Graphical modeling of binary data using the LASSO: a simulation study

Comorbidity network analysis using graphical models for electronic health records

Importance

The comorbidity network represents multiple diseases and their relationships in a graph. Understanding comorbidity networks among critical care unit (CCU) patients can help doctors diagnose patients faster, minimize missed diagnoses, and potentially decrease morbidity and mortality.

Objective

The main objective of this study was to identify the comorbidity network among CCU patients using a novel application of a machine learning method (graphical modeling method). The second objective was to compare the machine learning method with a traditional pairwise method in simulation.

Method

This cross-sectional study used CCU patients' data from Medical Information Mart for the Intensive Care-3 (MIMIC-3) dataset, an electronic health record (EHR) of patients with CCU hospitalizations within Beth Israel Deaconess Hospital from 2001 to 2012. A machine learning method (graphical modeling method) was applied to identify the comorbidity network of 654 diagnosis categories among 46,511 patients.

Results

Out of the 654 diagnosis categories, the graphical modeling method identified a comorbidity network of 2,806 associations in 510 diagnosis categories. Two medical professionals reviewed the comorbidity network and confirmed that the associations were consistent with current medical understanding. Moreover, the strongest association in our network was between "poisoning by psychotropic agents" and "accidental poisoning by tranquilizers" (logOR 8.16), and the most connected diagnosis was "disorders of fluid, electrolyte, and acid-base balance" (63 associated diagnosis categories). Our method outperformed traditional pairwise comorbidity network methods in simulation studies. Some strongest associations between diagnosis categories were also identified, for example, "diagnoses of mitral and aortic valve" and "other rheumatic heart disease" (logOR: 5.15). Furthermore, our method identified diagnosis categories that were connected with most other diagnosis categories, for example, "disorders of fluid, electrolyte, and acid-base balance" was associated with 63 other diagnosis categories. Additionally, using a data-driven approach, our method partitioned the diagnosis categories into 14 modularity classes.

Conclusion and relevance

Our graphical modeling method inferred a logical comorbidity network whose associations were consistent with current medical understanding and outperformed traditional network methods in simulation. Our comorbidity network method can potentially assist CCU doctors in diagnosing patients faster and minimizing missed diagnoses.

Describing Functioning in People Living With Spinal Cord Injury Across 22 Countries: A Graphical Modeling Approach

OBJECTIVE

To provide prevalence estimates for problems in functioning of community-dwelling persons with spinal cord injury (SCI) and to examine associations between various areas of functioning with the purpose of supporting countries in identifying targets for interventions.

DESIGN

Cross-sectional survey.

SETTING

Community, 22 countries including all World Health Organization regions.

PARTICIPANTS

Persons (N=12,591) with traumatic or nontraumatic SCI aged 18 years or older.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

We estimated the prevalence of problems in 53 areas of functioning from the Brief International Classification of Functioning, Disability and Health (ICF) core set for SCI, long-term context, or ICF rehabilitation set covering 4 domains: impairments in body functions, impairments in mental functions, independence in performing activities, and restrictions in participation. Associations between areas of functioning were identified and visualized using conditional independence graphs.

RESULTS

Participants had a median age of 52 years, 73% were male, and 63% had paraplegia. Feeling tired, bowel dysfunction, sexual functions, spasticity, pain, carrying out daily routine, doing housework, getting up off the floor from lying on the back, pushing open a heavy door, and standing unsupported had the highest prevalence of problems (>70%). Clustering of associations within the 4 functioning domains was found, with the highest numbers of associations within impairments in mental functions. For the whole International Spinal Cord Injury sample, areas with the highest numbers of associations were circulatory problems, transferring bed-wheelchair, and toileting, while for the World Health Organization European and Western Pacific regions, these were dressing upper body, transferring bed-wheelchair, handling stress, feeling downhearted and depressed, and feeling happy.

CONCLUSIONS

In each domain of functioning, high prevalence of problems and high connectivity of areas of functioning were identified. The understanding of problems and the identification of potential targets for intervention can inform decision makers at all levels of the health system aiming to improve the situation of people living with SCI.

Modelling of the ICF core sets for chronic ischemic heart disease using the LASSO model in Chinese patients

Background

This study aimed to examine the associations among the International Classification of Functioning, Disability, and Health (ICF) core sets relevant to chronic ischemic heart disease (CIHD) using the least absolute shrinkage and selection operator (LASSO) model based on the ICF core sets scale in Chinese patients.

Methods

This was a prospective study of 120 patients with CIHD selected from January 2013 to June 2014 at the Fada Institute of Forensic Medicine & Science (Beijing, China). Functioning was qualified using the ICF core sets checklist for CIHD (Chinese version). The variables of core set categories of the ICF assessment scale for CIHD were entered into the LASSO model for mining dependencies among those variables. Graphical modeling was applied using LASSO generalized linear models.

Results

“Muscle endurance functions”, “sensations associated with cardiovascular and respiratory functions”, “blood vessel functions”, and “heart functions” were the most injured in CIHD status. “Recreation and leisure” and “intimate relationships” were the most affected in CIHD status. “General social support services, systems, and policies” and “acquaintances, peers, colleagues, neighbors, and community members” were important for the outcome of functional status of the CIHD patient. “Economic self-sufficiency” and “family relationships” of the CIHD patient were not undermined in most cases.

Conclusions

Graphical modeling can be used to describe associations between different areas of functioning in CIHD patients. The results suggest that these associations could be used as basis to improve rehabilitation and provide a deeper understanding of functioning in Chinese CIHD patients.

Electronic supplementary material

The online version of this article (10.1186/s12955-018-0957-0) contains supplementary material, which is available to authorized users.

Graphical modelling: a tool for describing and understanding the functioning of people living with a health condition

Rehabilitation aims to optimize people's lived experience of health or functioning. A comprehensive understanding of people's functioning is thus fundamental for rehabilitation clinicians and scientists. Over the past ten years it has been shown that graphical modelling is a promising technique for modelling data on people's functioning. It can contribute to our understanding of the complex associations between domains of functioning and the identification of potential targets for rehabilitation interventions both at the level of the person and the environment. The objective of this methodological note is to demonstrate how graphical modelling can be used by rehabilitation clinicians and scientists in the description, understanding and influencing of people's functioning. The application of graphical modelling and the interpretation of results is illustrated using the Spinal Cord Injury Independence Measure - Self Report used in the Swiss Spinal Cord Injury Cohort Study. Finally, we discuss the potential of graphical modelling for the planning of studies that expand our understanding of functioning and for rehabilitation interventions.

Dynamic network model with continuous valued nodes for longitudinal brain morphometry

Longitudinal brain morphometry probes time-related brain morphometric patterns. We propose a method called dynamic network modeling with continuous valued nodes to generate a dynamic brain network from continuous valued longitudinal morphometric data. The mathematical framework of this method is based on state-space modeling. We use a bootstrap-enhanced least absolute shrinkage operator to solve the network-structure generation problem. In contrast to discrete dynamic Bayesian network modeling, the proposed method enables network generation directly from continuous valued high-dimensional short sequence data, being free from any discretization process. We applied the proposed method to a study of normal brain development.

Gaussian Graphical Models Identify Networks of Dietary Intake in a German Adult Population

BACKGROUND

Data-reduction methods such as principal component analysis are often used to derive dietary patterns. However, such methods do not assess how foods are consumed in relation to each other. Gaussian graphical models (GGMs) are a set of novel methods that can address this issue.

OBJECTIVE

We sought to apply GGMs to derive sex-specific dietary intake networks representing consumption patterns in a German adult population.

METHODS

Dietary intake data from 10,780 men and 16,340 women of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort were cross-sectionally analyzed to construct dietary intake networks. Food intake for each participant was estimated using a 148-item food-frequency questionnaire that captured the intake of 49 food groups. GGMs were applied to log-transformed intakes (grams per day) of 49 food groups to construct sex-specific food networks. Semiparametric Gaussian copula graphical models (SGCGMs) were used to confirm GGM results.

RESULTS

In men, GGMs identified 1 major dietary network that consisted of intakes of red meat, processed meat, cooked vegetables, sauces, potatoes, cabbage, poultry, legumes, mushrooms, soup, and whole-grain and refined breads. For women, a similar network was identified with the addition of fried potatoes. Other identified networks consisted of dairy products and sweet food groups. SGCGMs yielded results comparable to those of GGMs.

CONCLUSIONS

GGMs are a powerful exploratory method that can be used to construct dietary networks representing dietary intake patterns that reveal how foods are consumed in relation to each other. GGMs indicated an apparent major role of red meat intake in a consumption pattern in the studied population. In the future, identified networks might be transformed into pattern scores for investigating their associations with health outcomes.