2015
DOI: 10.1016/j.artmed.2015.09.001
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On local anomaly detection and analysis for clinical pathways

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Cited by 31 publications
(15 citation statements)
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“…In healthcare, medical errors [44], healthcare sensor faults [45], healthcare monitoring system [46], abnormal ECG signal [47], anomalous behavior signs [48] and clinical pathway [49] are examples of anomaly detection. The categories for anomalies are: (i) collective anomalies, a group of data points collectively facilitates the anomaly detection; (ii) contextual anomalies, it is context specific, normally in the form of time-series; and (iii) point anomalies, one instance is anomalous.…”
Section: Un-supervised Learningmentioning
confidence: 99%
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“…In healthcare, medical errors [44], healthcare sensor faults [45], healthcare monitoring system [46], abnormal ECG signal [47], anomalous behavior signs [48] and clinical pathway [49] are examples of anomaly detection. The categories for anomalies are: (i) collective anomalies, a group of data points collectively facilitates the anomaly detection; (ii) contextual anomalies, it is context specific, normally in the form of time-series; and (iii) point anomalies, one instance is anomalous.…”
Section: Un-supervised Learningmentioning
confidence: 99%
“…Three kinds of optimization algorithms, evolutionary [8][9][10][11][12][13][14][15][16][17], stochastic [18][19][20][21][22][23][24][25][26][27][28][29] and combinatorial optimization [30][31][32][33][34][35][36][37][38] will be addressed. For machine learning algorithms, the discussion is based on un-supervised learning [39][40][41][42][43][44][45][46][47][48][49], supervised learning and semi-supervised learning [71][72][73][74][75][76][77][78]…”
Section: Introductionmentioning
confidence: 99%
“…In term of process mining perspectives, sixteen studies only discussed the control-flow perspective [22], [25], [29], [33], [36], [37], [39], [41], [42], [44]- [50], fourteen studies discussed control-flow and performance perspectives [23], [24], [26], [30]- [32], [35], [38], [40], [51]- [55], and one study discussed all three perspectives [28]. There is one further study which discusses methods to ensure the provision high quality data for process mining [27] and this study proposed solutions to reducing errors during data-entry.…”
Section: Process Mining Perspectives Types and Toolsmentioning
confidence: 99%
“…The issues included the noise of the data, missing timestamp information, and data quality control. Technical limitations were identified in 13 papers [28], [23], [51], [46], [41], [27], [44], [45], [32], [33], [37], [48], [55] including limitations in computer processing power, memory usage, bias associated with the mining algorithm, and simplifying the control flow due to computational complexity. There were no studies which reported limitation of team composition, however, 14 studies reported that they had involved clinician(s) to help the team with analysis or verification of results [28], [23], [46], [41], [54], [30], [27], [32], [33], [48], [55], [49], [50], [42].…”
Section: Limitations and Future Workmentioning
confidence: 99%
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