An interpretable machine learning method for heart beat classification / 中国胸心血管外科临床杂志

@#Objective    To explore the application of Tsetlin Machine (TM) in heart beat classification. Methods    TM was used to classify the normal beats, premature ventricular contraction (PVC) and supraventricular premature beats (SPB) in the 2020 data set of China Physiological Signal Challenge. This data set consisted of the single-lead electro-cardiogram data of 10 patients with arrhythmia. One patient with atrial fibrillation was excluded, and finally data of the other 9 patients were included in this study. The classification results were then analyzed. Results    The classification results showed that the average recognition accuracy of TM was 84.3%, and the basis of classification could be shown by the bit pattern interpretation diagram. Conclusion    TM can explain the classification results when classifying heart beats. The reasonable interpretation of classification results can increase the reliability of the model and facilitate people's review and understanding.

An interpretable machine learning-based prediction model for risk of death for patients with ischemic stroke in intensive care unit / 南方医科大学学报

OBJECTIVE@#To construct an inherent interpretability machine learning model as an explainable boosting machine model (EBM) for predicting one-year risk of death in patients with severe ischemic stroke.@*METHODS@#We randomly divided the data of 2369 eligible patients with severe ischemic stroke in the MIMIC-Ⅳ(2.0) database, who were admitted in ICU in 2008 to 2019, into a training dataset (80%) and a test dataset (20%), and assessed the prognosis of the patients using the EBM model. The prediction performance of the model was evaluated by calculating the area under the receiver operating characteristic (AUC) curve. The calibration curve and Brier score were used to evaluate the degree of calibration of the model, and a decision curve was generated to assess the net clinical benefit.@*RESULTS@#The EBM model constructed in this study had good discrimination power, calibration and net benefit, with an AUC of 0.857 (95% CI: 0.831-0.887) for predicting prognosis of severe ischemic stroke. Calibration curve analysis showed that the standard curve of the EBM model was the closest to the ideal curve. Decision curve analysis showed that the model had the greatest net benefit rate at the prediction probability threshold of 0.10 to 0.80. The top 5 independent predictive variables based on the EBM model were age, SOFA score, mean heart rate, mechanical ventilation, and mean respiratory rate, whose significance scores ranged from 0.179 to 0.370.@*CONCLUSION@#This EBM model has a good performance for predicting the risk of death within one year in patients with severe ischemic stroke and allows clinicians to better understand the contributing factors of the patients' outcomes through the model interpretability.

Clinical decision support system based on explainable artificial intelligence?brain of Mengchao liver disease / 中华消化外科杂志

In recent years, the artificial intelligence machine learning and deep learning technology have made leap progress. Using clinical decision support system for auxiliary diagnosis and treatment is the inevitable developing trend of wisdom medical. Clinicians tend to ignore the interpretability of models while pursuing its high accuracy, which leads to the lack of trust of users and hamper the application of clinical decision support system. From the perspective of explainable artificial intelligence, the authors make some preliminary exploration on the construction of clinical decision support system in the field of liver disease. While pursuing high accuracy of the model, the data governance techniques, intrinsic interpretability models, post-hoc visualization of complex models, design of human-computer interactions, providing knowledge map based on clinical guidelines and data sources are used to endow the system with interpretability.

Research advance and challenges of the application of machine learning in laboratory medicine / 中华检验医学杂志

The application of machine learning has become an important direction for the development of intelligent laboratory medicine. Recently, the rapid development of open-source software and publicly available data sources made the application of machine learning highly accessible. It reduced the requirement for developers to have necessary matter knowledge and also facilitated a surge in interest and publications. However, the practicality and reproducibility of machine learning models still remain unclear. In the face of these challenges, some countermeasures were proposed, including strict control of data quality, improvemrnt of model applicability, establishment of model selction and validation strategies, enhancement of model interpretability and reproducibility. Machine learning helps to break through the bottleneck of clinical translation of laboratory big data and improve the quality of diagnostic services in the laboratory medicine.

Temporalidade e Interpretabilidade na Análise de Narrativas / Temporality and Interpretability: Analysis of Narratives

Resumo Destacamos a importância de manter uma consistência vertical nos estudos que versam sobre narrativas para a compreensão da psique humana. Nessa direção, os princípios da temporalidade e sua íntima relação com a interpretabilidade, propostos por autores clássicos como Bruner, Polkinghorne, Ricoeur e Sarbin, são tomados como guias na manutenção dessa consistência. Para ilustrá-la, analisamos duas narrativas versando sobre temas diversos: experiência escolar e supervisão acadêmica em psicoterapia. Na primeira, os processos de segmentação e encadeamento do tempo engajado no mundo da ação constituem elementos-chave de análise na significação da experiência escolar. Na segunda, o interdiscurso entre a estagiária e a supervisora se interliga e cria significado pela própria inter-relação entre tempos narrativos diversos.

Abstract This paper highlights the importance of maintaining vertical consistence in narrative studies investigating human psyche. The principles of temporality and interpretability, proposed by classic authors as Bruner, Polkinghorne, Ricoeur e Sarbin, are taken as guidelines to maintain this consistence. To illustrate this idea, two narratives dealing with different themes are analyzed: one referring to school experience and the other to academic supervision of psychotherapy. For the first one the processes of segmentation and entanglement of the engaging time in the world of action compose the key-elements to analyze the meaning of school experience. For the second, different narrating times - one of the trainee and the other of the supervisor - create an interdiscourse from which meaning emerges as a consequence of the interrelationships of diverse narrating times.

Uma análise das evidências de validade brasileiras do PMK / An analysis of the evidences of Brazilian validities about the PMK

Tendo em vista a proposta e a quantidade de medidas provenientes do PMK, foram colocados como objeto de análise os estudos de evidência de validade fornecidos pelo Manual. De fato, o interesse é buscar os fundamentos da interpretabilidade dos resultados de um instrumento com uma pretensão dessa magnitude. Como resultado, encontrou-se que as análises fatoriais realizadas não fornecem subsídios para a estrutura proposta para a interpretação das distintas medidas do PMK. Ao lado disso, os estudos com grupo-critério apresentados é, quando muito, incipiente e não fornece evidências de interpretabilidade para quase nenhum dos denominados seis fatores. As evidências apresentadas são confusas, há incoerências entre correlação e análise de variância e a interpretabilidade do teste não fica demonstrada pelas pesquisas.

Considering the proposal and amount of measures concerning with the PMK, the analysis of the studies of evidence of validity supplied by the Manual were placed under investigation. In fact, the interest is to search for foundations of the interpretability of the results of a test with a pretentiousness of this magnitude. As a result, the factorial analyzes do not support the proposed structure for the interpretation of the different measures of the PMK. Besides, the presented studies with criterion group were as much as incipient and do not furnish evidences of interpretability for almost none of the named six factors. The showed evidences are confusing, there are incoherencies between the concept of correlation and variance analysis and the interpretability of the test is not demonstrated by the researches.