall it can be stated that, from our point of view, the GAL research network has been run successfully and has achieved its major research objectives. Since we now know much more on how and where to use AAL technologies for new environments of living and new forms of care, a future focus for research can now be outlined for systematically planned studies, scientifically exploring the benefits of AAL technologies for senior citizens, in particular with respect to quality of life and the quality and efficiency of health care.

An HL7-FHIR-based Object Model for a Home-Centered Data Warehouse for Ambient Assisted Living Environments

Current AAL environments focus on assisting a single person with seperated technologies. There is no interoperability between sub-domains in home environments, like building energy management or housing industry services. BASIS (Building Automation by a Scalable and Intelligent System) aims to integrate all sensors and actuators into a single, efficient home bus. First step is to create a semtically enriched data warehouse object model. We choose FHIR and built an object model mainly based on the Observation, Device and Location resources with minor extensions needed by AAL-foreign sub domains. FHIR turned out to be very flexible and complete for other home related sub-domains. The object model is implemented in a separated software-partition storing all structural and procedural data of BASIS.

Rolling Medical Practice: Ambulant Mobile Medical Care for Rural Areas

We designed, constructed, and evaluated a mobile medical care vehicle called "Rollende Arztpraxis" (rolling medical practice, RMP) that delivers the full medical care of a general practitioner to increase medical care supply in rural areas. Six communities have been identified, where the RMP has been visited 501 times in 14 months. Two different schedules of stops and treatment times have been tested. We show that the RMP treated mainly elderly and multimorbid patients. An accompanying study showed high acceptance and satisfaction of treated patients and treating doctors. An economic evaluation of three different utilization models with three treatment modes each showed no financial sustainability. We show that ambulatory care in rural areas can be complemented by a mobile care unit, if legal and financial barriers can be overcome.

A methodological framework for the analysis of highly intensive, multimodal and heterogeneous data in the context of health-enabling technologies and ambient-assisted living

BACKGROUND

When applying health-enabling technologies (HET), researchers are faced with analyzing highly intensive, multimodal and heterogeneous data sets. Experience has shown that there is a lack of understanding concerning the relationship of analysis methods suitable for such data sets and their appropriate application.

OBJECTIVES

The objective of this paper is to describe the present situation when analyzing data of HET and the main problems in this context, to present a nomenclature suitable for analysis methods in the context of HET, to present an example dealing with geriatric diseases that highlights the problems and the urgent need for results and to explain some steps for future work.

METHODS

Nomenclatures as standard tools in information processing are applied.

RESULTS

We present an open three-axial mono-hierarchical nomenclature called SNOCAP-HET. Moreover, we explain other ideas to overcome the lack of systematization within the set of analysis methods suitable for HET.

CONCLUSIONS

Our approach allows for an extension of SNOCAP-HET and will allow for the development and evaluation of different measures for the appropriateness of analysis methods given a certain highly intensive, multimodal and heterogeneous data set in the context of HET. Our final future aim is to obtain better results when analyzing medical data.

Rollende arztpraxis - first results of an ambulant mobile care model for rural areas

German medical care is going to suffer from a decreasing number of general practitioners due to demographic change. We study if ambulatory care in rural areas can be complementary ensured by a mobile care unit. A medical care van - the "rolling medical practice" (RMP) - has been constructed based on care scenarios created for rural communities in northern Germany. Performance and acceptance of the RMP is evaluated by constant monitoring of anonymized medical documentation and questionnaires. The RMP is visiting six villages on two days a week in a three-week interval. It is constructed from a standard van with a custom box body fully equipped for general care needs. Actually treated care cases meet expectations and are acute as well as chronicle symptoms. Case numbers range from 6 to 50 visits in 5 month. We showed that almost full ranged mobile medical care, as supplement to general medical supply is possible.

Development and clinical validation of an unobtrusive ambulatory knee function monitoring system with inertial 9DoF sensors

Patients suffering from end-stage knee osteoarthritis are often treated with total knee arthroplasty, improving their functional mobility. A number of patients, however, report continued difficulty with stair ascent and descent or sportive activity after surgery and are not completely satisfied with the outcome. State-of-the-art analyses to evaluate the outcome and mobility after knee replacement are conducted under supervised settings in specialized gait labs and thus can only reflect a short period of time. A number of external factors may lead to artificial gait patterns in patients. Moreover, clinically relevant situations are difficult to simulate in a stationary gait lab. In contrast to this, inertial sensors may be used additionally for unobtrusive gait monitoring. However, recent notable approaches found in literature concerning knee function analysis have so far not been applied in a clinical context and have therefore not yet been validated in a clinical setting. The aim of this paper is to present a system for unsupervised long-term monitoring of human gait with a focus on knee joint function, which is applicable in patients' everyday lives and to report on the validation of this system gathered during walking with reference to state-of-the-art gait lab data using a vision system (VICON Motion System). The system KINEMATICWEAR - developed in close collaboration of computer scientists and physicians performing knee arthroplasty - consists of two sensor nodes with combined tri-axial accelerometer, gyroscope and magnetometer to be worn under normal trousers. Reliability of the system is shown in the results. An overall correlation of 0.99 (with an overall RMSE of 2.72) compared to the state-of-the-art reference system indicates a sound quality and a high degree of correspondence. KINEMATICWEAR enables ambulatory, unconstrained measurements of knee function outside a supervised lab inspection.

Performance comparison of accelerometer calibration algorithms based on 3D-ellipsoid fitting methods

Calibration of accelerometers can be reduced to 3D-ellipsoid fitting problems. Changing extrinsic factors like temperature, pressure or humidity, as well as intrinsic factors like the battery status, demand to calibrate the measurements permanently. Thus, there is a need for fast calibration algorithms, e.g. for online analyses. The primary aim of this paper is to propose a non-iterative calibration algorithm for accelerometers with the focus on minimal execution time and low memory consumption. The secondary aim is to benchmark existing calibration algorithms based on 3D-ellipsoid fitting methods. We compared the algorithms regarding the calibration quality and the execution time as well as the number of quasi-static measurements needed for a stable calibration. As evaluation criterion for the calibration, both the norm of calibrated real-life measurements during inactivity and simulation data was used. The algorithms showed a high calibration quality, but the execution time differed significantly. The calibration method proposed in this paper showed the shortest execution time and a very good performance regarding the number of measurements needed to produce stable results. Furthermore, this algorithm was successfully implemented on a sensor node and calibrates the measured data on-the-fly while continuously storing the measured data to a microSD-card.

Assessment of risk factors for post-rewarming "rebound hyperthermia" in cardiac arrest patients undergoing therapeutic hypothermia

INTRODUCTION

The outcomes associated with therapeutic hypothermia (TH) after cardiac arrest, while overwhelmingly positive, may be associated with adverse events. The incidence of post-rewarming rebound hyperthermia (RH) has been relatively unstudied and may worsen survival and neurologic outcome. The purpose of this study was to determine the incidence and risk factors associated with RH as well as its relationship to mortality, neurologic morbidity, and hospital length of stay (LOS).

METHODS

A retrospective, observational study was performed of adult patients who underwent therapeutic hypothermia after an out-of-hospital cardiac arrest. Data describing 17 potential risk factors for RH were collected. The primary outcome was the incidence of RH while the secondary outcomes were mortality, discharge neurologic status, and LOS.

RESULTS

141 patients were included. All 17 risk factors for RH were analyzed and no potential risk factors were found to be significant at a univariate level. 40.4% of patients without RH experienced any cause of death during the initial hospitalization compared to 64.3% patients who experienced RH (OR: 2.66; 95% CI: 1.26-5.61; p=0.011). The presence of RH is not associated with an increase in LOS (10.67 days vs. 9.45 days; absolute risk increase=-1.21 days, 95% CI: -1.84 to 4.27; p=0.434). RH is associated with increased neurologic morbidity (p=0.011).

CONCLUSIONS

While no potential risk factors for RH were identified, RH is a marker for increased mortality and worsened neurologic morbidity in cardiac arrest patients who have underwent TH.

High intensity, multimodality and incoherence: grand challenges in the analysis of data for health-enabling technologies

When working with health-enabling technologies, researchers all over the world usually have to analyze highly intensive, multimodal and incoherent data. We explain that there is a lack of systematization within the set of methods of analysis suitable for these data. As a first step towards a methodology in this context, we present the Systematic Nomenclature for Contexts, Analysis Methods and Problems in Health-Enabling Technologies (SNOCAP-HET).

Some thoughts on personal living environments as new diagnostic and therapeutic areas

Some thoughts on personal living environments as new diagnostic and therapeutic areas are presented, considering the following aspects: current potentials for diagnosis and therapy in personal living environments, consequences for care processes and for health information systems architectures and infrastructures, and informational self-determination and privacy. They may stimulate further discussion on respective research.

Design and implementation of the standards-based personal intelligent self-management system (PICS)

Against the background of demographic change and a diminishing care workforce there is a growing need for personalized decision support. The aim of this paper is to describe the design and implementation of the standards-based personal intelligent care systems (PICS). PICS makes consistent use of internationally accepted standards such as the Health Level 7 (HL7) Arden syntax for the representation of the decision logic, HL7 Clinical Document Architecture for information representation and is based on a open-source service-oriented architecture framework and a business process management system. Its functionality is exemplified for the application scenario of a patient suffering from congestive heart failure. Several vital signs sensors provide data for the decision support system, and a number of flexible communication channels are available for interaction with patient or caregiver. PICS is a standards-based, open and flexible system enabling personalized decision support. Further development will include the implementation of components on small computers and sensor nodes.

Wearable sensors in healthcare and sensor-enhanced health information systems: all our tomorrows?

Wearable sensor systems which allow for remote or self-monitoring of health-related parameters are regarded as one means to alleviate the consequences of demographic change. This paper aims to summarize current research in wearable sensors as well as in sensor-enhanced health information systems. Wearable sensor technologies are already advanced in terms of their technical capabilities and are frequently used for cardio-vascular monitoring. Epidemiologic predictions suggest that neuropsychiatric diseases will have a growing impact on our health systems and thus should be addressed more intensively. Two current project examples demonstrate the benefit of wearable sensor technologies: long-term, objective measurement under daily-life, unsupervised conditions. Finally, up-to-date approaches for the implementation of sensor-enhanced health information systems are outlined. Wearable sensors are an integral part of future pervasive, ubiquitous and person-centered health care delivery. Future challenges include their integration into sensor-enhanced health information systems and sound evaluation studies involving measures of workload reduction and costs.

Health-enabling technologies for the elderly--an overview of services based on a literature review

BACKGROUND

Services for the elderly based on health-enabling technologies promise to contribute significantly to the efficiency and effectiveness of future health care. Due to this promise, over the last years the scientific community has designed a complex variety of these valuable innovations. A systematic overview of the developed services would help to better understand their opportunities and limitations.

OBJECTIVE

To obtain a systematic overview of services for the elderly based on health-enabling technologies and to identify archetypical service categories.

METHODS

We conducted a literature review using PubMed and retrieved 1447 publications. We stepwise reduced this list to 27 key publications that describe typical service archetypes.

RESULTS

We present six archetypical service categories, namely handling adverse conditions, assessing state of health, consultation and education, motivation and feedback, service ordering and social inclusion and describe their implementation in current research projects.

ARDEN2BYTECODE: a one-pass Arden Syntax compiler for service-oriented decision support systems based on the OSGi platform

Patient empowerment might be one key to reduce the pressure on health care systems challenged by the expected demographic changes. Knowledge based systems can, in combination with automated sensor measurements, improve the patients' ability to review their state of health and make informed decisions. The Arden Syntax as a standardized language to represent medical knowledge can be used to express the corresponding decision rules. In this paper we introduce ARDEN2BYTECODE, a newly developed open source compiler for the Arden Syntax. ARDEN2BYTECODE runs on Java Virtual Machines (JVM) and translates Arden Syntax directly to Java Bytecode (JBC) executable on JVMs. ARDEN2BYTECODE easily integrates into service oriented architectures, like the Open Services Gateway Initiative (OSGi) platform. Apart from an evaluation of compilation performance and execution times, ARDEN2BYTECODE was integrated into an existing knowledge supported exercise training system and recorded training sessions have been used to check the implementation.

A method to align the coordinate system of accelerometers to the axes of a human body: The depitch algorithm

One of the key problems in accelerometry based gait analyses is that it may not be possible to attach an accelerometer to the lower trunk so that its axes are perfectly aligned to the axes of the subject. In this paper we will present an algorithm that was designed to virtually align the axes of the accelerometer to the axes of the subject during walking sections. This algorithm is based on a physically reasonable approach and built for measurements in unsupervised settings, where the test persons are applying the sensors by themselves. For evaluation purposes we conducted a study with 6 healthy subjects and measured their gait with a manually aligned and a skewed accelerometer attached to the subject's lower trunk. After applying the algorithm the intra-axis correlation of both sensors was on average 0.89±0.1 with a mean absolute error of 0.05g. We concluded that the algorithm was able to adjust the skewed sensor node virtually to the coordinate system of the subject.

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

Background

Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).

Methods

A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.

Results

The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.

Conclusions

Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.

A literature overview and comparison of driving impairing condition assessment approaches by health enabling technologies

Due to demographic change, more elderly people have the need to preserve and support mobility by car despite age-related functional limitations. Since accidents by the elderly are primarily caused by age related limitations, and not by careless or irresponsible behavior, it may be beneficial to detect driving impairing conditions. The presented review gives an overview of technologies to detect driving impairing conditions like drowsiness and stress or excessive demand. A comparison of the approaches to detect these conditions suggests that a combination of approaches is the most feasible method. However, there are still few systems that focus on the elderly.

Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

Background

Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data.

Methods

In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched.

Results

Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores.

Conclusions

Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.

Monitoring systems for the support of home care

In this article, the design of a system for the ambient, unobtrusive and automatic monitoring of Activities of Daily Living (ADL) is described. In the context of the growing imbalance between (potentially young) caregivers and (most often older) people receiving care, technical monitoring systems may help to organise care more efficiently and to identify degrading abilities very early to trigger preventive measures. To improve the acceptance of the system described in this article, the selection process of the sensors to be integrated into the flat or to be worn by the older people has been steered by the results of focus group interviews with older people, their relatives and professional caregivers. The interviews revealed that these people would in general accept such systems, but security, mobility and communication aspects have to be clearly and appropriately addressed. In an experimental study the recognition rate of the activity 'preparation and intake of food or beverages' has been measured with two age groups (6 subjects, age between 25 and 40/mean 30 years and 5 subjects, age between 72 and 84/mean 75.3 years). The food preparation was detected with a sensitivity of 74.7% and a specificity of 84.2% using a vision sensor.