ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data

Sepsis is a deadly condition affecting many patients in the hospital. Recent studies have shown that patients diagnosed with sepsis have significant mortality and morbidity, resulting from the body's dysfunctional host response to infection. Clinicians often rely on the use of Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and the Modified Early Warning Score (MEWS) to identify early signs of clinical deterioration requiring further work-up and treatment. However, many of these tools are manually computed and were not designed for automated computation. There have been different methods used for developing sepsis onset models, but many of these models must be trained on a sufficient number of patient observations in order to form accurate sepsis predictions. Additionally, the accurate annotation of patients with sepsis is a major ongoing challenge. In this paper, we propose the use of Active Learning Recurrent Neural Networks (ALRts) for short temporal horizons to improve the prediction of irregularly sampled temporal events such as sepsis. We show that an active learning RNN model trained on limited data can form robust sepsis predictions comparable to models using the entire training dataset.Comment: 11 pages, 5 figures, 2 table

Improving Delivery Lead Time In Medical Device Supplies To Public Hospitals In Malaysia

This project paper focuses on issues affecting Thera Medic on its delivery lead time for supplies of medical devices (surgical products) to public hospitals in Malaysia. This paper also aims to provide recommendation to improve the overall lead time to meet the lead time expected by the customers

A dynamic visual analytics framework for complex temporal environments

Introduction: Data streams are produced by sensors that sample an external system at a periodic interval. As the cost of developing sensors continues to fall, an increasing number of data stream acquisition systems have been deployed to take advantage of the volume and velocity of data streams. An overabundance of information in complex environments have been attributed to information overload, a state of exposure to overwhelming and excessive information. The use of visual analytics provides leverage over potential information overload challenges. Apart from automated online analysis, interactive visual tools provide significant leverage for human-driven trend analysis and pattern recognition. To facilitate analysis and knowledge discovery in the space of multidimensional big data, research is warranted for an online visual analytic framework that supports human-driven exploration and consumption of complex data streams. Method: A novel framework was developed called the temporal Tri-event parameter based Dynamic Visual Analytics (TDVA). The TDVA framework was instantiated in two case studies, namely, a case study involving a hypothesis generation scenario, and a second case study involving a cohort-based hypothesis testing scenario. Two evaluations were conducted for each case study involving expert participants. This framework is demonstrated in a neonatal intensive care unit case study. The hypothesis generation phase of the pipeline is conducted through a multidimensional and in-depth one subject study using PhysioEx, a novel visual analytic tool for physiologic data stream analysis. The cohort-based hypothesis testing component of the analytic pipeline is validated through CoRAD, a visual analytic tool for performing case-controlled studies. Results: The results of both evaluations show improved task performance, and subjective satisfaction with the use of PhysioEx and CoRAD. Results from the evaluation of PhysioEx reveals insight about current limitations for supporting single subject studies in complex environments, and areas for future research in that space. Results from CoRAD also support the need for additional research to explore complex multi-dimensional patterns across multiple observations. From an information systems approach, the efficacy and feasibility of the TDVA framework is demonstrated by the instantiation and evaluation of PhysioEx and CoRAD. Conclusion: This research, introduces the TDVA framework and provides results to validate the deployment of online dynamic visual analytics in complex environments. The TDVA framework was instantiated in two case studies derived from an environment where dynamic and complex data streams were available. The first instantiation enabled the end-user to rapidly extract information from complex data streams to conduct in-depth analysis. The second allowed the end-user to test emerging patterns across multiple observations. To both ends, this thesis provides knowledge that can be used to improve the visual analytic pipeline in dynamic and complex environments

Assessing potential applications of multi-coil and multi-frequency electromagnetic induction sensors for agricultural soils in western Newfoundland

Ground-based electromagnetic induction (EMI) sensors play a significant role in shallow soil characterization in precision agriculture. Two different types of EMI sensors were used in this study: (i) a multi-coil and (ii) a multi-frequency. The potential applications of both EMI sensors have been assessed through two different studies at the Pynn’s Brook Research Station, Pasadena, western Newfoundland. One study was on the development of relationships between apparent electrical conductivity (ECₐ) and soil properties, using geostatistical and multivariate statistical approaches, and the second study investigated the depth sensitivity (DS) of multi-coil and multi-frequency EMI sensors using small buried targets of known properties in shallow soils. Soil properties, such as sand, silt, soil moisture content (SMC), cation exchange capacity (CEC), and pore water electrical conductivity (ECw), were identified as significantly influenced soil properties on ECₐ measurements. The multi-frequency EMI sensor is more reliable on ECₐ variability for wet soils than dry soils and it could explore deeper soil compared to the multi-coil sensor. The second study revealed that the multi-coil EMI sensor was a more accurate and suitable sensor to detect small metallic targets in the shallow soils than the multi-frequency EMI sensor. Finally, I concluded that the multi-coil EMI sensor is a more appropriate compared to the multi-frequency sensor, to investigate depth sensitivity (DS) analysis as well as the spatiotemporal variability of ECₐ as a proxy of soil properties in shallow (agricultural) soils in western Newfoundland

Granger Causal Chain Discovery for Sepsis-Associated Derangements via Multivariate Hawkes Processes

Modern health care systems are conducting continuous, automated surveillance of the electronic medical record (EMR) to identify adverse events with increasing frequency; however, many events such as sepsis do not have clearly elucidated prodromes (i.e., event chains) that can be used to identify and intercept the adverse event early in its course. Currently there does not exist a reliable framework for discovering or describing causal chains that precede adverse hospital events. Clinically relevant and interpretable results require a framework that can (1) infer temporal interactions across multiple patient features found in EMR data (e.g., labs, vital signs, etc.) and (2) can identify pattern(s) which precede and are specific to an impending adverse event (e.g., sepsis). In this work, we propose a linear multivariate Hawkes process model, coupled with $g(x) = x^+$ link function to allow potential inhibition effects, in order to recover a Granger Causal (GC) graph. We develop a two-phase gradient-based scheme to maximize a surrogate of likelihood to estimate the problem parameters. This two-phase algorithm is scalable and shown to be effective via our numerical simulation. It is subsequently extended to a data set of patients admitted to Grady hospital system in Atalanta, GA, where the fitted Granger Causal graph identifies several highly interpretable chains that precede sepsis

Mixed-Integer Projections for Automated Data Correction of EMRs Improve Predictions of Sepsis among Hospitalized Patients

Machine learning (ML) models are increasingly pivotal in automating clinical decisions. Yet, a glaring oversight in prior research has been the lack of proper processing of Electronic Medical Record (EMR) data in the clinical context for errors and outliers. Addressing this oversight, we introduce an innovative projections-based method that seamlessly integrates clinical expertise as domain constraints, generating important meta-data that can be used in ML workflows. In particular, by using high-dimensional mixed-integer programs that capture physiological and biological constraints on patient vitals and lab values, we can harness the power of mathematical "projections" for the EMR data to correct patient data. Consequently, we measure the distance of corrected data from the constraints defining a healthy range of patient data, resulting in a unique predictive metric we term as "trust-scores". These scores provide insight into the patient's health status and significantly boost the performance of ML classifiers in real-life clinical settings. We validate the impact of our framework in the context of early detection of sepsis using ML. We show an AUROC of 0.865 and a precision of 0.922, that surpasses conventional ML models without such projections

Comparison of Multi-Frequency and Multi-Coil Electromagnetic Induction (EMI) for Mapping Properties in Shallow Podsolic Soils

Electromagnetic induction (EMI) technique is an established method to measure the apparent electrical conductivity (ECa) of soil as a proxy for its physicochemical properties. Multi-frequency (MF) and multi-coil (MC) are the two types of commercially available EMI sensors. Although the working principles are similar, their theoretical and effective depth of investigation and their resolution capacity can vary. Given the recent emphasis on non-invasive mapping of soil properties, the selection of the most appropriate instrument is critical to support robust relationships between ECa and the targeted properties. In this study, we compared the performance of MC and MF sensors by their ability to define relationships between ECa (i.e., MF–ECa and MC–ECa) and shallow soil properties. Field experiments were conducted under wet and dry conditions on a silage-corn field in western Newfoundland, Canada. Relationships between temporally stable properties, such as texture and bulk density, and temporally variable properties, such as soil water content (SWC), cation exchange capacity (CEC) and pore water electrical conductivity (ECw) were investigated. Results revealed significant (p < 0.05) positive correlations of ECa to silt content, SWC and CEC for both sensors under dry conditions, higher correlated for MC–ECa. Under wet conditions, correlation of MF–ECa to temporally variable properties decreased, particularly to SWC, while the correlations to sand and silt increased. We concluded that the MF sensor is more sensitive to changes in SWC which influenced its ability to map temporally variable properties. The performance of the MC sensor was less affected by variable weather conditions, providing overall stronger correlations to both, temporally stable or variable soil properties for the tested Podzol and hence the more suitable sensor toward various precision agricultural practices

The development of a year five children’s engineering teaching module for hots / Kamaleswaran Jayarajah

Currently, there is a new push for early engineering to be included in the Science, Technology, Engineering and Mathematics (STEM) program. Activities integrated with early engineering foster Higher Order Thinking Skills (HOTS) among the children. Based on the previous research, it was clear that Malaysian children were lacking of HOTS. In line with that, this study has designed and developed Children’s Engineering Teaching Module (CETM) to help the science teachers to foster HOTS using the engineering elements among the Year Five primary school children. The theoretical foundation of this study was based on Gagne, Piaget and Vygotsky views. The process of developing CETM using the Isman model was carried out using the modified Delphi technique. The interviews with the experts were carried online. A total of 22 experts were involved in the CETM development. The email responses received from the experts were analysed and classified into four themes. CETM was designed and developed based on these four themes. CETM contains four activities whereby each of the activity represents a specific theme in the Year Five science syllabus. The CETM activities encourages children to use the engineering elements such as cyclic process and design thinking. Apart from that, the CETM activities also encourages children to produce a three-dimensional prototype as a solution for the given challenge. Once the CETM was developed and pilot-tested, the CETM activities were implemented in a primary school in Perak. The findings were based on the children’s sketches during the brainstorming session, written answers, verbal expressions, the ability in testing the prototypes and other interactions in the classroom. In addition, the children were interviewed and observed. Based on the analysis, it was found that children had the ability to justify and evaluate, offer different viewpoints and interacted intellectually either with their teacher or among themselves during the CETM activities. Children were also engaged in the reasoning process, creative thinking and participated in the intellectual discussions while designing the prototypes. In fact, they were able to create strategies which promoted ideas when they were designing the prototypes for CETM activities. Based on the reasoning skills test before and after the implementation of CETM, it was observed that there was improvement for some of the HOTS elements and reasoning skills among the children. Based on the test findings, it was found that the children’s ability to make decision, argue, reason deductively and mechanically has improved. One of the implications of this study is the usage of online interview to obtain the experts’ view from various countries. Apart from that, through the development of CETM, teachers were guided to produce activities which encompasses interdisciplinary fields such as technology, astronomy, ecology and sustainable engineering. Meanwhile children will also gain the experience of using the engineering and designing elements while learning science subject in the classroom