Summarizing clinical pathways from event logs.

Clinical pathway analysis, as a pivotal issue in ensuring specialized, standardized, normalized and sophisticated therapy procedures, is receiving increasing attention in the field of medical informatics. Research in clinical pathway analysis has so far mostly focused on looking at aggregated data seen from an external perspective, and only provide very limited insight into the pathways. In some recent work, process mining techniques have been studied in discovering clinical pathway models from data. While it is interesting, discovered models may provide too much detail to give a comprehensive summary of the pathway. Moreover, the number of patterns discovered can be large. Alternatively, this article presents a new approach to build a concise and comprehensive summary that describes the entire structure of a clinical pathway, while revealing essential/critical medical behaviors in specific time intervals over the whole time period of the pathway. The presented approach summarizes a clinical pathway from the collected clinical event log, which regularly records all kinds of patient therapy and treatment activities in clinical workflow by various hospital information systems. The proposed approach formally defines the clinical pathway summarization problem as an optimization problem that can be solved in polynomial time by using a dynamic-programming algorithm. More specifically, given an input event log, the presented approach summarizes the pathway by segmenting the observed time period of the pathway into continuous and overlapping time intervals, and discovering frequent medical behavior patterns in each specific time interval from the log. The proposed approach is evaluated via real-world data-sets, which are extracted from Zhejiang Huzhou Central hospital of China with regard to four specific diseases, i.e., bronchial lung cancer, colon cancer, gastric cancer, and cerebral infarction, in two years (2007.08-2009.09). Although the medical behaviors contained in these logs are very diverse and heterogeneous, experimental results indicates that the presented approach is feasible to construct condensed clinical pathway summaries in polynomial time from the collected logs, and have a linear scalability against the increasing size of the logs. Experiments on real data-sets illustrate that the presented approach is efficient and discovers high-quality results: the observed time period of a clinical pathway is correctly segmented into a set of continuous and overlapping time intervals, in which essential/critical medical behaviors are well discovered from the event log to form the backbone of a clinical pathway. The experimental results indicate that the generated clinical pathway summary not only reveals the global structure of a pathway, but also provides a thorough understanding of the way in which actual medical behaviors are practiced in specific time intervals, which might be essential from the perspectives of clinical pathway analysis and improvement.

Huang Z ，Lu X ，Duan H ，Fan W ... -  《-》

On mining clinical pathway patterns from medical behaviors.

Clinical pathway analysis, as a pivotal issue in ensuring specialized, standardized, normalized and sophisticated therapy procedures, is receiving increasing attention in the field of medical informatics. Clinical pathway pattern mining is one of the most important components of clinical pathway analysis and aims to discover which medical behaviors are essential/critical for clinical pathways, and also where temporal orders of these medical behaviors are quantified with numerical bounds. Even though existing clinical pathway pattern mining techniques can tell us which medical behaviors are frequently performed and in which order, they seldom precisely provide quantified temporal order information of critical medical behaviors in clinical pathways. This study adopts process mining to analyze clinical pathways. The key contribution of the paper is to develop a new process mining approach to find a set of clinical pathway patterns given a specific clinical workflow log and minimum support threshold. The proposed approach not only discovers which critical medical behaviors are performed and in which order, but also provides comprehensive knowledge about quantified temporal orders of medical behaviors in clinical pathways. The proposed approach is evaluated via real-world data-sets, which are extracted from Zhejiang Huzhou Central hospital of China with regard to six specific diseases, i.e., bronchial lung cancer, gastric cancer, cerebral hemorrhage, breast cancer, infarction, and colon cancer, in two years (2007.08-2009.09). As compared to the general sequence pattern mining algorithm, the proposed approach consumes less processing time, generates quite a smaller number of clinical pathway patterns, and has a linear scalability in terms of execution time against the increasing size of data sets. The experimental results indicate the applicability of the proposed approach, based on which it is possible to discover clinical pathway patterns that can cover most frequent medical behaviors that are most regularly encountered in clinical practice. Therefore, it holds significant promise in research efforts related to the analysis of clinical pathways.

Huang Z ，Lu X ，Duan H 《-》

Mining the changes of medical behaviors for clinical pathways.

Huang Z ，Gan C ，Lu X ，Huan H ... -  《-》

Discovery of clinical pathway patterns from event logs using probabilistic topic models.

Discovery of clinical pathway (CP) patterns has experienced increased attention over the years due to its importance for revealing the structure, semantics and dynamics of CPs, and to its usefulness for providing clinicians with explicit knowledge which can be directly used to guide treatment activities of individual patients. Generally, discovery of CP patterns is a challenging task as treatment behaviors in CPs often have a large variability depending on factors such as time, location and patient individual. Based on the assumption that CP patterns can be derived from clinical event logs which usually record various treatment activities in CP executions, this study proposes a novel approach to CP pattern discovery by modeling CPs using mixtures of an extension to the Latent Dirichlet Allocation family that jointly models various treatment activities and their occurring time stamps in CPs. Clinical case studies are performed to evaluate the proposed approach via real-world data sets recording typical treatment behaviors in patient careflow. The obtained results demonstrate the suitability of the proposed approach for CP pattern discovery, and indicate the promise in research efforts related to CP analysis and optimization.

Huang Z ，Dong W ，Ji L ，Gan C ，Lu X ，Duan H ... -  《-》

Regarding: Rosenthal DI, Glatstein E. "We've Got a Treatment, but What's the Disease?" The Oncologist 1996;1.

Lunsford LD ，Flickinger JC ，Larson D 《-》