Clustering Cardiovascular Risk Trajectories of Patients with Type 2 Diabetes Using Process Mining

[EN] Patients with type 2 diabetes have a higher chance of developing cardiovascular diseases and an increased odds of mortality. Reliability of randomized clinical trials is continuously judged due to selection, attrition and reporting bias. Moreover, cardiovascular risk is frequently assessed in cross-sectional studies instead of observing the evolution of risk in longitudinal cohorts. In order to correctly assess the course of cardiovascular riskinpatientswithtype 2 diabetes, weappliedprocessminingtechniquesbasedontheprinciples of evidence-based medicine. Using a validated formulation of the cardiovascular risk, process mining allowed to cluster frequent risk pathways and produced 3 major trajectories related to risk management: high risk, medium risk and low risk.This enables the extractionofmeaningful distributions, such as the gender of the patients per cluster in a human understandable manner, leading to more insights to improve themanagementofcardiovasculardiseasesintype2diabetes patients.This work was supported by European Commission Grant No 600914 (MOSAIC Project).Pebesma, J.; Martinez-Millana, A.; Sacchi, L.; Fernández Llatas, C.; De Cata, P.; Chiovato, L.; Bellazzi, R.... (2019). Clustering Cardiovascular Risk Trajectories of Patients with Type 2 Diabetes Using Process Mining. IEEE. 341-344. https://doi.org/10.1109/EMBC.2019.8856507S34134

Disease heterogeneity of adult diabetes based on routine clinical parameters at diagnosis: Results from the German/Austrian DPV registry.

AIMS To cluster adults with diabetes using parameters from real-world clinical care at manifestation. MATERIALS AND METHODS We applied hierarchical clustering using Ward's method to 56,869 adults documented in the Prospective Diabetes Follow-up Registry (DPV). Clustering variables included age, sex, BMI, HbA1c, diabetic ketoacidosis (DKA), components of the metabolic syndrome (hypertension/dyslipidemia/hyperuricemia), and beta-cell antibody status. Time until use of oral antidiabetic drugs (OAD), use of insulin, chronic kidney disease (CKD), cardiovascular disease (CVD), retinopathy, or neuropathy were assessed using Kaplan Meier analysis and Cox regression models. RESULTS We identified eight clusters: Four clusters comprised early diabetes onset (median age between 40 and 50 years), but differed with regard to BMI, HbA1c, DKA and antibody positivity. Two clusters included adults with diabetes onset in their early 60s who met target HbA1c, but differed in BMI and sex distribution. Two clusters were characterized by late diabetes onset (median age 69 and 77 years) and relatively low BMI, but differences in HbA1c. Earlier insulin use was observed in adults with high HbA1c, and earlier OAD use was observed in those with high BMI. Time until CKD or CVD was shorter in those with late onset, whereas retinopathy occurred earlier in adults with late onset and high HbA1c, and in adults with early onset, but high HbA1c and high percentage of antibody positivity. CONCLUSIONS Adult diabetes is heterogeneous beyond classical type 1/type 2 diabetes, based on easily available parameters in clinical practice using an automated clustering algorithm which allows both continuous and binary variables. This article is protected by copyright. All rights reserved

Predicting morbidity by local similarities in multi-scale patient trajectories

[EN] Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.This work was supported by the CrowdHealth project (COLLECTIVE WISDOM DRIVING PUBLIC HEALTH POLICIES (727560)) and the MTS4up project (DPI2016-80054-R).Carrasco-Ribelles, LA.; Pardo-Más, JR.; Tortajada, S.; Sáez Silvestre, C.; Valdivieso, B.; Garcia-Gomez, JM. (2021). Predicting morbidity by local similarities in multi-scale patient trajectories. Journal of Biomedical Informatics. 120:1-9. https://doi.org/10.1016/j.jbi.2021.103837S1912

Analyzing Patient Trajectories With Artificial Intelligence

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery

DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic