Daniel B. Neill

Rapid detection of significant spatial clusters

Given an N x N grid of squares, where each square has a count cij and an underlying population pij, our goal is to find the rectangular region with the highest density, and to calculate its significance by randomization. An arbitrary density function D, dependent on a regions total count C and total population P, can be used. For example, if each count represents the number of disease cases occurring in that square, we can use Kulldorffs spatial scan statistic DK to find the most significant spatial disease cluster. A naive approach to finding the maximum density region requires O(N4) time, and is generally computationally infeasible. We present a multiresolution algorithm which partitions the grid into overlapping regions using a novel overlap-kd tree data structure, bounds the maximum score of subregions contained in each region, and prunes regions which cannot contain the maximum density region. For sufficiently dense regions, this method finds the maximum density region in O((N log N)2) time, in practice resulting in significant (20-2000x) speedups on both real and simulated datasets.

Guggulsterone enhances head and neck cancer therapies via inhibition of signal transducer and activator of transcription-3

Treatment of human head and neck squamous cell carcinoma (HNSCC) cell lines with guggulsterone, a widely available, well-tolerated nutraceutical, demonstrated dose-dependent decreases in cell viability with EC(50)s ranging from 5 to 8 microM. Guggulsterone induced apoptosis and cell cycle arrest, inhibited invasion and enhanced the efficacy of erlotinib, cetuximab and cisplatin in HNSCC cell lines. Guggulsterone induced decreased expression of both phosphotyrosine and total signal transducer and activator of transcription (STAT)-3, which contributed to guggulsterones growth inhibitory effect. Hypoxia-inducible factor (HIF)-1alpha was also decreased in response to guggulsterone treatment. In a xenograft model of HNSCC, guggulsterone treatment resulted in increased apoptosis and decreased expression of STAT3. In vivo treatment with a guggulsterone-containing natural product, Guggulipid, resulted in decreased rates of tumor growth and enhancement of cetuximabs activity. Our results suggest that guggulsterone-mediated inhibition of STAT3 and HIF-1alpha provide a biologic rationale for further clinical investigation of this compound in the treatment of HNSCC.

A multivariate Bayesian scan statistic for early event detection and characterization

We present the multivariate Bayesian scan statistic (MBSS), a general framework for event detection and characterization in multivariate spatial time series data. MBSS integrates prior information and observations from multiple data streams in a principled Bayesian framework, computing the posterior probability of each type of event in each space-time region. MBSS learns a multivariate Gamma-Poisson model from historical data, and models the effects of each event type on each stream using expert knowledge or labeled training examples. We evaluate MBSS on various disease surveillance tasks, detecting and characterizing outbreaks injected into three streams of Pennsylvania medication sales data. We demonstrate that MBSS can be used both as a “general” event detector, with high detection power across a variety of event types, and a “specific” detector that incorporates prior knowledge of an event’s effects to achieve much higher detection power. MBSS has many other advantages over previous event detection approaches, including faster computation and easy interpretation and visualization of results, and allows faster and more accurate event detection by integrating information from the multiple streams. Most importantly, MBSS can model and differentiate between multiple event types, thus distinguishing between events requiring urgent responses and other, less relevant patterns in the data.

Honokiol inhibits epidermal growth factor receptor signaling and enhances the antitumor effects of epidermal growth factor receptor inhibitors.

Purpose: This study aimed to investigate the utility of honokiol, a naturally occurring compound, in the treatment of head and neck squamous cell carcinoma (HNSCC) as well as its ability to target the epidermal growth factor receptor (EGFR), a critical therapeutic target in HNSCC, and to enhance the effects of other EGFR-targeting therapies. Experimental Design: Human HNSCC cell lines and the xenograft animal model of HNSCC were used to test the effects of honokiol treatment. Results: Honokiol was found to inhibit growth in human HNSCC cell lines, with 50% effective concentration (EC50) values ranging from 3.3 to 7.4 μmol/L, and to induce apoptosis, as shown through Annexin V staining. These effects were associated with inhibition of EGFR signaling, including downstream inhibition of mitogen-activated protein kinase, Akt, and signal transducer and activator of transcription 3 (STAT3), and expression of STAT3 target genes, Bcl-XL and cyclin D1. Furthermore, honokiol enhanced the growth inhibitory and anti-invasion activity of the EGFR-targeting agent erlotinib. Although HNSCC xenograft models did not show significant inhibition of in vivo tumor growth with honokiol treatment alone, the combination of honokiol plus cetuximab, a Food and Drug Administration–approved EGFR inhibitor for this malignancy, significantly enhanced growth inhibition. Finally, HNSCC cells rendered resistant to erlotinib retained sensitivity to the growth inhibitory effects of honokiol. Conclusions: These results suggest that honokiol may be an effective therapeutic agent in HNSCC, in which it can augment the effects of EGFR inhibitors and overcome drug resistance. Clin Cancer Res; 16(9); 2571–9. ©2010 AACR.

Inhibition of EGFR-STAT3 Signaling with Erlotinib Prevents Carcinogenesis in a Chemically-Induced Mouse Model of Oral Squamous Cell Carcinoma

Chemoprevention of head and neck squamous cell carcinoma (HNSCC), a disease associated with high mortality rates and frequent occurrence of second primary tumor (SPT), is an important clinical goal. The epidermal growth factor receptor (EGFR)-signal transducer and activator of transcription (STAT)-3 signaling pathway is known to play a key role in HNSCC growth, survival, and prognosis, thereby serving as a potential therapeutic target in the treatment of HNSCC. In the current study, the 4-nitroquinoline-1-oxide (4-NQO)–induced murine model of oral carcinogenesis was utilized to investigate the chemopreventive activities of compounds that target the EGFR-STAT3 signaling pathway. This model mimics the process of oral carcinogenesis in humans. The drugs under investigation included erlotinib, a small molecule inhibitor of the EGFR, and guggulipid, the extract of an Ayurvedic medicinal plant, which contains guggulsterone, a compound known to inhibit STAT3. Dietary administration of guggulipid failed to confer protection against oral carcinogenesis. On the other hand, the mice placed on erlotinib-supplemented diet exhibited a 69% decrease (P < 0.001) in incidence of preneoplastic and neoplastic lesions compared with mice on the control diet. Immunostaining of dysplastic lesions demonstrated modest decreases in STAT3 levels, with both drug treatments, that were not statistically significant. The results of the present study provide the basis for exploring the efficacy of erlotinib for prevention of HNSCC in a clinical setting. Cancer Prev Res; 4(2); 230–7. ©2010 AACR.

An empirical comparison of spatial scan statistics for outbreak detection

BackgroundThe spatial scan statistic is a widely used statistical method for the automatic detection of disease clusters from syndromic data. Recent work in the disease surveillance community has proposed many variants of Kulldorffs original spatial scan statistic, including expectation-based Poisson and Gaussian statistics, and incorporates a variety of time series analysis methods to obtain expected counts. We evaluate the detection performance of twelve variants of spatial scan, using synthetic outbreaks injected into four real-world public health datasets.ResultsThe relative performance of methods varies substantially depending on the size of the injected outbreak, the average daily count of the background data, and whether seasonal and day-of-week trends are present. The expectation-based Poisson (EBP) method achieves high performance across a wide range of datasets and outbreak sizes, making it useful in typical detection scenarios where the outbreak characteristics are not known. Kulldorffs statistic outperforms EBP for small outbreaks in datasets with high average daily counts, but has extremely poor detection power for outbreaks affecting more than of the monitored locations. Randomization testing did not improve detection power for the four datasets considered, is computationally expensive, and can lead to high false positive rates.ConclusionOur results suggest four main conclusions. First, spatial scan methods should be evaluated for a variety of different datasets and outbreak characteristics, since focusing only on a single scenario may give a misleading picture of which methods perform best. Second, we recommend the use of the expectation-based Poisson statistic rather than the traditional Kulldorff statistic when large outbreaks are of potential interest, or when average daily counts are low. Third, adjusting for seasonal and day-of-week trends can significantly improve performance in datasets where these trends are present. Finally, we recommend discontinuing the use of randomization testing in the spatial scan framework when sufficient historical data is available for empirical calibration of likelihood ratio scores.

Detection of Patterns in Water Distribution Pipe Breakage Using Spatial Scan Statistics for Point Events in a Physical Network

Infrastructure systems of many U.S. cities are in poor condition, with many assets reaching the end of their service life and requiring significant capital investments. One primary requirement to optimize the allocation of investments in such systems is an effective assessment of the physical condition of assets. This paper addresses the physical condition assessment of drinking water distribution systems by analyzing pipe breakage data as the main source of evidence about the current physical condition of water distribution pipes over space. From this spatial perspective, the primary questions are whether data sets present unexpected clustering of pipe breaks, and where those break clusters are located if they do exist. This paper presents a novel approach that aims to detect and locate clusters of break points in a water distribution network. The proposed approach extends existing spatial scan statistic approaches, which are commonly used for detection of disease outbreaks in a two-dimensional spatial framework, to data collected from networked infrastructure systems. This proposed approach is described and tested in a data set that consists of 491 breaks that occurred over six years in a 160-mi water distribution network. The results presented in this paper indicate that the adapted spatial scan statistic approach applied to points in physical networks is able to detect clusters of noncompact shapes, and that these clusters present significantly higher than expected breakage rates even after accounting for pipe age and diameter. Several possible hypotheses are explored for potential causes of these clusters.

Automatic detection of omissions in medication lists

OBJECTIVE Evidence suggests that the medication lists of patients are often incomplete and could negatively affect patient outcomes. In this article, the authors propose the application of collaborative filtering methods to the medication reconciliation task. Given a current medication list for a patient, the authors employ collaborative filtering approaches to predict drugs the patient could be taking but are missing from their observed list. DESIGN The collaborative filtering approach presented in this paper emerges from the insight that an omission in a medication list is analogous to an item a consumer might purchase from a product list. Online retailers use collaborative filtering to recommend relevant products using retrospective purchase data. In this article, the authors argue that patient information in electronic medical records, combined with artificial intelligence methods, can enhance medication reconciliation. The authors formulate the detection of omissions in medication lists as a collaborative filtering problem. Detection of omissions is accomplished using several machine-learning approaches. The effectiveness of these approaches is evaluated using medication data from three long-term care centers. The authors also propose several decision-theoretic extensions to the methodology for incorporating medical knowledge into recommendations. RESULTS Results show that collaborative filtering identifies the missing drug in the top-10 list about 40-50% of the time and the therapeutic class of the missing drug 50%-65% of the time at the three clinics in this study. CONCLUSION Results suggest that collaborative filtering can be a valuable tool for reconciling medication lists, complementing currently recommended process-driven approaches. However, a one-size-fits-all approach is not optimal, and consideration should be given to context (eg, types of patients and drug regimens) and consequence (eg, the impact of omission on outcomes).

Scalable Detection of Anomalous Patterns With Connectivity Constraints

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and exactly identify the most anomalous (highest-scoring) connected subgraph. Kulldorff’s spatial scan, which searches over circles consisting of a center location and its k − 1 nearest neighbors, has been extended to include connectivity constraints by FlexScan. However, FlexScan performs an exhaustive search over connected subsets and is computationally infeasible for k > 30. Alternatively, the upper level set (ULS) scan scales well to large graphs but is not guaranteed to find the highest-scoring subset. We demonstrate that GraphScan is able to scale to graphs an order of magnitude larger than FlexScan, while guaranteeing that the highest-scoring subgraph will be identified. We evaluate GraphScan, Kulldorff’s spatial scan (searching over circles) and ULS in two different settings of public health surveillance. The first examines detection power using simulated disease outbreaks injected into real-world Emergency Department data. GraphScan improved detection power by identifying connected, irregularly shaped spatial clusters while requiring less than 4.3 sec of computation time per day of data. The second scenario uses contaminant plumes spreading through a water distribution system to evaluate the spatial accuracy of the methods. GraphScan improved spatial accuracy using data generated from noisy, binary sensors in the network while requiring less than 0.22 sec of computation time per hour of data.

Dynamic Pattern Detection with Temporal Consistency and Connectivity Constraints

We explore scalable and accurate dynamic pattern detection methods in graph-based data sets. We apply our proposed Dynamic Subset Scan method to the task of detecting, tracking, and source-tracing contaminant plumes spreading through a water distribution system equipped with noisy, binary sensors. While static patterns affect the same subset of data over a period of time, dynamic patterns may affect different subsets of the data at each time step. These dynamic patterns require a new approach to define and optimize penalized likelihood ratio statistics in the subset scan framework, as well as new computational techniques that scale to large, real-world networks. To address the first concern, we develop new subset scan methods that allow the detected subset of nodes to change over time, while incorporating temporal consistency constraints to reward patterns that do not dramatically change between adjacent time steps. Second, our Additive Graph Scan algorithm allows our novel scan statistic to process small graphs (500 nodes) in 4.1 seconds on average while maintaining an approximation ratio over 99% compared to an exact optimization method, and to scale to large graphs with over 12,000 nodes in 30 minutes on average. Evaluation results across multiple detection, tracking, and source-tracing tasks demonstrate substantial performance gains achieved by the Dynamic Subset Scan approach.