waashk/dblp huggingface dataset:
classifying alarms in intensive care - analogy to hypothesis testing. monitoring devices in intensive care units observe a patient's health status and trigger an alarm in critical situations. the alarm rules in commercially available monitoring systems are usually based on simple thresholds set by the clinical staff. though there are some more advanced alarm rules integrated in modern monitoring devices, even for those, the false alarm rate is very high. decision trees have proven suitable for alarm classification and false alarm reduction. random forests which are ensembles of trees can improve the accuracy compared to single trees in many situations. in intensive care, the probability of misclassifying a situation in which an alarm is needed has to be controlled. subject to this constraint the probability of misclassifying a situation in which no alarm should be given has to be minimized - an analogy to a hypothesis test for testing "situation is alarm relevant" vs. "situation is non alarm relevant" based on an ensemble of trees. this yields a classification rule for any given significance level, which is the probability of misclassifying alarm relevant situations. we apply this procedure to annotated physiological data recorded at an intensive care unit and generate rules for false alarm reduction.
an intelligent aide for interpreting a patient's dialysis data set. many machines used in the modern hospital settings offer real time physiological monitoring. haemodialysis machines combine a therapeutic treatment system integrated with sophisticated monitoring equipment. a large array of parameters can be collected including cardiovascular measures such as heart rate and blood pressure together with treatment related data including relative blood volume, ultrafiltration rate and small molecule clearance. a small subset of this information is used by clinicians to monitor treatment and plan therapeutic strategies but it is not usually analysed in any detail. the focus of this paper is the analysis of data collected over a number of treatment sessions with a view to predicting patient physiological behaviour whilst on dialysis and correlating this with clinical characteristics of individual patients.one of the commonest complications experienced by patients on dialysis is symptomatic hypotension. we have taken real time treatment data and outline a program of work which attempts to predict when hypotension is likely to occur, and which patients might be particularly prone to haemodynamic instability. this initial study has investigated: the rate of change of blood pressure versus rate of change of heart rate, rate of fluid removal, and rate of uraemic toxin clearance. we have used a variety of machine learning techniques (including hierarchical clustering, and bayesian network analysis algorithms). we have been able to detect from this dataset, 3 distinct groups which appear to be clinically meaningful. furthermore we have investigated whether it is possible to predict changes in blood pressure in terms of other parameters with some encouraging results that merit further study.
knowledge acquisition from a medical corpus: use and return on experiences. the present work aims at refining and expanding user's queries thanks to association rules. we adapted the a-close algorithm to a medical corpus indexed by mesh descriptors. the originality of our approach lies in the use of the association rules in the information retrieval process and the exploitation of the structure of the domain knowledge to evaluate the association rules. the results show the usefulness of this query expansion approach. based on observations, new knowledge is modelled as expert rules.
building an ontology of hypertension management. the analysis of customized decisions during hypertension management in a specialized unit requires a detailed representation of clinical cases. we are building a specific ontology to code medical records and process them with computerized tools. relevant concepts to describe and justify medical decisions are extracted from three sources: (i) clinical guidelines; (ii) items of the semi-structured medical record form used in the clinical unit; (iii) free-text answers from 5,000 completed record forms. combining terminological sources is mandatory to cover the whole spectrum of possible justifications for clinical decisions, including contextual specificities and patients' particulars.
replacing sep-triplets in snomed ct using tractable description logic operators. reification of parthood relations according to the sep-triplet encoding pattern has been employed in the clinical terminology snomed ct to simulate transitivity of the part-of relation via transitivity of the is-a relation and to inherit properties along part-of links. in this paper we argue that using a more expressive representation language, which allows for a direct representation of the relevant properties of the part-of relation, makes modelling less error prone while having no adverse effect on the efficiency of reasoning.
discovery and integration of organ-failure episodes in mortality prediction. current predictive models in the intensive care rely on summaries of data collected at patient admission. it has been shown recently that temporal patterns of the daily sequential organ failure assessment (sofa) scores can improve predictions. however, the derangement of the six individual organ systems underlying the calculation of a sofa score were not taken into account, thus impeding the understanding of their prognostic merits. in this paper we propose a method for model induction that integrates in a novel way the individual organ failure scores with sofa scores. the integration of these two correlated components is achieved by summarizing the historic sofa information and at the same time by capturing the evolution of individual organ system failure status. the method also explicitly avoids the collinearity problem among organ failure episodes. we report on the application of our method to a large dataset and demonstrate its added value. the ubiquity of severity scores and sub-scores in medicine renders our approach relevant to a wide range of medical domains.
interpreting gene expression data by searching for enriched gene sets. this paper presents a novel method integrating gene-gene interaction information and gene ontology (go) for the construction of new gene sets that are potentially enriched. enrichment of a gene set is determined by gene set enrichment analysis. the experimental results show that the introduced method improves over existing methods, i.e. that it is capable to find new descriptions of the biology governing the experiments, not detectable by the traditional methods of evaluating the enrichment of predefined gene sets, defined by a single go term.
document-oriented views of guideline knowledge bases. a computer-interpretable guideline knowledge base can be a very large network whose information content is difficult for developers and clinicians to comprehend and review. we created a method to annotate a guideline model and use the annotations to export the guideline knowledge base in an xml format that can be transformed into a readable document. we applied this method to knowledge bases developed in three different guideline modeling projects to analyze uses and limitations of this approach. we demonstrate the promise of creating such document-oriented views, but conclude that guideline models and knowledge bases should be constructed with the goal of creating such human-comprehensible views from the beginning.
literature mining: towards better understanding of autism. in this article we present a literature mining method rajolink that upgrades swanson's abc model approach to uncovering hidden relations from a set of articles in a given domain. when these relations are interesting from medical point of view and can be verified by medical experts, they represent new pieces of knowledge and can contribute to better understanding of diseases. in our study we analyzed biomedical literature about autism, which is a very complex and not yet sufficiently understood domain. on the basis of word frequency statistics several rare terms were identified with the aim of generating potentially new explanations for the impairments that are observed in the affected population. calcineurin was discovered as a joint term in the intersection of their corresponding literature. similarly, nf-kappab was recognized as a joint term. pairs of documents that point to potential relations between the identified joint terms and autism were also automatically detected. expert evaluation confirmed the relevance of these relations.
hierarchical latent class models and statistical foundation for traditional chinese medicine. the theories of traditional chinese medicine (tcm) originated from experiences doctors had with patients in ancient times. we ask the question whether aspects of tcm theories can be reconstructed through modern day data analysis. we have recently analyzed a tcm data set using a machine learning method and found that the resulting statistical model matches the relevant tcm theory well. this is an exciting discovery because it shows that, contrary to common perception, there are scientific truths in tcm theories. it also suggests the possibility of laying a statistical foundation for tcm through data analysis and thereby turning it into a modern science.
r-cast-med: applying intelligent agents to support emergency medical decision-making teams. decision-making is a crucial aspect of emergency response during mass casualty incidents (mcis). mcis require rapid decisions to be taken by geographically-dispersed teams in an environment characterized by insufficient information, ineffective collaboration and inadequate resources. despite the increasing adoption of decision support systems in healthcare, there is limited evidence of their value in large-scale disasters. we conducted focus groups with emergency medical services and emergency department personnel who revealed that one of the main challenges in emergency response during mcis is information management. therefore, to alleviate the issues arising from ineffective information management, we propose r-cast-med, an intelligent agent architecture built on recognition-primed decision-making (rpd) and shared mental models (smms). a simulation of r-cast-med showed that this tool enabled efficient information management by identifying relevant information, inferring missing information and sharing information with other agents, which led to effective collaboration and coordination of tasks across teams.
an ultra low-power processor for sensor networks. we present a novel processor architecture designed specifically for use in low-power wireless sensor-network nodes. our sensor network asynchronous processor (snap/le) is based on an asynchronous data-driven 16-bit risc core with an extremely low-power idle state, and a wakeup response latency on the order of tens of nanoseconds. the processor instruction set is optimized for sensor-network applications, with support for event scheduling, pseudo-random number generation, bitfield operations, and radio/sensor interfaces. snap/le has a hardware event queue and event coprocessors, which allow the processor to avoid the overhead of operating system software (such as task schedulers and external interrupt servicing), while still providing a straightforward programming interface to the designer. the processor can meet performance levels required for data monitoring applications while executing instructions with tens of picojoules of energy.we evaluate the energy consumption of snap/le with several applications representative of the workload found in data-gathering wireless sensor networks. we compare our architecture and software against existing platforms for sensor networks, quantifying both the software and hardware benefits of our approach.
active disks: programming model, algorithms and evaluation. several application and technology trends indicate that it might be both profitable and feasible to move computation closer to the data that it processes. in this paper, we evaluate active disk architectures which integrate significant processing power and memory into a disk drive and allow application-specific code to be downloaded and executed on the data that is being read from (written to) disk. the key idea is to offload bulk of the processing to the diskresident processors and to use the host processor primarily for coordination, scheduling and combination of results from individual disks. to program active disks, we propose a stream-based programming model which allows disklets to be executed efficiently and safely. simulation results for a suite of six algorithms from three application domains (commercial data warehouses, image processing and satellite data processing) indicate that for these algorithms, active disks outperform conventional-disk architectures.
dcg: an efficient, retargetable dynamic code generation system. dynamic code generation allows aggressive optimization through the use of runtime information. previous systems typically relied on ad hoc code generators that were not designed for retargetability, and did not shield the client from machine-specific details. we present a system, dcg, that allows clients to specify dynamically generated code in a machine-independent manner. our one-pass code generator is easily retargeted and extremely efficient (code generation costs approximately 350 instructions per generated instruction). experiments show that dynamic code generation increases some application speeds by over an order of magnitude.
softflash: analyzing the performance of clustered distributed virtual shared memory. one potentially attractive way to build large-scale shared-memory machines is to use small-scale to medium-scale shared-memory machines as clusters that are interconnected with an off-the-shelf network. to create a shared-memory programming environment across the clusters, it is possible to use a virtual shared-memory software layer. because of the low latency and high bandwidth of the interconnect available within each cluster, there are clear advantages in making the clusters as large as possible. the critical question then becomes whether the latency and bandwidth of the top-level network and the software system are sufficient to support the communication demands generated by the clusters.to explore these questions, we have built an aggressive kernel implementation of a virtual shared-memory system using sgi multiprocessors and 100mbyte/sec hippi interconnects. the system obtains speedups on 32 processors (four nodes, eight processors per node plus additional reserved protocol processors) that range from 6.9 on the communication-intensive fft program to 21.6 on ocean (both from the splash 2 suite). in general, clustering is effective in reducing internode miss rates, but as the cluster size increases, increases in the remote latency, mostly due to increased tlb synchronization cost, offset the advantages. for communication-intensive applications, such as fft, the overhead of sending out network requests, the limited network bandwidth, and the long network latency prevent the achievement of good performance. overall, this approach still appears promising, but our results indicate that large low latency networks may be needed to make cluster-based virtual shared-memory machines broadly useful as large-scale shared-memory multiprocessors.
a multi-microprocessor architecture with hardware support for communication and scheduling. we describe a multiprocessor system that attempts to enhance the system performance by incorporating into its architecture a number of key operating system concepts. in particular: &mdash; the scheduling and synchronization of concurrent activities are built in at the hardware level, &mdash; the interprocess communication functions are performed in hardware, and, &mdash; a coupling between the scheduling and communication functions is provided which allows efficient implementation of parallel systems that is precluded when the scheduling and communication functions are realized in software.
high speed switch scheduling for local area networks. current technology trends make it possible to build communication networks that can support high-performance distributed computing. this paper describes issues in the design of a prototype switch for an arbitrary topology point-to-point network with link speeds of up to 1 gbit/s. the switch deals in fixed-length atm-style cells, which it can process at a rate of 37 million cells per second. it provides high bandwidth and low latency for datagram traffic. in addition, it supports real-time traffic by providing bandwidth reservations with guaranteed latency bounds. the key to the switch's operation is a technique called parallel iterative matching, which can quickly identify a set of conflict-free cells for transmission in a time slot. bandwidth reservations are accommodated in the switch by building a fixed schedule for transporting cells from reserved flows across the switch; parallel iterative matching can fill unused slots with datagram traffic. finally, we note that parallel iterative matching may not allocate bandwidth fairly among flows of datagram traffic. we describe a technique called statistical matching, which can be used to ensure fairness at the switch and to support applications with rapidly changing needs for guaranteed bandwidth.
thread level parallelism and interactive performance of desktop applications. multiprocessing is already prevalent in servers where multiple clients present an obvious source of thread-level parallelism. however, the case for multiprocessing is less clear for desktop applications. nevertheless, architects are designing processors that count on the availability of thread-level parallelism. unlike server workloads, the primary requirement of interactive applications is to respond to user events under human perception bounds rather than to maximize end-to-end throughput. in this paper we report on the thread-level parallelism and interactive response time of a variety of desktop applications. by tracking the communication between tasks, we can focus our measurements on the portions of the benchmark's execution that have the greatest impact on the user. we find that running our benchmarks on a dual-processor machine improves response time of mouse-click events by as much as 36%, and 22% on average---out of a maximum possible 50%. the benefits of multiprocessing are even more apparent when background tasks are considered. in our experiments, running a simple mp3 playback program in the background increases response time by 14% on a uniprocessor while it only increases the response time on a dual processor by 4%. when response times are fast enough for further improvements to be imperceptible, the increased idle time after interactive episodes could be exploited to build systems that are more power efficient.
adapting to network and client variability via on-demand dynamic distillation. the explosive growth of the internet and the proliferation of smart cellular phones and handheld wireless devices is widening an already large gap between internet clients. clients vary in their hardware resources, software sophistication, and quality of connectivity, yet server support for client variation ranges from relatively poor to none at all. in this paper we introduce some design principles that we believe are fundamental to providing "meaningful" internet access for the entire range of clients. in particular, we show how to perform on-demand datatype-specific lossy compression on semantically typed data, tailoring content to the specific constraints of the client. we instantiate our design principles in a proxy architecture that further exploits typed data to enable application-level management of scarce network resources. our proxy architecture generalizes previous work addressing all three aspects of client variation by applying well-understood techniques in a novel way, resulting in quantitatively better end-to-end performance, higher quality display output, and new capabilities for low-end clients.
efficient debugging primitives for multiprocessors. existing kernel-level debugging primitives are inappropriate for instrumenting complex sequential or parallel programs. these functions incur a heavy overhead in their use of system calls and process switches. context switches are used to alternately invoke the debugger and the target programs. system calls are used to communicate data between the target and debugger. none of this is necessary in shared-memory multiprocessors. multiple processors concurrently run both the debugger and the target. shared-memory is used to implement efficient communication. the target's state is accessed by running both the target and the debugger in the same address space. finally, instrumentation points, which have largely been implemented as traps to the system, are reimplemented as simple branches to routines of arbitrary complexity maintained by the debugger. not only are primitives such as conditional breakpoints thus generalized, but their efficiency is improved by several orders of magnitude. in the process, much of the traditional system's kernel support for debugging is reimplemented at user-level. this paper describes the implementation of debugging primitives in parasight, a parallel programming environment. parasight has been used to implement conditional breakpoints, an important primitive for both high-level and parallel debugging. preliminary measurements indicate that parasight breakpoints are 1000 times faster than the breakpoints in parallel &ldquo;cdb&rdquo;, a conventional unix debugger. light-weight conditional breakpoints open up new opportunities for debugging and profiling both parallel and sequential programs.
value speculation scheduling for high performance processors. recent research in value prediction shows a surprising amount of predictability for the values produced by register-writing instructions. several hardware based value predictor designs have been proposed to exploit this predictability by eliminating flow dependencies for highly predictable values. this paper proposed a hardware and software based scheme for value speculation scheduling (vss). static vliw scheduling techniques are used to speculate value dependent instructions by scheduling them above the instructions whose results they are dependent on. prediction hardware is used to provide value predictions for allowing the execution of speculated instructions to continue. in the case of miss-predicted values, control flow is redirected to patch-up code so that execution can proceed with the correct results. in this paper, experiments in vss for load operations in the specint95 benchmarks are performed. speedup of up to 17% has been shown for using vss. empirical results on the value predictability of loads, based on value profiling data, are also provided.
a technique for monitoring run-time dynamics of an operating system and a microprocessor executing user applications. in this paper, we present a non-invasive and efficient technique for simulating applications complete with their operating system interaction. the technique involves booting and initiating an application on a hardware development system, capturing the entire state of the application and the microprocessor at a well defined point in execution and then simulating the application on microprocessor simulators. extensive statistics generated from the simulators on run-time dynamics of the application, the operating system as well as the microprocessor enabled us to tune the operating system and the microprocessor architecture and implementation. the results also enabled us to optimize system level design choices by anticipating/predicting the performance of the target system. lastly, the results were used to adjust and refocus the evolution of the architecture of both the operating system and the microprocessor.