Learning a Cost-Effective Strategy on Incomplete Medical Data

Zhu, Mengxiao; Zhu, Haogang

doi:10.1007/978-3-030-59416-9_11

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…This study extends our earlier work [12]. Compared with the conference version, the extensions are as follows: (1) The objective of the problem is adjusted.…”

Section: Related Workmentioning

confidence: 88%

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Learning a Diagnostic Strategy on Medical Data With Deep Reinforcement Learning

Zhu

2021

IEEE Access

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In recent years, Artificial Intelligence based disease diagnosis has drawn considerable attention both in academia and industry. In medical scenarios, a well-trained classifier can effectively detect a disease with sufficient features associating with medical tests. However, such features are not always readily available due to the high cost of time and money associating with medical tests. To address this, this study identifies the diagnostic strategy learning problem and proposes a novel framework consisting of three components to learn a diagnostic strategy with limited features. First, as we often encounter incomplete medical records of the patients, a sequence encoder is designed to encode any set of information in various sizes into fixed-length vectors. Second, taking the output of the encoder as the input, a feature selector based on reinforcement learning techniques is proposed to learn the best feature sequence for diagnosis. Finally, with the best feature sequence, an oracle classifier is used to give the final diagnosis. To evaluate the performance of the proposed method, experiments are conducted on nine real medical datasets. The results suggest that the proposed method is effective for providing personalized diagnostic strategies and makes better diagnoses with fewer features compared with existing methods.

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“…This study extends our earlier work [12]. Compared with the conference version, the extensions are as follows: (1) The objective of the problem is adjusted.…”

Section: Related Workmentioning

confidence: 88%

“…Compared with the conference version, the extensions are as follows: (1) The objective of the problem is adjusted. In [12], we only consider the cost of money, which is myopic. In addition to money, the cost of time and energy are also important.…”

Section: Related Workmentioning

confidence: 99%

Learning a Diagnostic Strategy on Medical Data With Deep Reinforcement Learning

Zhu

2021

IEEE Access

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A Decision Support System for Heart Failure Risk Prediction Based on Weighted Naive Bayes

Song

Zhang

et al. 2021

Database Systems for Advanced Applications

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Classification with costly features in hierarchical deep sets

Janisch,

Pevný,

Lisý

2024

Mach Learn

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Classification with costly features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features are sequentially acquired to maximize accuracy while minimizing the acquired features’ cost. However, existing approaches can only process data that can be expressed as vectors of fixed length. In real life, the data often possesses rich and complex structure, which can be more precisely described with formats such as XML or JSON. The data is hierarchical and often contains nested lists of objects. In this work, we extend an existing deep reinforcement learning-based algorithm with hierarchical deep sets and hierarchical softmax, so that it can directly process this data. The extended method has greater control over which features it can acquire and, in experiments with seven datasets, we show that this leads to superior performance. To showcase the real usage of the new method, we apply it to a real-life problem of classifying malicious web domains, using an online service.

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Learning a Cost-Effective Strategy on Incomplete Medical Data

Cited by 3 publications

References 8 publications

Learning a Diagnostic Strategy on Medical Data With Deep Reinforcement Learning

Learning a Diagnostic Strategy on Medical Data With Deep Reinforcement Learning

A Decision Support System for Heart Failure Risk Prediction Based on Weighted Naive Bayes

Classification with costly features in hierarchical deep sets

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