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A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications.
This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3step modeling approach:
The MIPS framework was developed using bipartite networks to identify patient subgroups based on frequently cooccurring highrisk comorbidities, multinomial logistic regression to classify patients into subgroups, and hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership compared with standard logistic regression without subgroup membership. The MIPS framework was evaluated for 3 hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) (COPD: n=29,016; CHF: n=51,550; THA/TKA: n=16,498). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge. Controls were defined as patients not readmitted within 90 days of discharge, matched by age, sex, race, and Medicaid eligibility.
In each condition, the visual analytical model identified patient subgroups that were statistically significant (
Although the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra and intercluster associations. The high accuracy of the classification models reflects the strong separation of patient subgroups, despite the size and density of the data sets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors of hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission, and beyond.
A wide range of studies [
However, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications. To bridge this gap, we developed and evaluated a novel analytical framework called modeling and interpreting patient subgroups (MIPS) using a 3step modeling approach: (1) identification of patient subgroups, their frequently cooccurring characteristics, and their risk of adverse outcomes; (2) classification of a new patient into one or more subgroups; and (3) prediction of an adverse outcome for a new patient informed by subgroup membership. We evaluated MIPS on 3 data sets related to hospital readmission, which helped pinpoint the strengths and limitations of MIPS. Furthermore, the results provided implications for improving the interpretability of patient subgroups in large and dense data sets, and for the design of clinical decision support systems to prevent adverse outcomes such as hospital readmissions.
Patients have been divided into subgroups using (1) investigatorselected variables such as race for developing hierarchical regression models [
Several studies have used a wide range of computational methods to identify patient subgroups, each with critical tradeoffs. Some studies have used
More recently, bipartite network analysis [
However, although several studies [
An estimated 1 in 5 elderly patients (more than 2.3 million Americans) is readmitted to a hospital within 30 days of discharge [
To address this epidemic of hospital readmission, CMS sponsored the development of models to predict the patientspecific risk of readmission in specific index conditions such as chronic obstructive pulmonary disease (COPD) [
Inputs and outputs for the 3step modeling in MIPS consisting of the visual analytical model, classification model, and prediction model. MIPS: Modeling and Interpreting Patient Subgroups.
We analyzed patients hospitalized for COPD, CHF, or THA/TKA. We selected these 3 index conditions because (1) hospitalizations for each of these conditions are highly prevalent in older adults [
Data for these 3 index conditions were extracted from the Medicare insurance claims data set. In 2019, Medicare provided health insurance to approximately 64.4 million Americans, of whom 55.5 million were older Americans (≥65 years) [
For each index condition, we used the same inclusion and exclusion criteria that were used to develop the CMS models but with the most recent years (20132014) provided by Medicare when we started the project. We extracted all patients who were admitted to an acute care hospital between July 2013 and August 2014 with a principal diagnosis of the index condition, were aged ≥66 years, and were enrolled in both Medicare parts A and B feeforservice plans 6 months before admission. Furthermore, we excluded patients who were transferred from other facilities, died during hospitalization, or transferred to another acute care hospital. Similar to the CMS models, we selected the first admission for patients with multiple admissions during the study period, and we did not use data from Medicare Part D (related to prescription medications).
The independent variables consisted of comorbidities and patient demographics (age, sex, and race). Comorbidities common in older adults were derived from 3 established comorbidity indices: Charlson Comorbidity Index [
The goal of visual analytical modeling was to identify and interpret biclusters of readmitted patients (cases), consisting of patient subgroups and their most frequently cooccurring comorbidities. The data used to build the visual analytical model in each index condition consisted of randomly dividing 100% of the cases into training (50%) and replication (50%) data sets (we use the term
The goal of classification modeling was to classify all cases and controls from the entire Medicare data set into the biclusters identified from the visual analytical model. The resulting bicluster membership for all cases and controls was designed to (1) develop the predictive modeling described in the next section and (2) measure the risk of each subgroup to enable clinical interpretation of the patient subgroups. The training data set in each condition consisted of a random sample of 75% cases with their subgroup membership (output of the visual analytical modeling) and an internal validation data set consisting of randomly selected 25% of the cases (with subgroup membership used to validate the model). These data were used to develop and use classification models for each index condition using the following steps (
The goal of prediction modeling was to predict the risk of readmission for a patient, taking into consideration subgroup membership. The data used to build the prediction models consisted 100% of cases and 100% of controls, with subgroup membership generated from the classification modeling. These data were randomly spilt into training (75%) and internal validation (25%) data sets. These data were used to train, internally validate, and compare the prediction models in each index condition using the following steps (
Medicare data were analyzed using a CMS datause agreement (CMS DUA RSCH201751404) and approved by the University of Texas Medical Branch Institutional Review Board (160361).
Training and replication/validation data sets used to develop the three models in each of the 3 index conditions.
Model  Training  Replication/validation  Total  



Chronic obstructive pulmonary disease (COPD)  14,508/14,508  14,508/14,508  29,016/29,016 

Congestive heart failure (CHF)  25,775/25,775  25,775/25,775  51,550/51,550 

Total hip arthroplasty/total knee arthroplasty (THA/TKA)  8249/8249  8249/8249  16,498/16,948 



COPD  10,842  3615  14,457 

CHF  19,254  6418  25,672 

THA/TKA  5257  1753  7010 



COPD  21,692/117,839  7334/39,176  29,026/157,015 

CHF  38,728/183,093  12,845/61,095  51,573/244,188 

THA/TKA  12,376/255,203  41,44/85,049  16,520/340,252 
^{a}The visual analytical models used 1:1 matched controls for the feature selection, and used only cases for the bipartite networks to analyze heterogeneity in readmission. The numbers shown for the visual analytical models are before removing patients with no comorbidities. The resulting casesonly data sets were used for the classification modelling as shown.
Visual analytical modeling of readmitted patients in all 3 index conditions produced statistically and clinically significant patient subgroups and their most frequently cooccurring comorbidities, which were significantly replicated. We report the results for each index condition.
The inclusion and exclusion selection criteria (
As shown in
The pulmonologist inspected the visualization and noted that the readmission risk of the patient subgroups had a wide range (12.7%19.6%) with clinical (face) validity. Furthermore, the cooccurrence of comorbidities in each patient subgroup was clinically meaningful with interpretations for each subgroup. Subgroup1 had a low disease burden, with uncomplicated hypertension leading to the lowest risk (12.7%). This subgroup represented patients with early organ dysfunction and would benefit from using checklists such as regular monitoring of blood pressure in predischarge protocols to reduce the risk of readmission. Subgroup3 had mainly psychosocial comorbidities, which could lead to aspiration precipitating pneumonia, leading to an increased risk for readmission (15.9%). This subgroup would benefit from early consultation with specialists (eg, psychiatrists, therapists, neurologists, and geriatricians) who have expertise in psychosocial comorbidities, with a focus on the early identification of aspiration risks and precautions. Subgroup2 had diabetes with complications, renal failure, and heart failure and therefore had higher disease burden, leading to an increased risk of readmission (17.8%) compared with Subgroup1. This subgroup had metabolic abnormalities with greater endorgan dysfunction and would therefore benefit from case management by advanced practice providers (eg, nurse practitioners) with rigorous adherence to established guidelines to reduce the risk of readmission. Subgroup4 had diseases with endorgan damage, including gastrointestinal disorders, and therefore had the highest disease burden and risk for readmission (19.6%). This subgroup would also benefit from case management with rigorous adherence to established guidelines to reduce the risk of readmission. Furthermore, as patients in this subgroup typically experience complications that could impair their ability to make medical decisions, they should be provided with early consultation with a palliative care team to ensure that care interventions align with patients’ preferences and values.
The chronic obstructive pulmonary disease (COPD) visual analytical model showing 4 biclusters consisting of patient subgroups and their most frequently cooccurring comorbidities (whose labels are ranked by their univariable odds ratios, shown within parentheses) and their risk of readmission (shown in blue text). GI: Gastrointestinal disorders; HD: Heart disease; MV: History of mechanical ventilation.
The inclusion and exclusion selection criteria (
The geriatrician inspected the visualization and noted that the readmission risk of the patient subgroups, ranging from 15.1% to 19.9%, was wide, with clinical (face) validity. Furthermore, the cooccurrence of comorbidities in each patient subgroup was clinically significant. Subgroup1 had chronic but stable conditions and therefore had the lowest risk for readmission (15.1%). Subgroup3 had mainly psychosocial comorbidities but was not as clinically unstable or fragile compared with Subgroup2 and Subgroup4, and therefore had medium risk (16.6%). Subgroup2 had severe chronic conditions, making them clinically fragile (with potential benefits from early palliative and hospice care referrals), and were therefore at high risk for readmission if nonpalliative approaches were used (19.9%). Subgroup4 had severe acute conditions that were also clinically unstable, associated with substantial disability and care debility and therefore at high risk for readmission and recurrent intensive care unit use (19.9%).
The congestive heart failure (CHF) visual analytical model showing 4 biclusters consisting of patient subgroups and their most frequently cooccurring comorbidities (whose labels are ranked by their univariable odds ratios, shown within parentheses) and their risk of readmission (shown in blue text). CABG: History of coronary artery bypass graft surgery; COPD: Chronic obstructive pulmonary disease; GI: Gastrointestinal disorders; HD: Heart disease.
The inclusion and exclusion selection criteria (
As shown in
The geriatrician inspected the network and noted that patients with total knee arthroplasty, in general, were healthier than patients with total hip arthroplasty. Therefore, the network was difficult to interpret when the 2 index conditions were merged together. Although our analysis was constrained because we used the conditions defined by CMS, these results nonetheless suggest that the interpretations did not suffer from a
The total hip arthroplasty/total knee arthroplasty (THA/TKA) visual analytical model showing 4 biclusters consisting of patient subgroups and their most frequently cooccurring comorbidities (whose labels are ranked by their univariable odds ratios, shown within parentheses) and their risk for readmission (shown in blue text). CHF: Congestive heart failure; COPD: Chronic obstructive pulmonary disease; OB: Obesity.
The classification model used multinomial logistic regression for each index condition (
Internal validation results showing the percentage of chronic obstructive pulmonary disease (COPD) congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) patients correctlyassigned to a subgroup by the classification models in each condition.
Models  Quantiles  Summary, mean (SD; range)  

Q 0.025  Q 0.25  Q 0.50  Q 0.75  Q 0.975 





Training (n=10842)  99.90  100.00  100.00  100.00  100.00  100 (0.02; 99.7100)  

Testing (n=3615)  99.30  99.40  99.60  99.60  99.80  99.6 (0.15; 99.1100)  



Training (n=19254)  99.40  99.50  99.60  99.60  99.80  99.57 (0.11; 9999.9)  

Testing (n=6418)  99.00  99.30  99.30  99.40  99.60  99.34 (0.15; 98.799.7)  



Training (n=5257)  100.00  100.00  100.00  100.00  100.00  100 (0; 100100)  

Testing (n=1753)  99.70  99.80  99.90  99.90  100.00  99.86 (0.09; 99.4100) 
The model correctly predicted subgroup membership for 99.9% (14,443/14,457) of the cases in the full data set. Furthermore
The model correctly predicted the subgroup membership for 99.2% (25,476/25,672) of the cases in the full data set. Furthermore
The model correctly predicted subgroup membership in 100% (7010/7010) of the cases in the full data set. Furthermore
The classification model was used to classify 100% of cases and 100% of controls for use in the prediction model (described in the next section). Furthermore, the proportion of cases and controls classified into each subgroup was used to calculate the risk of readmission for the respective subgroup (
For each of the 3 index conditions, we developed 2 binary logistic regression models to predict readmission, with comorbidities in addition to sex, age, and race: (1) Standard Model representing all patients without subgroup membership, similar to the CMS models and (2) Hierarchical Model with an additional variable that adjusted for subgroup membership.
The inclusion and exclusion criteria (
As shown in
Predictive accuracy of the Standard Model compared with the Hierarchical Model in chronic obstructive pulmonary disease (COPD), as measured by the Cstatistic. The Cstatistic for the Centers for Medicare & Medicaid Services Standard Model is shown as a dotted line. (B) Predictive accuracy of the Standard Model when applied separately to patients classified to each subgroup. Subgroup1 has lower accuracy than Subgroup3 and Subgroup4. (Cstatistics in A and B cannot be compared, as they are based on models from different populations).
The inclusion and exclusion criteria (
As shown in
(A) Predictive accuracy of the Standard Model compared with the Hierarchical Model in congestive heart failure (CHF) as measured by the Cstatistic. The Cstatistic for the Centers for Medicare & Medicaid Services Standard Model is shown as a dotted line. (B) Predictive accuracy of the Standard Model when applied separately to patients classified to each subgroup. Subgroup1 has lower accuracy than Subgroup3 and Subgroup4. (Cstatistics in A and B cannot be compared, as they are based on models from different populations).
The inclusion and exclusion criteria (
As shown in
(A) Predictive accuracy of the Standard Model compared with the Hierarchical Model in total hip arthroplasty/total knee arthroplasty (THA/TKA) as measured by the Cstatistic. The Cstatistic for the Centers for Medicare & Medicaid Services Standard Model is shown as a dotted line. (B) Predictive accuracy of the Standard Model when applied separately to patients classified to each subgroup. Subgroup1 has lower accuracy than Subgroup7. (Cstatistics in A and B cannot be compared, as they are based on models developed from different populations).
Unlike the CMS published models, the models we developed used only the comorbidities that survived the feature selection. Therefore, to perform a headtohead comparison with the published CMS models, we also developed a CMS Standard Model (using the same variables from the published CMS model) and compared it to the corresponding CMS Hierarchical Model (with an additional variable for subgroup membership) in each condition. Similar to the models in
Comparison of the Centers for Medicare & Medicaid Services (CMS) Standard Model with the CMS Hierarchical Model across the three index conditions based on net reclassification improvement (NRI) and integrated discrimination improvement (IDI).
Model  NRI  IDI  

Categorical (95% CI)  Continuous (95% CI)  IDI (95% CI)  
COPD^{a}  0.023 (0.012 to 0.034)  −4.10  <.001  0.059 (0.034 to 0.083)  −4.68  <.001  0.0002 (−0.0004 to 0.0008)  −0.65  .51  
CHF^{b}  −0.010 (−0.016 to −0.004)  3.27  .001  −0.038 (−0.057 to −0.019)  3.92  <.001  −0.0006 (−0.0009 to −0.0003)  3.92  <.001  
THA/TKA^{c}  0.022 (0.012 to 0.032)  −4.31  <.001  0.111 (0.080 to 0.142)  −7.01  <.001  −0.003 (−0.004 to −0.002)  5.88  <.001 
^{a}COPD: chronic obstructive pulmonary disease.
^{b}CHF: congestive heart failure.
^{c}THA/TKA: total hip arthroplasty/total knee arthroplasty.
Our overall approach of using the MIPS framework to identify patient subgroups through visual analytics, and using those subgroups to build classification and prediction models revealed strengths and limitations for each modeling approach and for our data source. This examination provided insights for developing future clinical decision support systems and a methodological framework for improving the clinical interpretability of subgroup modeling results.
The results revealed three strengths of the visual analytical modeling: (1) the use of bipartite networks to simultaneously model patients and comorbidities enabled the automatic identification of patientcomorbidity biclusters and the integrated analysis of cooccurrence and risk; (2) the use of a bipartite modularity maximization algorithm to identify the biclusters enabled the measurement of the strength of the biclustering, critical for gauging its significance; and (3) the use of a graph representation enabled the results to be visualized through a network. Furthermore, the clinician stakeholders’ request to juxtapose the risk of each subgroup with their visualizations appeared to be driven by the need to reduce working memory loads (from having to remember that information when its spread over different outputs), which could have enhanced their ability to match bicluster patterns with chunks (previously learned patterns of information) stored in longterm memory. The resulting visualizations enabled them to recognize subtypes based on cooccurring comorbidities in each subgroup, reason about the processes that precipitate readmission based on the risk of each subtype relative to the other subtypes, and propose interventions that were targeted to those subtypes and their risks. Finally, the fact that the geriatrician could not fully interpret the THA/TKA network because it combined 2 fairly different conditions suggests that the clinical interpretations were not the result of a
However, the results also revealed two limitations: (1) although modularity is estimated using a closedform equation (formula), no closedform equation exists to estimate modularity variance, which is necessary to measure its significance. To estimate modularity variance, we used a permutation test by generating 1000 random permutations of the data and then compared the modularity generated from the real data, to the mean modularity generated from the permuted data. Given the size of our data sets (ranging from 7000 to 25,000 patients), this computationally expensive test took approximately 7 days to complete, despite the use of a dedicated server with multiple cores, and (2) although bicluster modularity was successful in identifying significant and meaningful patientcomorbidity biclusters, the visualizations themselves were extremely dense and therefore potentially concealed patterns within and between the subgroups. Future research should explore defining a closedform equation to estimate modularity variance, with the goal of accelerating the estimation of modularity significance, and more powerful analytical and visualization methods to reveal intra and intercluster associations in large and dense networks.
The results revealed two strengths of the classification modeling: (1) the use of a simple multinomial classifier was adequate to predict with high accuracy the subgroup to which a patient belonged; (2) because the model produced membership probabilities for each patient for each subgroup, the model captured the dense intercluster edges observed in the network visualization; and (3) the coefficients of the trained classifier could be inspected by an analyst, making it more transparent (relative to most deep learning classifiers that tend to be black boxes).
However, because we dichotomized the classification probabilities into a single subgroup membership, our approach did not fully leverage membership probabilities for modeling and visual interpretation. For example, some patients have high classification probabilities (representing strong membership) for a single subgroup (as shown by patients in the outer periphery of the biclusters with edges only within their bicluster), whereas others have equal probabilities for all subgroups (as shown in the inner periphery of the biclusters with edges going to multiple clusters). Future research should explore incorporating the probability of subgroup membership into the design of Hierarchical Models for improving predictive accuracy, and visualization methods for helping clinicians interpret patients with different profiles of membership strength, with the goal of designing patientspecific interventions.
The results revealed two strengths of the predictive modeling: (1) the use of the Standard Model to measure predictive accuracy across the subgroups helped to pinpoint which subgroups tended to have lower predictive accuracy than the rest and therefore which of them could benefit from a more complex but accurate SubgroupSpecific Model and (2) despite the use of a simple Hierarchical Model with a dichotomized membership label for each patient, the predictive CMS models detected significant differences in the prediction accuracy as measured by NRI in 2 of the conditions, when compared with the CMS Standard Models. However, the results also revealed that the differences in predictive accuracy as measured by the Cstatistic and NRI were small, suggesting that comorbidities alone were potentially insufficient for accurately predicting readmission. Future research should explore the use of electronic health records and multiple subgroupspecific models targeted to each subgroup (enabling each model to have different slopes and intercepts) to potentially improve the predictive accuracy of the prediction models.
The Medicare claims data had four key strengths: (1) the scale of the data sets that enabled subgroup identification with sufficient statistical power; (2) spread of the data collected from across the United States, which enabled generalizability of the results; (3) data about older adults, which enabled examination of subgroups in an underrepresented segment of the US population; and (4) data used by CMS to build predictive readmission models, which enabled a headtohead comparison with the Hierarchical Modeling approach.
However, these data had two critical limitations: (1) as we compared our models with the CMS models, we had to use the same definition for controls (90 days with no readmission) that had been used, which introduced a selection bias that exaggerated the separation between cases and controls. Similarly, by excluding patients who died, this exclusion criterion potentially biased the results toward healthier patients and (2) administrative data have known limitations, such as the lack of comorbidity severity and test results, which could strongly impact the accuracy of predictive models. Future research should consider the use of nationallevel electronic health record data, such as those assembled by the National COVID Cohort Collaborative [
Although the focus of this project was to develop and evaluate the MIPS framework, its application to 3 index conditions, coupled with extensive discussions with clinicians, led to insights for designing a future clinical decision support system. Such a system could integrate the outputs from all 3 models in MIPS. As we have shown, the visual analytical model automatically identified and visualized the patient subgroups, which enabled the clinicians to comprehend the cooccurrence and risk information in the visualization, reason about the processes that lead to readmission in each subgroup, and design targeted interventions. The classification model leveraged the observation that many patients have comorbidities in other biclusters (shown by a large number of edges between biclusters) and accordingly generated a membership probability (MP) of a patient belonging to each bicluster, from which the highest was chosen for bicluster membership. Finally, the predictive model calculated the risk of readmission for a patient by using the most accurate model designed for the bicluster to which the patient belonged.
The outputs from these models could be integrated into a clinical decision support system to provide recommendations for a specific patient using the following algorithm: (1) use the classifier to generate the MP of a new patient belonging to each subgroup; (2) use the predictive model to calculate the risk (R) of that patient in each subgroup; (3) generate an importance score (IS) for each subgroup, such as by calculating a
Although the visual analytical model enabled clinicians to interpret the patient subgroups, they were unable to interpret the associations within and between the subgroups because of the large number of nodes in each bicluster and the dense edges between them. Several network filtering methods [
An alternate approach that preserves the full data set leverages the notion of analytic granularity, in which the data are progressively analyzed at different levels. For example, we have analyzed patients with COVID19 [
Although we have demonstrated the application of the MIPS framework across multiple readmission conditions, its architecture has 3 properties that should enable its generalizability across other medical conditions. First, as shown in
Although several studies have identified patient subgroups in different health conditions, there is a considerable gap between the identification of subgroups and their modeling and interpretation for clinical applications. Here, we developed MIPS, a novel analytical framework to bridge this gap, using a 3step modeling approach. A visual analytical method automatically identified statistically significant and replicated patient subgroups and their frequently cooccurring comorbidities, which were clinically significant. Next, a multinomial logistic regression classifier was highly accurate in correctly classifying patients into subgroups identified by the visual analytical model. Finally, despite using a simple hierarchical logistic regression model to incorporate subgroup information, the predictive models showed a statistically significant improvement in discriminating between readmitted and not readmitted patients in 2 of the 3 readmission conditions, and additional analysis pinpointed for which patient subgroups the current CMS model might be underperforming. Furthermore, the integration of the 3 models helped to (1) elucidate the data input and output dependencies among the models, enabling clinicians to interpret the patient subgroups, reason about mechanisms precipitating hospital readmission, and design targeted interventions and (2) provide a generalizable framework for the development of future clinical decision support systems that integrate outputs from each of the 3 modeling approaches.
However, the evaluation of MIPS across the 3 readmission index conditions also helped to identify the limitations of each modeling method, and of the data. The visual analytical model was too dense to enable clinicians to interpret the associations within and between subgroups, and the absence of a closedform equation to measure modularity variance required a computationally expensive process to measure the significance of the biclustering. Furthermore, the small improvement in predictive accuracy suggested that comorbidities alone were insufficient for accurately predicting hospital readmission.
By leveraging the modular and extensible nature of the MIPS framework, future research should address these limitations by developing more powerful algorithms that analyze subgroups at different levels of granularity to improve the interpretability of intra and intercluster associations and the evaluation of subgroupspecific models to predict outcomes. Furthermore, data from electronic health records made available through nationallevel data initiatives, such as National COVID Cohort Collaborative and TriNetX, now provide access to critical variables, including laboratory results and comorbidity severity, which should lead to higher accuracy in predicting adverse outcomes. Finally, extensive discussions with clinicians have confirmed the need for decision support systems that integrate outputs from the 3 models to provide for a specific patient, predicted subgroup memberships, and ranked interventions, along with associated subgroup profiles and mechanisms. Such interpretable and explainable systems could enable clinicians to use patient subgroup information for informing the design of precision medicine interventions, with the goal of reducing adverse outcomes such as unplanned hospital readmissions and beyond.
Analytical methods for modeling and interpreting patient subgroups.
Patient inclusion and exclusion criteria.
Variable and feature selection.
Classification modeling.
Predictive modeling.
congestive heart failure
Centers for Medicare & Medicaid Services
chronic obstructive pulmonary disease
integrated discrimination improvement
importance score
modeling and interpreting patient subgroups
membership probability
net reclassification improvement
odds ratio
Rand Index
total hip arthroplasty/total knee arthroplasty
The authors thank Tianlong Chen, Clark Andersen, YuLi Lin, Gautam Vallabha, Erich Kummerfeld, and Emmanuel Santillana for their assistance in conducting the analyses. This study was supported in part by the PatientCentered Outcomes Research Institute (ME151133194) and the Clinical and Translational Science Award (UL1 TR001439) from the National Center for Advancing Translational Sciences at the National Institutes of Health, the University of Texas Medical Branch Claude D Pepper Older Americans Independence Center funded by the National Institute of Aging at the National Institutes of Health (P30 AG024832), MD Anderson Cancer Center, and the National Library of Medicine (R01 LM012095) at the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the PatientCentered Outcomes Research Institute or National Institutes of Health. Data from Medicare were analyzed using a Centers for Medicare & Medicaid Services datause agreement (CMS DUA RSCH201751404).
None declared.