Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/sym16020223

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Jmir AI  

Background: Although machine learning is a promising tool for making prognoses, the performance of machine learning in predicting outcomes after stroke remains to be examined. Objective: This study aims to examine how much data-driven models with machine learning improve predictive performance for poststroke outcomes compared with conventional stroke prognostic scores and to elucidate how explanatory variables in machine learning–based models differ from the items of the stroke prognostic scores. Methods: We used data from 10,513 patients who were registered in a multicenter prospective stroke registry in Japan between 2007 and 2017. The outcomes were poor functional outcome (modified Rankin Scale score >2) and death at 3 months after stroke. Machine learning–based models were developed using all variables with regularization methods, random forests, or boosted trees. We selected 3 stroke prognostic scores, namely, ASTRAL (Acute Stroke Registry and Analysis of Lausanne), PLAN (preadmission comorbidities, level of consciousness, age, neurologic deficit), and iScore (Ischemic Stroke Predictive Risk Score) for comparison. Item-based regression models were developed using the items of these 3 scores. The model performance was assessed in terms of discrimination and calibration. To compare the predictive performance of the data-driven model with that of the item-based model, we performed internal validation after random splits of identical populations into 80% of patients as a training set and 20% of patients as a test set; the models were developed in the training set and were validated in the test set. We evaluated the contribution of each variable to the models and compared the predictors used in the machine learning–based models with the items of the stroke prognostic scores. Results: The mean age of the study patients was 73.0 (SD 12.5) years, and 59.1% (6209/10,513) of them were men. The area under the receiver operating characteristic curves and the area under the precision-recall curves for predicting poststroke outcomes were higher for machine learning–based models than for item-based models in identical populations after random splits. Machine learning–based models also performed better than item-based models in terms of the Brier score. Machine learning–based models used different explanatory variables, such as laboratory data, from the items of the conventional stroke prognostic scores. Including these data in the machine learning–based models as explanatory variables improved performance in predicting outcomes after stroke, especially poststroke death. Conclusions: Machine learning–based models performed better in predicting poststroke outcomes than regression models using the items of conventional stroke prognostic scores, although they required additional variables, such as laboratory data, to attain improved performance. Further studies are warranted to validate the usefulness of machine learning in clinical settings.

DOI： 10.2196/46840

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Studies in Health Technology and Informatics  

Delirium is common in the emergency department, and once it develops, there is a risk of self-extubation of drains and tubes, so it is critical to predict delirium before it occurs. Machine learning was used to create two prediction models in this study: one for predicting the occurrence of delirium and one for predicting self-extubation after delirium. Each model showed high discriminative performance, indicating the possibility of selecting high-risk cases. Visualization of predictors using Shapley additive explanation (SHAP), a machine learning interpretability method, showed that the predictors of delirium were different from those of self-extubation after delirium. Data-driven decisions, rather than empirical decisions, on whether or not to use physical restraints or other actions that cause patient suffering will result in improved value in medical care.

DOI： 10.3233/SHTI231115

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/bioengineering10121383

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Jmir Perioperative Medicine  

Background: Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications. Objective: The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model. Methods: The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method. Results: A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept –0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance. Conclusions: The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.

DOI： 10.2196/50895

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Scientific Reports  

There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This study enrolled 1049 patients with pathological stage I–IIIA surgically resected NSCLC at Kyushu University. We set 17 clinicopathological factors and 30 preoperative and 22 postoperative blood test results as explanatory variables. Disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS) were set as objective variables. The eXtreme Gradient Boosting (XGBoost) was used as the machine learning algorithm. The median age was 69 (23–89) years, and 605 patients (57.7%) were male. The numbers of patients with pathological stage IA, IB, IIA, IIB, and IIIA were 553 (52.7%), 223 (21.4%), 100 (9.5%), 55 (5.3%), and 118 (11.2%), respectively. The 5-year DFS, OS, and CSS rates were 71.0%, 82.8%, and 88.7%, respectively. Our AI prognostic model showed that the areas under the curve of the receiver operating characteristic curves of DFS, OS, and CSS at 5 years were 0.890, 0.926, and 0.960, respectively. The AI prognostic model using XGBoost showed good prediction accuracy and provided accurate predictive probability of postoperative prognosis of NSCLC.

DOI： 10.1038/s41598-023-42964-8

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Scientific Reports  

This study aimed to determine whether body weight is associated with functional outcome after acute ischemic stroke. We measured the body mass index (BMI) and assessed clinical outcomes in patients with acute ischemic stroke. The BMI was categorized into underweight (< 18.5 kg/m²), normal weight (18.5–22.9 kg/m²), overweight (23.0–24.9 kg/m²), and obesity (≥ 25.0 kg/m²). The association between BMI and a poor functional outcome (modified Rankin Scale [mRS] score: 3–6) was evaluated. We included 11,749 patients with acute ischemic stroke (70.3 ± 12.2 years, 36.1% women). The risk of a 3-month poor functional outcome was higher for underweight, lower for overweight, and did not change for obesity in reference to a normal weight even after adjusting for covariates by logistic regression analysis. Restricted cubic splines and SHapley Additive exPlanation values in eXtreme Gradient Boosting model also showed non-linear relationships. Associations between BMI and a poor functional outcome were maintained even after excluding death (mRS score: 3–5) or including mild disability (mRS score: 2–6) as the outcome. The associations were strong in older patients, non-diabetic patients, and patients with mild stroke. Body weight has a non-linear relationship with the risk of a poor functional outcome after acute ischemic stroke.

DOI： 10.1038/s41598-023-35894-y

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Medicine United States  

The turnover of kindergarten teachers has drastically increased in the past 10 years. Reducing the turnover rates among preschool workers has become an important issue worldwide. Parents have avoided enrolling children in preschools due to insufficient care, which affects their ability to work. Therefore, this study developed a diagnostic model to understand preschool workers' unwillingness to continue working. A total of 1002 full-time preschool workers were divided into 2 groups. Predictors were drawn from general questionnaires, including those for mental health. We compared 3 algorithms: the least absolute shrinkage and selection operator, eXtreme Gradient Boosting, and logistic regression. Additionally, the SHapley Additive exPlanation was used to visualize the relationship between years of work experience and intention to continue working. The logistic regression model was adopted as the diagnostic model, and the predictors were "not living with children," "human relation problems with boss," "high risk of mental distress," and "work experience." The developed risk score and the optimal cutoff value were 14 points. By using the diagnostic model to determine workers' unwillingness to continue working, supervisors can intervene with workers who are experiencing difficulties at work and can help resolve their problems.

DOI： 10.1097/MD.0000000000032630

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PubMed

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Applied Sciences Switzerland  

Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML interpretation method) presents the results of machine learning predictions and promotes guided decisions. This study focuses on visualizing the predictors of delirium using a ML interpretation method and implementing the analysis results in clinical practice. Retrospective data of 55,389 patients hospitalized in a single acute care center in Japan between December 2017 and February 2022 were collected. Patients were categorized into three analysis populations, according to inclusion and exclusion criteria, to develop delirium prediction models. The predictors were then visualized using Shapley additive explanation (SHAP) and fed back to clinical practice. The machine learning-based prediction of delirium in each population exhibited excellent predictive performance. SHAP was used to visualize the body mass index and albumin levels as critical contributors to delirium prediction. In addition, the cutoff value for age, which was previously unknown, was visualized, and the risk threshold for age was raised. By using the SHAP method, we demonstrated that data-driven decision support is possible using electronic medical record data.

DOI： 10.3390/app13031564

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Computer Methods and Programs in Biomedicine  

Background and Objective: When using machine learning techniques in decision-making processes, the interpretability of the models is important. In the present paper, we adopted the Shapley additive explanation (SHAP), which is based on fair profit allocation among many stakeholders depending on their contribution, for interpreting a gradient-boosting decision tree model using hospital data. Methods: For better interpretability, we propose two novel techniques as follows: (1) a new metric of feature importance using SHAP and (2) a technique termed feature packing, which packs multiple similar features into one grouped feature to allow an easier understanding of the model without reconstruction of the model. We then compared the explanation results between the SHAP framework and existing methods using cerebral infarction data from our hospital. Results: The interpretation by SHAP was mostly consistent with that by the existing methods. We showed how the A/G ratio works as an important prognostic factor for cerebral infarction using proposed techniques. Conclusion: Our techniques are useful for interpreting machine learning models and can uncover the underlying relationships between features and outcome.

DOI： 10.1016/j.cmpb.2021.106584

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1002/lrh2.10223

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1002/lrh2.10252

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1161/STROKEAHA.119.027300

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

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Country：Japan  

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Country：China  

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Country：United States  

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Country：Japan  

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Country：Taiwan, Province of China  

Language：English   Presentation type：Oral presentation (general)  

Country：Australia  

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Language：Japanese   Presentation type：Symposium, workshop panel (public)  

Country：Japan  

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