The automatic detection of patterns in people's movements

Bibliography: leaves 102-105

Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure

© 2013 IEEE. Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients

Discovering Predictive Event Sequences in Criminal Careers

In this work, we consider the problem of predicting criminal behavior, and propose a method for discovering predictive patterns in criminal histories. Quantitative criminal career analysis typically involves clustering individuals according to frequency of a particular event type over time, using cluster membership as a basis for comparison. We demonstrate the effectiveness of hazard pattern mining for the discovery of relationships between different types of events that may occur in criminal careers. Hazard pattern mining is an extension of event sequence mining, with the additional restriction that each event in the pattern is the first subsequent event of the specified type. This restriction facilitates application of established time based measures such as those used in survival analysis. We evaluate hazard patterns using a relative risk model and an accelerated failure time model. The results show that hazard patterns can reliably capture unexpected relationships between events of different types

Applied temporal Rule Mining to Time Series

Association rule mining from time series has attracted considerable interest over the last years and various methods have been developed. Temporal rules between discovered episodes provide useful knowledge for the dynamics of the problem domain and the underlying data generating process. However, temporal rule mining has received little attention over the last years. In addition, the proposed methods suffer from two significant drawbacks. First the rules they produce are not robust enough with respect to noise. Second the proposed methods are highly dependent on the choice of the parameters since small perturbations on the parameters lead to significantly different results. In this paper we propose a frame-work to derive temporal rules from time series. Our approach is based on episode rule mining that discovers temporal rules from time series in the frequency domain using the discrete cosine transform. The rules are then translated to temporal relations between time series patterns of arbitrary length. Experimental results of the proposed framework are presented in the relevant section

Information extraction and data mining from Chinese financial news.

Ng Anny.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 139-142).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Definition --- p.2Chapter 1.2 --- Thesis Organization --- p.3Chapter 2 --- Chinese Text Summarization Using Genetic Algorithm --- p.4Chapter 2.1 --- Introduction --- p.4Chapter 2.2 --- Related Work --- p.6Chapter 2.3 --- Genetic Algorithm Approach --- p.10Chapter 2.3.1 --- Fitness Function --- p.11Chapter 2.3.2 --- Genetic operators --- p.14Chapter 2.4 --- Implementation Details --- p.15Chapter 2.5 --- Experimental results --- p.19Chapter 2.6 --- Limitations and Future Work --- p.24Chapter 2.7 --- Conclusion --- p.26Chapter 3 --- Event Extraction from Chinese Financial News --- p.27Chapter 3.1 --- Introduction --- p.28Chapter 3.2 --- Method --- p.29Chapter 3.2.1 --- Data Set Preparation --- p.29Chapter 3.2.2 --- Positive Word --- p.30Chapter 3.2.3 --- Negative Word --- p.31Chapter 3.2.4 --- Window --- p.31Chapter 3.2.5 --- Event Extraction --- p.32Chapter 3.3 --- System Overview --- p.33Chapter 3.4 --- Implementation --- p.33Chapter 3.4.1 --- Event Type and Positive Word --- p.34Chapter 3.4.2 --- Company Name --- p.34Chapter 3.4.3 --- Negative Word --- p.36Chapter 3.4.4 --- Event Extraction --- p.37Chapter 3.5 --- Stock Database --- p.38Chapter 3.5.1 --- Stock Movements --- p.39Chapter 3.5.2 --- Implementation --- p.39Chapter 3.5.3 --- Stock Database Transformation --- p.39Chapter 3.6 --- Performance Evaluation --- p.40Chapter 3.6.1 --- Performance measures --- p.40Chapter 3.6.2 --- Evaluation --- p.41Chapter 3.7 --- Conclusion --- p.45Chapter 4 --- Mining Frequent Episodes --- p.46Chapter 4.1 --- Introduction --- p.46Chapter 4.1.1 --- Definitions --- p.48Chapter 4.2 --- Related Work --- p.50Chapter 4.3 --- Double-Part Event Tree for the database --- p.56Chapter 4.3.1 --- Complexity of tree construction --- p.62Chapter 4.4 --- Mining Frequent Episodes with the DE-tree --- p.63Chapter 4.4.1 --- Conditional Event Trees --- p.66Chapter 4.4.2 --- Single Path Conditional Event Tree --- p.67Chapter 4.4.3 --- Complexity of Mining Frequent Episodes with DE-Tree --- p.67Chapter 4.4.4 --- An Example --- p.68Chapter 4.4.5 --- Completeness of finding frequent episodes --- p.71Chapter 4.5 --- Implementation of DE-Tree --- p.71Chapter 4.6 --- Method 2: Node-List Event Tree --- p.76Chapter 4.6.1 --- Tree construction --- p.79Chapter 4.6.2 --- Order of Position Bits --- p.83Chapter 4.7 --- Implementation of NE-tree construction --- p.84Chapter 4.7.1 --- Complexity of NE-Tree Construction --- p.86Chapter 4.8 --- Mining Frequent Episodes with NE-tree --- p.87Chapter 4.8.1 --- Conditional NE-Tree --- p.87Chapter 4.8.2 --- Single Path Conditional NE-Tree --- p.88Chapter 4.8.3 --- Complexity of Mining Frequent Episodes with NE-Tree --- p.89Chapter 4.8.4 --- An Example --- p.89Chapter 4.9 --- Performance evaluation --- p.91Chapter 4.9.1 --- Synthetic data --- p.91Chapter 4.9.2 --- Real data --- p.99Chapter 4.10 --- Conclusion --- p.103Chapter 5 --- Mining N-most Interesting Episodes --- p.104Chapter 5.1 --- Introduction --- p.105Chapter 5.2 --- Method --- p.106Chapter 5.2.1 --- Threshold Improvement --- p.108Chapter 5.2.2 --- Pseudocode --- p.112Chapter 5.3 --- Experimental Results --- p.112Chapter 5.3.1 --- Synthetic Data --- p.113Chapter 5.3.2 --- Real Data --- p.119Chapter 5.4 --- Conclusion --- p.121Chapter 6 --- Mining Frequent Episodes with Event Constraints --- p.122Chapter 6.1 --- Introduction --- p.122Chapter 6.2 --- Method --- p.123Chapter 6.3 --- Experimental Results --- p.125Chapter 6.3.1 --- Synthetic Data --- p.126Chapter 6.3.2 --- Real Data --- p.129Chapter 6.4 --- Conclusion --- p.131Chapter 7 --- Conclusion --- p.133Chapter A --- Test Cases --- p.135Chapter A.1 --- Text 1 --- p.135Chapter A.2 --- Text 2 --- p.137Bibliography --- p.13