Deep-learning-based personalized prediction of absolute neutrophil count recovery and comparison with clinicians for validation

Choo, Hyunwoo; Yoo, Su Young; Moon, Suhyeon; Park, Minsu; Lee, Jiwon; Sung, Ki Woong; Chul, Won; Shin, Soo-Yong; Son, Meong Hi

doi:10.1016/j.jbi.2022.104268

Cited by 3 publications

(4 citation statements)

References 28 publications

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“…Future work could compare the performance of this hybrid approach with more complex ML approaches in different levels of data sparsity. For example, Choo et al 5 proposed a deep learning time series approach for describing ANC dynamics during chemotherapy, however, this model was developed on data an order of magnitude more densely sampled than our data, and may not be practical for many clinical applications with sparser sampling. One limitation of our study is that the data set lacked drug concentrations, and so our PKPD model relied on PK parameters reported in the literature.…”

Section: Discussionmentioning

confidence: 99%

“…Clinical decision support tools based on predictive models can potentially identify patients at risk of neutropenia and suggest treatment modifications 5–7 . Models that allow individualization of risk scores have been proposed 8,9 .…”

Section: Introductionmentioning

confidence: 99%

“…Clinical decision support tools based on predictive models can potentially identify patients at risk of neutropenia and suggest treatment modifications. [5][6][7] Models that allow individualization of risk scores have been proposed. 8,9 However, these models dichotomize neutropenia risk and do not incorporate administration of G-CSF in their risk scores, preventing prediction of when ANC may rebound sufficiently for the beginning of the next round of chemotherapy.…”

Section: Introductionmentioning

confidence: 99%

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Clinical decision support for chemotherapy‐induced neutropenia using a hybrid pharmacodynamic/machine learning model

Hughes,

Tong,

Burns

et al. 2023

CPT Pharmacom & Syst Pharma

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Consensus guidelines recommend use of granulocyte colony stimulating factor in patients deemed at risk of chemotherapy‐induced neutropenia, however, these risk models are limited in the factors they consider and miss some cases of neutropenia. Clinical decision making could be supported using models that better tailor their predictions to the individual patient using the wealth of data available in electronic health records (EHRs). Here, we present a hybrid pharmacokinetic/pharmacodynamic (PKPD)/machine learning (ML) approach that uses predictions and individual Bayesian parameter estimates from a PKPD model to enrich an ML model built on her data. We demonstrate this approach using models developed on a large real‐world data set of 9121 patients treated for lymphoma, breast, or thoracic cancer. We also investigate the benefits of augmenting the training data using synthetic data simulated with the PKPD model. We find that PKPD‐enrichment of ML models improves prediction of grade 3–4 neutropenia, as measured by higher precision (61%) and recall (39%) compared to PKPD model predictions (47%, 33%) or base ML model predictions (51%, 31%). PKPD augmentation of ML models showed minor improvements in recall (44%) but not precision (56%), and data augmentation required careful tuning to control overfitting its predictions to the PKPD model. PKPD enrichment of ML shows promise for leveraging both the physiology‐informed predictions of PKPD and the ability of ML to learn predictor‐outcome relationships from large data sets to predict patient response to drugs in a clinical precision dosing context.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinical decision support for chemotherapy‐induced neutropenia using a hybrid pharmacodynamic/machine learning model

Hughes,

Tong,

Burns

et al. 2023

CPT Pharmacom & Syst Pharma

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“…Deep learning is a field of study that shows great promise for medical image analysis and knowledge discovery that is approaching clinicians' performance in a growing range of tasks [1][2][3]. The interdisciplinary study of Alzheimer's disease (AD) and deep learning have been a focus of interest for the past 13 years.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

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Mbi-Tcmr: A Traditional Chinese Formula Recommendation Deep Learning Model Based on Multi-Task Meta Learning and Biological Network Structure

MIAO,

HAN,

ZI-HAN

et al. 2024

J. Mech. Med. Biol.

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The performance of existing traditional Chinese medicine (TCM) recommendation models is generally poor because of their weak generalization ability, overfitting, and inability to use known biological networks. Therefore, building a TCM recommendation model based on artificial intelligence has currently become an important bioinformatics task. This study aimed to design a multitask meta-learning model with good biological interpretation for TCM formula recommendation (MBI-TCMR) for deep learning regularization. This method was based on the known biological network structure to sparse the deep learning network, solve the overfitting problem of the model, and enhance the biological interpretability of the model. Furthermore, a multi-learning framework based on meta-learning was also proposed. The framework allowed the MBI-TCMR model to mine knowledge of TCM formulas and quickly adapt to different types of TCM formula recommendation tasks. Finally, we used a gradient-based deep learning feature backtracking method to calculate the feature weight for each neuron. This weight could provide valuable explanatory information for researchers to study how the model made its medicine recommendations. We designed three independent experiments. The experimental results showed that the hit ratio (HR), AUC, and recall and precision value of the MBI-TCMR model outperformed the existing TCM formula recommendation models. The MBI-TCMR model’s HR of top 1–10 reached 0.15–0.9 (Gynecologic Disease Dataset). HR was 10 for the MBI-TCMR model, which was an improvement of 11.1% compared with the best baseline model. The bio-enrichment analysis showed that the model exhibited good bio-interpretation. In summary, this study proposed a novel TCM formula recommendation model, which expanded the application of the artificial intelligence model and achieved good results.

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Deep-learning-based personalized prediction of absolute neutrophil count recovery and comparison with clinicians for validation

Cited by 3 publications

References 28 publications

Clinical decision support for chemotherapy‐induced neutropenia using a hybrid pharmacodynamic/machine learning model

Clinical decision support for chemotherapy‐induced neutropenia using a hybrid pharmacodynamic/machine learning model

A Survey of Deep Learning for Alzheimer’s Disease

Mbi-Tcmr: A Traditional Chinese Formula Recommendation Deep Learning Model Based on Multi-Task Meta Learning and Biological Network Structure

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