845 research outputs found
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Machine learning approaches have been effective in predicting adverse
outcomes in different clinical settings. These models are often developed and
evaluated on datasets with heterogeneous patient populations. However, good
predictive performance on the aggregate population does not imply good
performance for specific groups.
In this work, we present a two-step framework to 1) learn relevant patient
subgroups, and 2) predict an outcome for separate patient populations in a
multi-task framework, where each population is a separate task. We demonstrate
how to discover relevant groups in an unsupervised way with a
sequence-to-sequence autoencoder. We show that using these groups in a
multi-task framework leads to better predictive performance of in-hospital
mortality both across groups and overall. We also highlight the need for more
granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201
Unsupervised learning methods for identifying and evaluating disease clusters in electronic health records
Introduction
Clustering algorithms are a class of algorithms that can discover groups of observations in
complex data and are often used to identify subtypes of heterogeneous diseases in electronic
health records (EHR). Evaluating clustering experiments for biological and clinical significance is
a vital but challenging task due to the lack of consensus on best practices. As a result, the
translation of findings from clustering experiments to clinical practice is limited.
Aim
The aim of this thesis was to investigate and evaluate approaches that enable the evaluation of
clustering experiments using EHR.
Methods
We conducted a scoping review of clustering studies in EHR to identify common evaluation
approaches. We systematically investigated the performance of the identified approaches using
a cohort of Alzheimer's Disease (AD) patients as an exemplar comparing four different
clustering methods (K-means, Kernel K-means, Affinity Propagation and Latent Class
Analysis.). Using the same population, we developed and evaluated a method (MCHAMMER)
that tested whether clusterable structures exist in EHR. To develop this method we tested
several cluster validation indexes and methods of generating null data to see which are the best
at discovering clusters. In order to enable the robust benchmarking of evaluation approaches,
we created a tool that generated synthetic EHR data that contain known cluster labels across a
range of clustering scenarios.
Results
Across 67 EHR clustering studies, the most popular internal evaluation metric was comparing
cluster results across multiple algorithms (30% of studies). We examined this approach
conducting a clustering experiment on AD patients using a population of 10,065 AD patients and
21 demographic, symptom and comorbidity features. K-means found 5 clusters, Kernel K means found 2 clusters, Affinity propagation found 5 and latent class analysis found 6. K-means
4
was found to have the best clustering solution with the highest silhouette score (0.19) and was
more predictive of outcomes. The five clusters found were: typical AD (n=2026), non-typical AD
(n=1640), cardiovascular disease cluster (n=686), a cancer cluster (n=1710) and a cluster of
mental health issues, smoking and early disease onset (n=1528), which has been found in
previous research as well as in the results of other clustering methods. We created a synthetic
data generation tool which allows for the generation of realistic EHR clusters that can vary in
separation and number of noise variables to alter the difficulty of the clustering problem. We
found that decreasing cluster separation did increase cluster difficulty significantly whereas
noise variables increased cluster difficulty but not significantly. To develop the tool to assess
clusters existence we tested different methods of null dataset generation and cluster validation
indices, the best performing null dataset method was the min max method and the best
performing indices we Calinksi Harabasz index which had an accuracy of 94%, Davies Bouldin
index (97%) silhouette score ( 93%) and BWC index (90%). We further found that when clusters
were identified using the Calinski Harabasz index they were more likely to have significantly
different outcomes between clusters. Lastly we repeated the initial clustering experiment,
comparing 10 different pre-processing methods. The three best performing methods were RBF
kernel (2 clusters), MCA (4 clusters) and MCA and PCA (6 clusters). The MCA approach gave
the best results highest silhouette score (0.23) and meaningful clusters, producing 4 clusters;
heart and circulatory( n=1379), early onset mental health (n=1761), male cluster with memory
loss (n = 1823), female with more problem (n=2244).
Conclusion
We have developed and tested a series of methods and tools to enable the evaluation of EHR
clustering experiments. We developed and proposed a novel cluster evaluation metric and
provided a tool for benchmarking evaluation approaches in synthetic but realistic EHR
Discovering Patient Phenotypes Using Generalized Low Rank Models
The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients
to inform corresponding treatment. Given a patient grouping (hereafter referred to as a p henotype ), clinicians can
implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally,
phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these
approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our
understanding of disease has progressed substantially in the past century, there are still important domains in which
our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery,
researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by
missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low
Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we
analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains
upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second,
we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that
low rank modeling successfully captures known and putative phenotypes in these vastly different datasets
Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records
Diabetes tipus 2; Complicacions diabètiques; Registres de salut electrònicsDiabetes tipo 2; Complicaciones diabéticas; Registros de salud electrónicosType 2 diabetes; Diabetic complications; Electronic health recordsType 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) techniques and the availability of a large amount of healthcare data allow us to investigate T2DM characteristics and evolution with a completely new approach, studying common disease trajectories rather than cross sectional values. We used an Kernelized-AutoEncoder algorithm to map 5 years of data of 11,028 subjects diagnosed with T2DM in a latent space that embedded similarities and differences between patients in terms of the evolution of the disease. Once we obtained the latent space, we used classical clustering algorithms to create longitudinal clusters representing different evolutions of the diabetic disease. Our unsupervised DL clustering algorithm suggested seven different longitudinal clusters. Different mean ages were observed among the clusters (ranging from 65.3±11.6 to 72.8±9.4). Subjects in clusters B (Hypercholesteraemic) and E (Hypertensive) had shorter diabetes duration (9.2±3.9 and 9.5±3.9 years respectively). Subjects in Cluster G (Metabolic) had the poorest glycaemic control (mean glycated hemoglobin 7.99±1.42%), while cluster E had the best one (mean glycated hemoglobin 7.04±1.11%). Obesity was observed mainly in clusters A (Neuropathic), C (Multiple Complications), F (Retinopathy) and G. A dashboard is available at dm2.b2slab.upc.edu to visualize the different trajectories corresponding to the 7 clusters
Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records
Altres ajuts: Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN); Instituto de Investigación Carlos III (ISCIII); CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM).Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) techniques and the availability of a large amount of healthcare data allow us to investigate T2DM characteristics and evolution with a completely new approach, studying common disease trajectories rather than cross sectional values. We used an Kernelized-AutoEncoder algorithm to map 5 years of data of 11,028 subjects diagnosed with T2DM in a latent space that embedded similarities and differences between patients in terms of the evolution of the disease. Once we obtained the latent space, we used classical clustering algorithms to create longitudinal clusters representing different evolutions of the diabetic disease. Our unsupervised DL clustering algorithm suggested seven different longitudinal clusters. Different mean ages were observed among the clusters (ranging from 65.3±11.6 to 72.8±9.4). Subjects in clusters B (Hypercholesteraemic) and E (Hypertensive) had shorter diabetes duration (9.2±3.9 and 9.5±3.9 years respectively). Subjects in Cluster G (Metabolic) had the poorest glycaemic control (mean glycated hemoglobin 7.99±1.42%), while cluster E had the best one (mean glycated hemoglobin 7.04±1.11%). Obesity was observed mainly in clusters A (Neuropathic), C (Multiple Complications), F (Retinopathy) and G. A dashboard is available at dm2.b2slab.upc.edu to visualize the different trajectories corresponding to the 7 clusters
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