Incorporating comorbidities into latent treatment pattern mining for clinical pathways

Abstract

In healthcare organizational settings, the design of a clinical pathway (CP) is challenging since patients following a particular pathway may have not only one single first-diagnosis but also several typical comorbidities, and thus it requires different disciplines involved to put together their partial knowledge about the overall pathway. Although many data mining techniques have been proposed to discover latent treatment information for CP analysis and reconstruction from a large volume of clinical data, they are specific to extract nontrivial information about the therapy and treatment of the first-diagnosis. The influence of comorbidities on adopting essential treatments is crucial for a pathway but has seldom been explored. This study proposes to extract latent treatment patterns that characterize essential treatments for both first-diagnosis and typical comorbidities from the execution data of a pathway. In particular, we propose a generative statistical model to extract underlying treatment patterns, unveil the latent associations between diagnosis labels (including both first-diagnosis and comorbidities) and treatments, and compute the contribution of comorbidities in these patterns. The proposed model extends latent Dirichlet allocation with an additional layer for diagnosis modeling. It first generates a set of latent treatment patterns from diagnosis labels, followed by sampling treatments from each pattern. We verify the effectiveness of the proposed model on a real clinical dataset containing 12,120 patient traces, which pertain to the unstable angina CP. Three treatment patterns are discovered from data, indicating latent correlations between comorbidities and treatments in the pathway. In addition, a possible medical application in terms of treatment recommendation is provided to illustrate the potential of the proposed model. Experimental results indicate that our approach can discover not only meaningful latent treatment patterns exhibiting comorbidity focus, but also implicit changes of treatments of first-diagnosis due to the incorporation of typical comorbidities potentially.

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining

“…Each patient trace was regarded as a mixture of different treatment patterns. In addition, the team integrated time stamps [3], patient features [4], and comorbidities [5] into LDA to enhance the ability of summarizing patterns. However, treating a patient trace as a whole can hardly represent the feature of CP that the treatment process is consisted of several stages with different clinical goals.…”

“…For a patient, a doctor would choose one pattern for him/her according to the symptoms. To achieve this target, topic modeling technologies, such as latent Dirichlet allocation (LDA) [1] and its variants, are applied on the clinical data [2–5]. They model each patient trace as mixtures of multiple topics, while each topic is modeled as a multinomial distribution over clinical activities.…”

Sequential pattern mining on electronic medical records with handling time intervals and the efficacy of medicines

“…7. It is not possible to extract latent medical treatments by normal data mining according to [14]. Thus, we regarded items with the same probability of more than 50% of occurring the day before the surgery as one item.…”

A Knowledge Management System for the Variation in Regional Clinical Pathways of Traditional Chinese Medicine Based on Smart Cloud Services

“…The variation law of the TCM CPs could be discovered by mining the objective indices (e.g. gender, age, drug dosage, and nursing grade) of data sources like hospital information system (HIS) [13] and electronic medical records (EMR), laying the basis for early warning of the variation [14][15][16]. Nevertheless, there is little report on the knowledge management of the variation in the TCM CPs from the regional perspective.…”