Multi-label classification methods for improving comorbidities identification

Wosiak, Agnieszka; Glinka, Kinga; Zakrzewska, Danuta

doi:10.1016/j.compbiomed.2017.07.006

Cited by 14 publications

(2 citation statements)

References 24 publications

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“…Furthermore, MTL assumes that every example (patient) is associated with all tasks (diseases), while MLL allows each example (patient) to be associated with a subset of labels (diseases) [54], making MLL a more general model. Hence, most previous works on MDPA have used MLL models [39,42,49,57].…”

Section: Multi-disease Predictive Analyticsmentioning

confidence: 99%

Multi-disease Predictive Analytics: A Clinical Knowledge-aware Approach

Qiu

Gorantla

Rajan

et al. 2021

ACM Trans. Manage. Inf. Syst.

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Multi-Disease Predictive Analytics (MDPA) models simultaneously predict the risks of multiple diseases in patients and are valuable in early diagnoses. Patients tend to have multiple diseases simultaneously or develop multiple complications over time, and MDPA models can learn and effectively utilize such correlations between diseases. Data from large-scale Electronic Health Records (EHR) can be used through Multi-Label Learning (MLL) methods to develop MDPA models. However, data-driven approaches for MDPA face the challenge of data imbalance, because rare diseases tend to have much less data than common diseases. Insufficient data for rare diseases makes it difficult to leverage correlations with other diseases. These correlations are studied and recorded in biomedical literature but are rarely utilized in predictive analytics. This article presents a novel method called Knowledge-Aware Approach (KAA) that learns clinical correlations from the rapidly growing body of clinical knowledge. KAA can be combined with any data-driven MLL model for MDPA to refine the predictions of the model. Our extensive experiments, on real EHR data, show that the use of KAA improves the predictive performance of commonly used MDPA models, particularly for rare diseases. KAA is also found to be superior to existing general approaches of combining clinical knowledge with data-driven models. Further, a counterfactual analysis shows the efficacy of KAA in improving physicians’ ability to prescribe preventive treatments.

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Section: Multi-disease Predictive Analyticsmentioning

confidence: 99%

Multi-disease Predictive Analytics: A Clinical Knowledge-aware Approach

Qiu

Gorantla

Rajan

et al. 2021

ACM Trans. Manage. Inf. Syst.

View full text Add to dashboard Cite

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“…However, most of these works focused on mortality or readmission prediction, with few focusing on complications of heart failure. Wosiak et al proposed a multilabel classification technique for comorbidities identification, which is a general model for all kinds of diseases [19]. Xiang et al proposed a multi-task framework that can jointly predict the risk of multiple related diseases, and the method was tested on patients with Congestive Heart Failure and Chronic Obstructive Pulmonary Disease [20].…”

Section: Related Work a Heart Failure Outcomes Predictionmentioning

confidence: 99%

A Multi-Task Neural Network Architecture for Renal Dysfunction Prediction in Heart Failure Patients With Electronic Health Records

et al. 2019

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Renal dysfunction, which is associated with bad clinical outcomes, is one of the most common complications of heart failure (HF). Timely prediction of renal dysfunction can help medical staffs intervene early to avoid catastrophic consequences. In this paper, we proposed a multi-task deep and wide neural network (MT-DWNN) for predicting fatal complications during hospitalization. The algorithm was tested on a dataset collected from Chinese PLA General Hospital, which contains 35,101 hospitalizations with HF diagnosis during the last 18 years, and 2,478 hospitalizations with a diagnosis of renal dysfunction. For the renal dysfunction task, the AUC of the proposed method is 0.9393, which is a significant improvement (p < 0.01) compared to that of conventional methods, while that of single task deep neural networks is 0.9370, that of random forest is 0.9360, and that of logistic regression is 0.9233. The experimental results show that the proposed MT-DWNN model achieves better prediction performance on renal dysfunction in HF patients than conventional models.INDEX TERMS Heart failure, renal dysfunction, deep and wide neural network, multi-task.

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