Considerations for online deviation detection in medical processes

Christov, Stefan C.; Avrunin, George S.; Clarke, Lori A.

doi:10.1109/sehc.2013.6602478

Cited by 3 publications

(3 citation statements)

References 10 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…allowing only the insertion or deletion of characters, as is done in Longest Common Subsequence method. 21 Edit Distance approaches to clustering sequences are popular in health and medical research applications such as understanding care pathways in the care of multiple sclerosis 9 and end-stage renal disease, 10 the analysis of life history data, 11 detecting deviations from standard processes in blood transfusion, 22 predicting outcomes for cancer patients whilst they remain in a hospital, 23 detecting scalping of health services 24 and identifying long-term patterns in schizophrenia symptoms. 25 Additionally, they are widely used in genomics.…”

Section: Clustering Variable-length Ordinal Sequences and Conventiomentioning

confidence: 99%

Clustering clinical and health care processes using a novel measure of dissimilarity for variable-length sequences of ordinal states

Johns

Hearne

Bernhardt

et al. 2020

Stat Methods Med Res

View full text Add to dashboard Cite

Clinical and health care processes are often summarised through sequences of ordinal data describing patient’s state over time. Identifying patterns in these sequences can provide valuable insights into patient progression trajectories for the purposes of clinical monitoring and quality assurance. However, both the variation in the length of each sequence and the ordinal nature of observable states present challenges to pattern identification. In this paper, we address these challenges by presenting a novel measure of dissimilarity for comparing two or more variable-length ordinal sequences that can be used in conjunction with conventional clustering methods to identify patterns in patient progression trajectories. We provide practical guidance on how this can be achieved, and demonstrate it in the context of identifying patterns in post-stoke recovery trajectories.

show abstract

Section: Clustering Variable-length Ordinal Sequences and Conventiomentioning

confidence: 99%

Clustering clinical and health care processes using a novel measure of dissimilarity for variable-length sequences of ordinal states

Johns

Hearne

Bernhardt

et al. 2020

Stat Methods Med Res

View full text Add to dashboard Cite

show abstract

“…The proof of triple (15) is equivalent to prove the following two triples: {inv ∧ i < n ∧ bd = z ∧ B}v := i; x := true{n − i < z + 1} (17) {inv ∧ i < n ∧ bd = z ∧ ¬B}skip {n − i < z + 1} (18) The proof of triple (17) is as follows.…”

Section: Appendix a Proof Of The Lemmasmentioning

confidence: 99%

“…In addition, based on organ-specific physiology, a system that integrates medical devices into semi-autonomous clusters in a network-failsafe manner has also been developed by Kang et al [14]. Christov et al [15] proposed an approach to detect whether the performed medical procedures have deviated from the recommended ways to perform the medical procedures, i.e., medical best practices. To formally verify safety properties of medical guideline models, Guo et al [16] presented an approach to transform statecharts to timed automata.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Design Verifiably Correct Model Patterns to Facilitate Modeling Medical Best Practice Guidelines With Statecharts

Guo¹,

Fu²,

Zhang³

et al. 2019

IEEE Internet Things J.

View full text Add to dashboard Cite

Improving patient care safety is an ultimate objective for medical cyber-physical systems. A recent study shows that the patients' death rate can be significantly reduced by computerizing medical best practice guidelines. To facilitate the development of computerized medical best practice guidelines, statecharts are often used as a modeling tool because of their high resemblances to disease and treatment models and their capabilities to provide rapid prototyping and simulation for clinical validations. However, some implementations of statecharts, such as Yakindu statecharts, are priority-based and have synchronous execution semantics which makes it difficult to model certain functionalities that are essential in modeling medical guidelines, such as two-way communications and configurable execution orders. Rather than introducing new statechart elements or changing the statechart implementation's underline semantics, we use existing basic statechart elements to design model patterns for the commonly occurring issues. In particular, we show the design of model patterns for two-way communications and configurable execution orders and formally prove the correctness of these model patterns. We further use a simplified airway laser surgery scenario as a case study to demonstrate how the developed model patterns address the two-way communication and configurable execution order issues and their impact on validation and verification of medical safety properties.

show abstract