Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm

Combining External Medical Knowledge for Improving Obstetric Intelligent Diagnosis: Model Development and Validation

BACKGROUND

Data-driven medical health information processing has become a new development trend in obstetrics. Electronic medical records (EMRs) are the basis of evidence-based medicine and an important information source for intelligent diagnosis. To obtain diagnostic results, doctors combine clinical experience and medical knowledge in their diagnosis process. External medical knowledge provides strong support for diagnosis. Therefore, it is worth studying how to make full use of EMRs and medical knowledge in intelligent diagnosis.

OBJECTIVE

This study aims to improve the performance of intelligent diagnosis in EMRs by combining medical knowledge.

METHODS

As an EMR usually contains multiple types of diagnostic results, the intelligent diagnosis can be treated as a multilabel classification task. We propose a novel neural network knowledge-aware hierarchical diagnosis model (KHDM) in which Chinese obstetric EMRs and external medical knowledge can be synchronously and effectively used for intelligent diagnostics. In KHDM, EMRs and external knowledge documents are integrated by the attention mechanism contained in the hierarchical deep learning framework. In this way, we enrich the language model with curated knowledge documents, combining the advantages of both to make a knowledge-aware diagnosis.

RESULTS

We evaluate our model on a real-world Chinese obstetric EMR dataset and showed that KHDM achieves an accuracy of 0.8929, which exceeds that of the most advanced classification benchmark methods. We also verified the model's interpretability advantage.

CONCLUSIONS

In this paper, an improved model combining medical knowledge and an attention mechanism is proposed, based on the problem of diversity of diagnostic results in Chinese EMRs. KHDM can effectively integrate domain knowledge to greatly improve the accuracy of diagnosis.

The characteristics and regularities of cardiac adverse drug reactions induced by Chinese materia medica: A bibliometric research and association rules analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Chinese materia medica (CMM) has been widely used as an approach of ethnomedicine worldwide. Recently, there are growing concerns related to the potential cardiotoxicity of herbal medicines but comprehensive studies are limited.

METHODS

Comprehensive literature retrieval via publicly available electronic databases was performed to identify the case reports that focused on cardiac adverse reactions (ADRs) triggered by oral CMMs. And a bibliometric survey was conducted to analyze the most commonly suspected risk factors in terms of responsible CMMs, susceptible patients and clinical administration of cardiac ADRs. Moreover, the techniques of data mining were utilized to investigate the regularities and association between the ADRs status and major contributory factors.

RESULTS

The available evidence of current research indicated that many influential factors were strongly associated with cardiac ADRs caused by oral CMMs inevitably, including pediatric patients, poisonous CMMs (especially herbs of Aconitum species), overdose and self-medication. Specifically, the timely and effective resuscitation could attribute their favorable capacity to reduce mortality for cardiac ADRs. Notably, the cardiac ADRs cases had often concomitant the ADRs of the nervous system and digestive system.

CONCLUSION

The comprehensive features and risk factors of cardiac ADRs induced by oral CMMs can be discovered and elucidated through the approaches of bibliometric research, association rules analysis, and data mining technology, which raise the profile and awareness of the rational applications of CMMs and pharmacovigilance within relevant heart side effects.