Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network

Guo, Wei; Gao, Guoyun; Dai, Jun; Sun, Qiming

doi:10.1155/2022/4312117

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…Another study delved into taste and general activity [ 40 ]. Individual studies were dedicated to each of the following symptoms: delirium [ 31 ], lung infection [ 32 ], lymphedema [ 39 ], well-being [ 37 ], odynophagia [ 54 ], social distress [ 26 ], spiritual pain [ 26 ], dyspnea [ 26 ], and hearing loss [ 57 ]. The distribution of these symptoms is depicted in Multimedia Appendix 5 .…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Zeinali,

Youn,

Albashayreh

et al. 2024

JMIR Cancer

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Background People with cancer frequently experience severe and distressing symptoms associated with cancer and its treatments. Predicting symptoms in patients with cancer continues to be a significant challenge for both clinicians and researchers. The rapid evolution of machine learning (ML) highlights the need for a current systematic review to improve cancer symptom prediction. Objective This systematic review aims to synthesize the literature that has used ML algorithms to predict the development of cancer symptoms and to identify the predictors of these symptoms. This is essential for integrating new developments and identifying gaps in existing literature. Methods We conducted this systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. We conducted a systematic search of CINAHL, Embase, and PubMed for English records published from 1984 to August 11, 2023, using the following search terms: cancer, neoplasm, specific symptoms, neural networks, machine learning, specific algorithm names, and deep learning. All records that met the eligibility criteria were individually reviewed by 2 coauthors, and key findings were extracted and synthesized. We focused on studies using ML algorithms to predict cancer symptoms, excluding nonhuman research, technical reports, reviews, book chapters, conference proceedings, and inaccessible full texts. Results A total of 42 studies were included, the majority of which were published after 2017. Most studies were conducted in North America (18/42, 43%) and Asia (16/42, 38%). The sample sizes in most studies (27/42, 64%) typically ranged from 100 to 1000 participants. The most prevalent category of algorithms was supervised ML, accounting for 39 (93%) of the 42 studies. Each of the methods—deep learning, ensemble classifiers, and unsupervised ML—constituted 3 (3%) of the 42 studies. The ML algorithms with the best performance were logistic regression (9/42, 17%), random forest (7/42, 13%), artificial neural networks (5/42, 9%), and decision trees (5/42, 9%). The most commonly included primary cancer sites were the head and neck (9/42, 22%) and breast (8/42, 19%), with 17 (41%) of the 42 studies not specifying the site. The most frequently studied symptoms were xerostomia (9/42, 14%), depression (8/42, 13%), pain (8/42, 13%), and fatigue (6/42, 10%). The significant predictors were age, gender, treatment type, treatment number, cancer site, cancer stage, chemotherapy, radiotherapy, chronic diseases, comorbidities, physical factors, and psychological factors. Conclusions This review outlines the algorithms used for predicting symptoms in individuals with cancer. Given the diversity of symptoms people with cancer experience, analytic approaches that can handle complex and nonlinear relationships are critical. This knowledge can pave the way for crafting algorithms tailored to a specific symptom. In addition, to improve prediction precision, future research should compare cutting-edge ML strategies such as deep learning and ensemble methods with traditional statistical models.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Zeinali,

Youn,

Albashayreh

et al. 2024

JMIR Cancer

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“…In addition to predicting survival, ML models can also be used to predict other clinical endpoints, such as patient complications during treatment. For example, Guo et al [12] used ML and DL models with inputs from demographics, pathological information, and patient history to predict outcomes of pulmonary infection for patients receiving chemotherapy. Christopherson et al [16] used various ML models, taking input data from demographics, medical history, and clinical procedure information, ] used a DL model with input data from demographics and health profiles to predict SPC services required by cancer patients at home.…”

Section: Dihman Et Al [43mentioning

confidence: 99%

Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients

Reddy

Nafees

Raman

2023

Current Opinion in Supportive &Amp; Palliative Care

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Purpose of review Artificial intelligence (AI) is a transformative technology that has the potential to improve and augment the clinical workflow in supportive and palliative care (SPC). The objective of this study was to provide an overview of the recent studies applying AI to SPC in cancer patients. Recent findings Between 2020 and 2022, 29 relevant studies were identified and categorized into two applications: predictive modeling and text screening. Predictive modeling uses machine learning and/or deep learning algorithms to make predictions regarding clinical outcomes. Most studies focused on predicting short-term mortality risk or survival within 6 months, while others used models to predict complications in patients receiving treatment and forecast the need for SPC services. Text screening typically uses natural language processing (NLP) to identify specific keywords, phrases, or documents from patient notes. Various applications of NLP were found, including the classification of symptom severity, identifying patients without documentation related to advance care planning, and monitoring online support group chat data. Summary This literature review indicates that AI tools can be used to support SPC clinicians in decision-making and reduce manual workload, leading to potentially improved care and outcomes for cancer patients. Emerging data from prospective studies supports the clinical benefit of these tools; however, more rigorous clinical validation is required before AI is routinely adopted in the SPC clinical workflow.

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“…To address these concerns, the existing work information gain model was used to select attributes. Then, for lung cancer prediction, multilayer perceptron (MLP), random subspace, and SMO are used 6 , 7 . However, the total number of parameters in a MLP can become extremely large.…”

Section: Introductionmentioning

confidence: 99%

Lung cancer diagnosis based on weighted convolutional neural network using gene data expression

Koti,

B.A

et al. 2024

Sci Rep

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Lung cancer is thought to be a genetic disease with a variety of unknown origins. Globocan2020 report tells in 2020 new cancer cases identified was 19.3 million and nearly 10.0 million died owed to cancer. GLOBOCAN envisages that the cancer cases will raised to 28.4 million in 2040. This charge is superior to the combined rates of the former generally prevalent malignancies, like breast, colorectal, and prostate cancers. For attribute selection in previous work, the information gain model was applied. Then, for lung cancer prediction, multilayer perceptron, random subspace, and sequential minimal optimization (SMO) are used. However, the total number of parameters in a multilayer perceptron can become extremely large. This is inefficient because of the duplication in such high dimensions, and SMO can become ineffective due to its calculating method and maintaining a single threshold value for prediction. To avoid these difficulties, our research presented a novel technique including Z-score normalization, levy flight cuckoo search optimization, and a weighted convolutional neural network for predicting lung cancer. This result findings show that the proposed technique is effective in precision, recall, and accuracy for the Kent Ridge Bio-Medical Dataset Repository.

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Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network

Cited by 6 publications

References 26 publications

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients

Lung cancer diagnosis based on weighted convolutional neural network using gene data expression

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