simpleNomo: A Python Package of Making Nomograms for Visualizable Calculation of Logistic Regression Models

Hong, Helen; Hong, Shenda

doi:10.34133/hds.0023

Cited by 9 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To facilitate clinical application, a nomogram visually depicting the risk of thyroid nodules was developed using the simpleNomo package in Python ( 36 ). A calibration curve was utilized to measure the consistency between the predicted risks and the actual outcomes.…”

Section: Methodsmentioning

confidence: 99%

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography

Liang,

Qi,

2024

Front. Endocrinol.

View full text Add to dashboard Cite

IntroductionUltrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography in routine screening. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to leverage the ML approach in assessing the risk of thyroid nodules based on common clinical features.MethodsData were sourced from a Chinese cohort undergoing routine physical examinations including thyroid ultrasonography between 2013 and 2023. Models were established to predict the 3-year risk of thyroid nodules based on patients’ baseline characteristics and laboratory tests. Four ML algorithms, including logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine, were trained and tested using fivefold cross-validation. The importance of each feature was measured by the permutation score. A nomogram was established to facilitate risk assessment in the clinical settings.ResultsThe final dataset comprised 4,386 eligible subjects. Thyroid nodules were detected in 54.8% (n=2,404) individuals within the 3-year observation period. All ML models significantly outperformed the baseline regression model, successfully predicting the occurrence of thyroid nodules in approximately two-thirds of individuals. Age, high-density lipoprotein, fasting blood glucose and creatinine levels exhibited the highest impact on the outcome in these models. The nomogram showed consistency and validity, providing greater net benefits for clinical decision-making than other strategies.ConclusionThis study demonstrates the viability of an ML-based approach in predicting the occurrence of thyroid nodules. The findings highlight the potential of ML models in identifying high-risk individuals for personalized screening, thereby guiding the judicious use of ultrasound in this context.

show abstract

Section: Methodsmentioning

confidence: 99%

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography

Liang,

Qi,

2024

Front. Endocrinol.

View full text Add to dashboard Cite

show abstract

“…For instance, screening requires higher sensitivity to avoid missing potential positive cases and delay in treatment, while diagnosis requires higher specificity to avoid unnecessary treatment that could cause harm. 114 Additionally, it is imperative to systematically develop strategies and workflows to facilitate collaboration between humans (clinicians) and machines (AI-assisted diagnostic systems). As a tool, AI models can only be effective when being utilized properly by humans.…”

Section: Realization Of Ai In Vertical Areasmentioning

confidence: 99%

Artificial intelligence for medicine: Progress, challenges, and perspectives

Huang,

Xu,

Wang

et al. 2023

TIME

Self Cite

View full text Add to dashboard Cite

<p>Artificial Intelligence (AI) has transformed how we live and how we think, and it will change how we practice medicine. With multimodal big data, we can develop large medical models that enables what used to unimaginable, such as early cancer detection several years in advance and effective control of virus outbreaks without imposing social burdens. The future is promising, and we are witnessing the advancement. That said, there are challenges that cannot be overlooked. For example, data generated is often isolated and difficult to integrate from both perspectives of data ownership and fusion algorithms. Additionally, existing AI models are often treated as black boxes, resulting in vague interpretation of the results. Patients also exhibit a lack of trust to AI applications, and there are insufficient regulations to protect patients�� privacy and rights. However, with the advancement of AI technologies, such as more sophisticated multimodal algorithms and federated learning, we may overcome the barriers posed by data silos. Deeper understanding of human brain and network structures can also help to unravel the mysteries of neural networks and construct more transparent yet more powerful AI models. It has become something of a trend that an increasing number of clinicians and patients will implement AI in their life and medical practice, which in turn can generate more data and improve the performance of models and networks. Last but not the least, it is crucial to monitor the practice of AI in medicine and ensure its equity, security, and responsibility.</p>

show abstract

Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning

Xu,

Li,

Zhang

et al. 2023

Health Inf Sci Syst

View full text Add to dashboard Cite

simpleNomo: A Python Package of Making Nomograms for Visualizable Calculation of Logistic Regression Models

Cited by 9 publications

References 17 publications

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography

Artificial intelligence for medicine: Progress, challenges, and perspectives

Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning

Contact Info

Product

Resources

About