Application of Neural Network and Cluster Analyses to Differentiate TCM Patterns in Patients With Breast Cancer

Background and Purpose

Pattern differentiation is a critical element of the prescription process for Traditional Chinese Medicine (TCM) practitioners. Application of advanced machine learning techniques will enhance the effectiveness of TCM in clinical practice. The aim of this study is to explore the relationships between clinical features and TCM patterns in breast cancer patients.

Methods

The dataset of breast cancer patients receiving TCM treatment was recruited from a single medical center. We utilized a neural network model to standardize terminologies and address TCM pattern differentiation in breast cancer cases. Cluster analysis was applied to classify the clinical features in the breast cancer patient dataset. To evaluate the performance of the proposed method, we further compared the TCM patterns to therapeutic principles of Chinese herbal medication in Taiwan.

Results

A total of 2,738 breast cancer cases were recruited and standardized. They were divided into 5 groups according to clinical features via cluster analysis. The pattern differentiation model revealed that liver-gallbladder dampness-heat was the primary TCM pattern identified in patients. The main therapeutic goals of the top 10 Chinese herbal medicines prescribed for breast cancer patients were to clear heat, drain dampness, and detoxify. These results demonstrated that the neural network successfully identified patterns from a dataset similar to the prescriptions of TCM clinical practitioners.

Conclusion

This is the first study using machine-learning methodology to standardize and analyze TCM electronic medical records. The patterns revealed by the analyses were highly correlated with the therapeutic principles of TCM practitioners. Machine learning technology could assist TCM practitioners to comprehensively differentiate patterns and identify effective Chinese herbal medicine treatments in clinical practice.

Abstract Representation of Prospective Reward in the Hippocampus

Motivation enhances memory by increasing hippocampal engagement during encoding. However, whether such increased hippocampal activation reflects encoding of the value of highly rewarding events per se is less understood. Here, using a monetary incentive encoding task with a novel manipulation, we tested in humans whether the hippocampus represents abstract reward value, independent of perceptual content. During functional MRI scanning, men and women studied object pairs, each preceded by a monetary reward cue indicating the amount of money they would receive if they successfully remembered the object pair at test. Reward cues varied on both the level of reward (penny, dime, and dollar) and visual form (picture or word) across trials to dissociate hippocampal responses to reward value from those reflecting the perceptual properties of the cues. Behaviorally, participants remembered pairs associated with the high reward (dollar) more often than pairs associated with lower rewards. Neural pattern-similarity analysis revealed that hippocampal and parahippocampal cortex activation patterns discriminated between cues of different value regardless of their visual form, and that hippocampal discrimination of value was most pronounced in participants who showed the greatest behavioral sensitivity to reward. Strikingly, hippocampal patterns were most distinct for reward cues that differed in value but had similar visual appearance, consistent with theoretical proposals of hippocampal-pattern differentiation of competing representations. Our data illustrate how contextual representations within the hippocampus go beyond space and time to include information about the motivational salience of events, with hippocampal reward coding tracking the motivational impact on later memory.SIGNIFICANCE STATEMENT Motivation, such as the promise of future rewards, enhances hippocampal engagement during encoding and promotes successful retention of events associated with valuable rewards. However, whether the hippocampus explicitly encodes reward value, dissociable from sensory information, is unclear. Here, we show that the hippocampus forms abstract representation of valuable rewards, encoding conceptual rather than perceptual information about the motivational context of individual events. Reward representation within the hippocampus is associated with preferential retention of high-value events in memory. Furthermore, we show that hippocampal-pattern differentiation serves to emphasize differences between visually similar events with distinct motivational salience. Collectively, these findings indicate that hippocampal contextual representations enable individuals to distinguish the motivational value of events, leading to prioritized encoding of significant memories.

Guidelines on common cold for traditional Chinese medicine based on pattern differentiation

OBJECTIVE

To establish the guidelines on common cold treated with Traditional Chinese Medicine (TCM) in terms of pattern identification.

METHODS

The guidelines were formulated by using the basic patterns common cold in China Pharmacopeia integrated with findings from systematic literature review and the experts' consensus on the issue in question.

RESULTS

Common cold was divided into four patterns in the guidelines. The medications were recommended respectively: Ganmaoqingre granule for wind-cold exterior syndrome, Yinqiaojiedu granule for wind-heat exterior syndrome, Huoxiangzhengqi Wan for summer-heat dampness exterior syndrome and Shensu Wan for wind-cold exterior syndrome accompanied with Qi deficiency.

CONCLUSION

The guidelines were primarily derived from the practice experience of TCM and the experts' consensus. The process was not strictly evidence-based because of lacking enough clinical studies. Further refinement of the guidelines should be needed as more studies are available.