Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China

Digital intelligence technology: new quality productivity for precision traditional Chinese medicine

Traditional Chinese medicine is a complex medical system characterized by multiple metabolites, targets, and pathways, known for its low drug resistance and significant efficacy. However, challenges persist within Traditional Chinese medicine, including difficulties in assessing the quality of Botanical drugs, reliance on experiential knowledge for disease diagnosis and treatment, and a lack of clarity regarding the pharmacological mechanisms of Traditional Chinese medicine. The advancement of digital intelligence technology is driving a shift towards precision medicine within the Traditional Chinese medicine model. This transition propels Traditional Chinese medicine into an era of precision, intelligence, and digitalization. This paper introduces standard digital intelligence technologies and explores the application of digital intelligence technologies in quality control and evaluation of Traditional Chinese medicine, studies the research status of digital intelligence technologies in assisting diagnosis, treatment and prevention of diseases, and further promotes the application and development of digital intelligence technologies in the field of Traditional Chinese medicine.

Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review

BACKGROUND AND OBJECTIVES

Having a sufficient sample size is crucial when developing a clinical prediction model. We reviewed details of sample size in studies developing prediction models for binary outcomes using machine learning (ML) methods within oncology and compared the sample size used to develop the models with the minimum required sample size needed when developing a regression-based model (Nmin).

METHODS

We searched the Medline (via OVID) database for studies developing a prediction model using ML methods published in December 2022. We reviewed how sample size was justified. We calculated Nmin, which is the Nmin, and compared this with the sample size that was used to develop the models.

RESULTS

Only one of 36 included studies justified their sample size. We were able to calculate Nmin for 17 (47%) studies. 5/17 studies met Nmin, allowing to precisely estimate the overall risk and minimize overfitting. There was a median deficit of 302 participants with the event (n = 17; range: -21,331 to 2298) when developing the ML models. An additional three out of the 17 studies met the required sample size to precisely estimate the overall risk only.

CONCLUSION

Studies developing a prediction model using ML in oncology seldom justified their sample size and sample sizes were often smaller than Nmin. As ML models almost certainly require a larger sample size than regression models, the deficit is likely larger. We recommend that researchers consider and report their sample size and at least meet the minimum sample size required when developing a regression-based model.

Chinese Medicine in the Era of Artificial Intelligence: Challenges and Development Prospects

Traditional Chinese medicine (TCM) has protected the health of Chinese people for thousands of years. With the rapid development of artificial intelligence (AI), various fields of TCM are facing both opportunities and challenges. This review discusses the development prospects and challenges of Chinese medicine in the AI era, emphasizing that AI, as an important tool in the process of Chinese medicine healthcare services, can assist doctors in making objective, rational and professional treatment decisions, and that AI has a strong potential for development in the field of Chinese medicine. However, the emotions, complex thoughts, and humanistic values of doctors are qualities that AI is currently unable to realize, so as the dominant player, the doctor is indispensable to the medical process. By summarizing and analyzing the current development status of AI in diagnosis, drug research, health management and education in TCM, this paper reveals the development prospects and potential risks of combining TCM with AI, and suggests that AI is an important aid for modernizing and improving the quality of TCM medical care in a coordinated manner.

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning

OBJECTIVE

To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology.

STUDY DESIGN AND SETTING

We conducted a systematic review, searching the MEDLINE database between December 1, 2022, and December 31, 2022, for studies developing a multivariable prognostic model using machine learning methods (as defined by the authors) in oncology. Two authors independently screened records and extracted open science practices.

RESULTS

We identified 46 publications describing the development of a multivariable prognostic model. The adoption of open science principles was poor. Only one study reported availability of a study protocol, and only one study was registered. Funding statements and conflicts of interest statements were common. Thirty-five studies (76%) provided data sharing statements, with 21 (46%) indicating data were available on request to the authors and seven declaring data sharing was not applicable. Two studies (4%) shared data. Only 12 studies (26%) provided code sharing statements, including 2 (4%) that indicated the code was available on request to the authors. Only 11 studies (24%) provided sufficient information to allow their model to be used in practice. The use of reporting guidelines was rare: eight studies (18%) mentioning using a reporting guideline, with 4 (10%) using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis statement, 1 (2%) using Minimum Information About Clinical Artificial Intelligence Modeling and Consolidated Standards Of Reporting Trials-Artificial Intelligence, 1 (2%) using Strengthening The Reporting Of Observational Studies In Epidemiology, 1 (2%) using Standards for Reporting Diagnostic Accuracy Studies, and 1 (2%) using Transparent Reporting of Evaluations with Nonrandomized Designs.

CONCLUSION

The adoption of open science principles in oncology studies developing prognostic models using machine learning methods is poor. Guidance and an increased awareness of benefits and best practices of open science are needed for prediction research in oncology.

Machine learning in TCM with natural products and molecules: current status and future perspectives

Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.