Online mining abnormal period patterns from multiple medical sensor data streams

With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient's status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients' streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.

Enhancing medical named entity recognition with an extended segment representation technique

Objective : The objective of this paper is to formulate an extended segment representation (SR) technique to enhance named entity recognition (NER) in medical applications.

Methods : An extension to the IOBES (Inside/Outside/Begin/End/Single) SR technique is formulated. In the proposed extension, a new class is assigned to words that do not belong to a named entity (NE) in one context but appear as an NE in other contexts. Ambiguity in such cases can negatively affect the results of classification-based NER techniques. Assigning a separate class to words that can potentially cause ambiguity in NER allows a classifier to detect NEs more accurately; therefore increasing classification accuracy.

Results : The proposed SR technique is evaluated using the i2b2 2010 medical challenge data set with eight different classifiers. Each classifier is trained separately to extract three different medical NEs, namely treatment, problem, and test. From the three experimental results, the extended SR technique is able to improve the average F1-measure results pertaining to seven out of eight classifiers. The kNN classifier shows an average reduction of 0.18% across three experiments, while the C4.5 classifier records an average improvement of 9.33%.

Fall incidence and fall prevention practices at acute care hospitals in Singapore : a retrospective audit

Objective To investigate the incidence of falls and explore fall prevention practices at acute care hospitals in Singapore.

Design A retrospective audit to collect baseline data on (1) incidence of falls (patient fall rates and fall injury rates) and (2) fall prevention practices, was conducted in five acute care hospitals in Singapore from December 2004 to March 2005.

Study participants Medical record data (n = 6000) of patients admitted into the medical, surgical and geriatric units in the five hospitals.

Outcome measures Fall incidence was obtained from the hospital's fall databases and incident reports for the period of June 2003 to May 2004. In total, 6000 medical records from five hospitals were randomly selected, retrieved and reviewed to determine whether falls, fall assessments and interventions were being initiated and documented.

Results The number of fallers for all hospitals was 825. Analysis showed that patient fall rates ranged from 0.68 to 1.44 per 1000 patient days, and the proportion of falls associated with injury ranged from 27.4% to 71.7%. The use of a fall risk assessment tool by nurses was recorded in 77% of all the nursing records.

Conclusion This study has laid the foundation for further research for fall prevention in Singapore by describing current fall rates, fall-associated injury rates and the status of fall prevention practices in acute care settings. The results will be used to inform the development of a tailored multifaceted strategy to facilitate the implementation of Fall Prevention Clinical Practice Guidelines to reduce the burden of falls and fall injuries in hospitals in Singapore.

Continuity of midwifery care and gestational weight gain in obese women : a randomised controlled trial

Background: The increased prevalence of obesity in pregnant women in Australia and other developed countries is a significant public health concern. Obese women are at increased risk of serious perinatal complications and guidelines recommend weight gain restriction and additional care. There is limited evidence to support the effectiveness of dietary and physical activity lifestyle interventions in preventing adverse perinatal outcomes and new strategies need to be evaluated. The primary aim of this project is to evaluate the effect of continuity of midwifery care on restricting gestational weight gain in obese women to the recommended range. The secondary aims of the study are to assess the impact of continuity of midwifery care on: women’s experience of pregnancy care; women’s satisfaction with care and a range of psychological factors.
Methods/Design: A two arm randomised controlled trial (RCT) will be conducted with primigravid women recruited from maternity services in Victoria, Australia. Participants will be primigravid women, with a BMI≥30 who are less than 17 weeks gestation. Women allocated to the intervention arm will be cared for in a midwifery continuity of care model and receive an informational leaflet on managing weight gain in pregnancy. Women allocated to the control group will receive routine care in addition to the same informational leaflet. Weight gain during pregnancy, standards of care, medical and obstetric information will be extracted from medical records. Data collected at recruitment (self administered survey) and at 36 weeks by postal survey will include sociodemographic information and the use of validated scales to measure secondary outcomes.
Discussion: Continuity of midwifery care models are well aligned with current Victorian, Australian and many international government policies on maternity care. Increasingly, midwifery continuity models of care are being introduced in low risk maternity care, and information on their application in high risk populations is required. There is an identified need to trial alternative antenatal interventions to reduce perinatal risk factors for women who are obese and the findings from this project may have application in other maternity services. In addition this study will inform a larger trial that will focus on birth and postnatal outcomes.

Sparse subspace clustering via group sparse coding

We propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efficient algorithms for solving the proposed group formulations. We demonstrate the advantage of the framework on three challenging benchmark datasets ranging from medical record data to image and text clustering and show that they consistently outperforms rival methods.

Medical image classification using an efficient data mining technique

Data mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.

An authorization policy management framework for dynamic medical data sharing

In this paper, we propose a novel feature reduction approach to group words hierarchically into clusters which can then be used as new features for document classification. Initially, each word constitutes a cluster. We calculate the mutual confidence between any two different words. The pair of clusters containing the two words with the highest mutual confidence are combined into a new cluster. This process of merging is iterated until all the mutual confidences between the un-processed pair of words are smaller than a predefined threshold or only one cluster exists. In this way, a hierarchy of word clusters is obtained. The user can decide the clusters, from a certain level, to be used as new features for document classification. Experimental results have shown that our method can perform better than other methods.

A particle swarm based hybrid system for imbalances medical data sampling

Background
Medical and biological data are commonly with small sample size, missing values, and most importantly, imbalanced class distribution. In this study we propose a particle swarm based hybrid system for remedying the class imbalance problem in medical and biological data mining. This hybrid system combines the particle swarm optimization (PSO) algorithm with multiple classifiers and evaluation metrics for evaluation fusion. Samples from the majority class are ranked using multiple objectives according to their merit in compensating the class imbalance, and then combined with the minority class to form a balanced dataset.

Results
One important finding of this study is that different classifiers and metrics often provide different evaluation results. Nevertheless, the proposed hybrid system demonstrates consistent improvements over several alternative methods with three different metrics. The sampling results also demonstrate good generalization on different types of classification algorithms, indicating the advantage of information fusion applied in the hybrid system.

Conclusion
The experimental results demonstrate that unlike many currently available methods which often perform unevenly with different datasets the proposed hybrid system has a better generalization property which alleviates the method-data dependency problem. From the biological perspective, the system provides indication for further investigation of the highly ranked samples, which may result in the discovery of new conditions or disease subtypes.

SMS-based medical diagnostic telemetry data transmission protocol for medical sensors

People with special medical monitoring needs can, these days, be sent home and remotely monitored through the use of data logging medical sensors and a transmission base-station. While this can improve quality of life by allowing the patient to spend most of their time at home, most current technologies rely on hardwired landline technology or expensive mobile data transmissions to transmit data to a medical facility. The aim of this paper is to investigate and develop an approach to increase the freedom of a monitored patient and decrease costs by utilising mobile technologies and SMS messaging to transmit data from patient to medico. To this end, we evaluated the capabilities of SMS and propose a generic communications protocol which can work within the constraints of the SMS format, but provide the necessary redundancy and robustness to be used for the transmission of non-critical medical telemetry from data logging medical sensors.

Data Integration Protocol In Ten-steps (DIPIT) : a new standard for medical researchers

The exponential increase in data, computing power and the availability of readily accessible analytical software has allowed organisations around the world to leverage the benefits of integrating multiple heterogeneous data files for enterprise-level planning and decision making. Benefits from effective data integration to the health and medical research community include more trustworthy research, higher service quality, improved personnel efficiency, reduction of redundant tasks, facilitation of auditing and more timely, relevant and specific information. The costs of poor quality processes elevate the risk of erroneous outcomes, an erosion of confidence in the data and the organisations using these data. To date there are no documented set of standards for best practice integration of heterogeneous data files for research purposes. Therefore, the aim of this paper is to describe a set of clear protocol for data file integration (Data Integration Protocol In Ten-steps; DIPIT) translational to any field of research.