Comorbidity Clusters in Autism Spectrum Disorders: An Electronic Health Record Time-Series Analysis

By Finale Doshi-Velez, Yaorong Ge, Isaac S. Kohane. 2014

403 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Genetics, Psychiatry and Mental health

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Citations in the corpus

SmoothGrad: removing noise by adding noise (2017, background): “From health care domains (Hughes et al., 2016; Doshi-Velez et al., 2014; Lou et al., 2012) to education (Kim et al., 2015), there are many domains where interpretability is important.”
Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[45] applied hierarchical clustering to define subgroups with distinct courses among autism spectrum disorders.”
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records (2016, background): “…studies have shown that secondary use of EHRs has enabled data-driven prediction of drug effects and interactions 2 , identification of type 2 diabetes subgroups 3 , discovery of comorbidity clusters in autism spectrum disorders 4 , and improvements in recruiting patients for clinical trials 5 .”
Graph-Sparse LDA: A Topic Model with Structured Sparsity (2014, neutral): “This topic—which shows a connection between the more severe form of ASD, intellectual disability, and epilepsy—as well as the other topics, matched recently published clinical results on ASD subtypes [16].”
Task-oriented Dialogue System for Automatic Diagnosis (2018, background): “Experimental re-sults prove the effectiveness of the identiﬁcation of heart failure (Jonnalagadda et al., 2017; Choi et al., 2016), type 2 diabetes (Li et al., 2015; Zheng et al., 2017), autism spectrum disorders (Doshi-Velez et al., 2014), infection detection (Tou et al., 2018) etc.”
Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery (2015, methodology): “Beyond s-markers, others have used ICD-9 codes—codes indicating the presence or absence of a condition—to study comorbidity patterns over time among patients with a shared disease (e.g. Doshi-Velez, Ge, and Kohane 2014).”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, background): “Now, researchers are able to study phenotypes at different levels of detail - for example, drug-dose response [48,87,88] versus longitudinal analyses [89,90].”
Using Electronic Health Records for Population Health Research: A Review of Methods and Applications (2015, background): “These could include improved capture of social/ behavioral (2), environmental, and genetic data (56); phenotyping (37); clinical biobanks; improved natural language processing; personal sensing via smartphone; and social media.”
Opportunities in Machine Learning for Healthcare (2018, neutral): “mmensely, including work in diagnosing diabetic retinopathy (Gulshan et al., 2016), detecting lymph node metastases from breast pathology (Golden, 2017), autism subtyping by clustering comorbidities (Doshi-Velez et al., 2014), and largescale phenotyping from observational data (Pivovarovet al., 2015”
A Roadmap for a Rigorous Science of Interpretability (2017, background): “…to predictive policing systems, machine learning (ML) systems are increasingly ubiquitous; they outperform humans on specific tasks [Mnih et al., 2013, Silver et al., 2016, Hamill, 2017] and often guide processes of human understanding and decisions [Carton et al., 2016, Doshi-Velez et al., 2014].”
Building a knowledge graph to enable precision medicine (2022, methodology): “Autism has been shown to manifest as three clinical subgroups characterized primarily by seizures, multisystem and gastrointestinal disorders, and psychiatric disorders 56 .”
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction (2015, background): “Diseases Finally, we consider a data set of patients with autism spectrum disorder (ASD) accumulated over the first 15 years of life [25].”
Towards A Rigorous Science of Interpretable Machine Learning (2017, background): “…to predictive policing systems, machine learning (ML) systems are increasingly ubiquitous; they outperform humans on speciﬁc tasks [Mnih et al., 2013, Silver et al., 2016, Hamill, 2017] and often guide processes of human understanding and decisions [Carton et al., 2016, Doshi-Velez et al., 2014].”