Exploring Fever of Unknown Origin Intelligent Diagnosis Based on Clinical Data: Model Development and Validation

Jiang, Huizhen; Li, Yuanjie; Zeng, Xuejun; Xu, Na; Zhao, Congpu; Zhang, Jing; Zhu, Weiguo

doi:10.2196/24375

Cited by 7 publications

(10 citation statements)

References 31 publications

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“…Our systematic search could only identify studies explicitly mentioning transfer learning or a similar term (e.g. ‘knowledge transfer’), however this does not seem to be a common practice in medical text analysis [ 121 – 123 ]. Future studies are warranted about the impact of specific models (e.g.…”

Section: Discussionmentioning

confidence: 99%

Transfer learning for non-image data in clinical research: A scoping review

et al. 2022

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Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.

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Section: Discussionmentioning

confidence: 99%

Transfer learning for non-image data in clinical research: A scoping review

et al. 2022

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“…59,60 Unlike the above-mentioned diagnostic and predictive tools, AUC values that quantified discriminative power were not used as the main evaluation indictor for classification tools, with only four studies reported the values of AUC. 54,59,60,62 Instead, model accuracy 54,56,58,59,62 and precision 55,56,61 were the favorable indicator evaluating model performance. Other supporting indices included recall rates, 58,61 F-1 score, 56,58,59 and Kappa value.…”

Section: Validation Methods and Outcomes Measuresmentioning

confidence: 99%

“…Chinese medicine syndromes of liver cancer, 58 various causes for fever of unknown origin, 55 multidisease and multilesion diagnosis, 56 and the severity of diabetic retinopathy. 60…”

Section: Clinical Benefits and Therapeutic Areasmentioning

confidence: 99%

Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review

Liao,

Yao,

Zhang

et al. 2023

J Evidence Based Medicine

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ObjectiveStriking innovations and advancements have been achieved with the use of artificial intelligence and healthcare information technology being integrated into clinical real‐world data. The current scoping review aimed to provide an overview of the current status of artificial intelligence‐/information technology‐based clinical decision support tools in China.MethodsPubMed/MEDLINE, Embase, China National Knowledge Internet, and Wanfang data were searched for both English and Chinese literature. The gray literature search was conducted for commercially available tools. Original studies that focused on clinical decision support tools driven by artificial intelligence or information technology in China and were published between 2010 and February 2022 were included. Information extracted from each article was further synthesized by themes based on three types of clinical decision‐making.ResultsA total of 37 peer‐reviewed publications and 13 commercially available tools were included in the final analysis. Among them, 32.0% were developed for disease diagnosis, 54.0% for risk prediction and classification, and 14.0% for disease management. Chronic diseases were the most popular therapeutic areas of exploration, with particular emphasis on cardiovascular and cerebrovascular diseases. Single‐center electronic medical records were the mainstream data sources leveraged to inform clinical decision‐making, with internal validation being predominately used for model evaluation.ConclusionsTo effectively promote the extensive use of real‐world data and drive a paradigm shift in clinical decision‐making in China, multidisciplinary collaboration of key stakeholders is urgently needed.

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“…Therefore, the first stage of etiology screening is very important. If the etiology of a FUO can be classified into one category, no matter the disease that caused the FUO, the direction of diagnosis can be determined, which is of great significance to physicians (16,17). Previous studies of classic FUO have focused on the etiology, prognosis, or diagnosis of classic FUO (18,19).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies of classic FUO have focused on the etiology, prognosis, or diagnosis of classic FUO (18,19). So far, few researchers have studied the etiological causes of classic FUO from the perspective of clinical prediction models and machine learning (ML) (16). In recent years, ML has been widely used in the medical field and has achieved good results in disease diagnosis, risk assessment, and other factors (20)(21)(22).…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin

Yan

Chen

Liu

et al. 2021

Front. Public Health

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Background: The etiology of fever of unknown origin (FUO) is complex and remains a major challenge for clinicians. This study aims to investigate the distribution of the etiology of classic FUO and the differences in clinical indicators in patients with different etiologies of classic FUO and to establish a machine learning (ML) model based on clinical data.Methods: The clinical data and final diagnosis results of 527 patients with classic FUO admitted to 7 medical institutions in Chongqing from January 2012 to August 2021 and who met the classic FUO diagnostic criteria were collected. Three hundred seventy-three patients with final diagnosis were divided into 4 groups according to 4 different etiological types of classical FUO, and statistical analysis was carried out to screen out the indicators with statistical differences under different etiological types. On the basis of these indicators, five kinds of ML models, i.e., random forest (RF), support vector machine (SVM), Light Gradient Boosting Machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models, were used to evaluate all datasets using 5-fold cross-validation, and the performance of the models were evaluated using micro-F1 scores.Results: The 373 patients were divided into the infectious disease group (n = 277), non-infectious inflammatory disease group (n = 51), neoplastic disease group (n = 31), and other diseases group (n = 14) according to 4 different etiological types. Another 154 patients were classified as undetermined group because the cause of fever was still unclear at discharge. There were significant differences in gender, age, and 18 other indicators among the four groups of patients with classic FUO with different etiological types (P < 0.05). The micro-F1 score for LightGBM was 75.8%, which was higher than that for the other four ML models, and the LightGBM prediction model had the best performance.Conclusions: Infectious diseases are still the main etiological type of classic FUO. Based on 18 statistically significant clinical indicators such as gender and age, we constructed and evaluated five ML models. LightGBM model has a good effect on predicting the etiological type of classic FUO, which will play a good auxiliary decision-making function.

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Exploring Fever of Unknown Origin Intelligent Diagnosis Based on Clinical Data: Model Development and Validation

Cited by 7 publications

References 31 publications

Transfer learning for non-image data in clinical research: A scoping review

Transfer learning for non-image data in clinical research: A scoping review

Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review

Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin

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