Clinical risk prediction with multilinear sparse logistic regression

Wang, Fei; Zhang, Ping; Qian, Buyue; Wang, Xiang; Davidson, Ian

doi:10.1145/2623330.2623755

Cited by 40 publications

(32 citation statements)

References 31 publications

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“…This technique is computationally expensive and would not work efficiently with a large dataset, therefore, they only focused on a small number of diagnoses. By taking advantage of a different set of inputs, functional magnetic resonance imaging (fMRI) images, Wang et al proposed high-order sparse logistic regression and multilinear sparse logistic regression [18,19] for early detection of Alzheimer disease and congestive heart failure. Their results surpassed standard learning algorithms, such as nearest neighbor, support vector machines (SVM), logistic regression (LR), and sparse logistic regression.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

Farhan¹,

Wang²,

Huang³

et al. 2016

JMIR Med Inform

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BackgroundMedical concepts are inherently ambiguous and error-prone due to human fallibility, which makes it hard for them to be fully used by classical machine learning methods (eg, for tasks like early stage disease prediction).ObjectiveOur work was to create a new machine-friendly representation that resembles the semantics of medical concepts. We then developed a sequential predictive model for medical events based on this new representation.MethodsWe developed novel contextual embedding techniques to combine different medical events (eg, diagnoses, prescriptions, and labs tests). Each medical event is converted into a numerical vector that resembles its “semantics,” via which the similarity between medical events can be easily measured. We developed simple and effective predictive models based on these vectors to predict novel diagnoses.ResultsWe evaluated our sequential prediction model (and standard learning methods) in estimating the risk of potential diseases based on our contextual embedding representation. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.79 on chronic systolic heart failure and an average AUC of 0.67 (over the 80 most common diagnoses) using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset.ConclusionsWe propose a general early prognosis predictor for 80 different diagnoses. Our method computes numeric representation for each medical event to uncover the potential meaning of those events. Our results demonstrate the efficiency of the proposed method, which will benefit patients and physicians by offering more accurate diagnosis.

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Section: Introductionmentioning

confidence: 99%

A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

Farhan¹,

Wang²,

Huang³

et al. 2016

JMIR Med Inform

Self Cite

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“…They build a patient-diagnosis-procedure tensor and apply CP-APR factorization to decompose it as summations of rank-1 bias tensors and rank-R interaction tensors with sparsity constraints on the factor matrices of interaction tensors, in order to explicitly account for interactions among groups of the same modality. Wang et al 44 studied the problem of predicting the onset risk of patients with heart failure. They applied tensor modeling to generalize sparse logistic regression to multiple modalities on EHR data, such as comorbidity diagnosis codes and medications, and called their model High Order Sparse Logistic Regression (HOSLR).…”

Section: Tensor Factorization: a Potential Solution For Multi-modal Dmentioning

confidence: 99%

Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction

Luo

Ahmad

Shah

2017

J. of Cardiovasc. Trans. Res.

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Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome that may benefit from improved subtyping in order to better characterize its pathophysiology and to develop novel targeted therapies. The United States Precision Medicine Initiative comes amid the rapid growth in quantity and modality of clinical data for HFpEF patients ranging from deep phenotypic to trans-omic data. Tensor factorization, a form of machine learning, allows for the integration of multiple data modalities to derive clinically relevant HFpEF subtypes that may have significant differences in underlying pathophysiology and differential response to therapies. Tensor factorization also allows for better interpretability by supporting dimensionality reduction and identifying latent groups of data for meaningful summarization of both features and disease outcomes. In this narrative review, we analyze the modest literature on the application of tensor factorization to related biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest multiple tensor factorization formulations capable of integrating the deep phenotypic and trans-omic modalities of data for HFpEF, or accounting for interactions between genetic variants at different -omic hierarchies. We encourage extensive experimental studies to tackle challenges in applying tensor factorization for precision medicine in HFpEF, including effectively incorporating existing medical knowledge, properly accounting for uncertainty, and efficiently enforcing sparsity for better interpretability.

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“…For example, Wang et al [4] presented a multilinear sparse logistic regression for risk prediction with patient EHRs. Zhang et al [5] proposed a similarity based approach for personalized treatment recommendation.…”

Section: Introductionmentioning

confidence: 99%

Risk Prediction with Electronic Health Records: A Deep Learning Approach

Cheng

Wang

Zhang

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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316

187

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The recent years have witnessed a surge of interests in data analytics with patient Electronic Health Records (EHR). Data-driven healthcare, which aims at effective utilization of big medical data, representing the collective learning in treating hundreds of millions of patients, to provide the best and most personalized care, is believed to be one of the most promising directions for transforming healthcare. EHR is one of the major carriers for make this data-driven healthcare revolution successful. There are many challenges on working directly with EHR, such as temporality, sparsity, noisiness, bias, etc. Thus effective feature extraction, or phenotyping from patient EHRs is a key step before any further applications. In this paper, we propose a deep learning approach for phenotyping from patient EHRs. We first represent the EHRs for every patient as a temporal matrix with time on one dimension and event on the other dimension. Then we build a fourlayer convolutional neural network model for extracting phenotypes and perform prediction. The first layer is composed of those EHR matrices. The second layer is a one-side convolution layer that can extract phenotypes from the first layer. The third layer is a max pooling layer introducing sparsity on the detected phenotypes, so that only those significant phenotypes will remain. The fourth layer is a fully connected softmax prediction layer. In order to incorporate the temporal smoothness of the patient EHR, we also investigated three different temporal fusion mechanisms in the model: early fusion, late fusion and slow fusion. Finally the proposed model is validated on a real world EHR data warehouse under the specific scenario of predictive modeling of chronic diseases.

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Clinical risk prediction with multilinear sparse logistic regression

Cited by 40 publications

References 31 publications

A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction

Risk Prediction with Electronic Health Records: A Deep Learning Approach

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