Care-needs level prediction for elderly long-term care using insurance claims data

Background and objective: Owing to an aging population, the increase in the number of elderly people certified as requiring long-term care has become a critical social issue in Japan. This study aimed to construct a machine learning model predicting the maximum care-needs level required for long-term care within the next three years for persons aged over 75 years. Methods: The prediction model was constructed using features extracted from long-term care and healthcare insurance claims data. The study subjects were a total of 47,862 elderly individuals who had not received long-term care services in a large city in Japan. The prediction classes for outcome variable were categorized according to the criteria of the Japanese long-term care system: class 0 (no required), class 1 (support levels 1 and 2), class 2 (care levels 1 and 2), and class 3 (care levels 3–5). As explanatory variables, a total of 516 features were used, including age, sex, and 514 diseases classified under ICD-10. In this study, we focused on constructing a prediction model with the interpretability and adopted multinomial logistic regression (MLR) with L2 regularization as a machine learning algorithm. MLR allowed us to identify the characteristics influencing each prediction class of care-needs levels. Results: In terms of overall predictive performance, MLR achieved weighted average precision, recall, F-value, and lift scores of 0.694, 0.505, 0.567, and 1.333, respectively. Compared to other machine learning algorithms, MLR demonstrated comparable performance to Support Vector Machine (SVM) and Random Forest (RF). From the factor analysis based on the magnitudes of coefficients of the MLR model, the top three features influencing each prediction class were as follows: class1: female sex, hypertension, and gonarthrosis; class 2: age, Alzheimer-type dementia, and neuromuscular dysfunction of the bladder; class 3: age, Alzheimer-type dementia, and type 2 diabetes mellitus. Conclusions: In practical terms, the care-needs level prediction can be applied by local governments to identify high-risk areas by comprehensively and routinely predicting insured persons under public health insurance and long-term care insurance systems

Development of a Prediction Method of Cell Density in Autotrophic/Heterotrophic Microorganism Mixtures by Machine Learning Using Absorbance Spectrum Data

Microflora is actively used to produce value-added materials in industry, and each cell density should be controlled for stable microflora use. In this study, a simple system evaluating the cell density was constructed with artificial intelligence (AI) using the absorbance spectra data of microflora. To set up the system, the prediction system for cell density based on machine learning was constructed using the spectra data as the feature from the mixture of Saccharomyces cerevisiae and Chlamydomonas reinhardtii. As the results of predicting cell density by extremely randomized trees, when the cell densities of S. cerevisiae and C. reinhardtii were shifted and fixed, the coefficient of determination (R2) was 0.8495; on the other hand, when the cell densities of S. cerevisiae and C. reinhardtii were fixed and shifted, the R2 was 0.9232. To explain the prediction system, the randomized trees regressor of the decision tree-based ensemble learning method as the machine learning algorithm and Shapley additive explanations (SHAPs) as the explainable AI (XAI) to interpret the features contributing to the prediction results were used. As a result of the SHAP analyses, not only the optical density, but also the absorbance of the Soret and Q bands derived from the chloroplasts of C. reinhardtii could contribute to the prediction as the features. The simple cell density evaluating system could have an industrial impact

Antigen–Antibody Interactions and Structural Flexibility of a Femtomolar-Affinity Antibody

The femtomolar-affinity mutant antibody (4M5.3) generated by directed evolution is interesting because of the potential of antibody engineering. In this study, the mutant and its wild type (4-4-20) were compared in terms of antigen–antibody interactions and structural flexibility to elucidate the effects of directed evolution. For this purpose, multiple steered molecular dynamics (SMD) simulations were performed. The pulling forces of SMD simulations elucidated the regions that form strong attractive interactions in the binding pocket. Structural analysis in these regions showed two important mutations for improving attractive interactions. First, mutation of Tyr102­(H) to Ser (sequence numbering of Protein Data Bank entry 1FLR) played a role in resolving the steric hindrance on the pathway of the antigen in the binding pocket. Second, mutation of Asp31­(H) to His played a role in resolving electrostatic repulsion. Potentials of mean force (PMFs) of both the wild type and the mutant showed landscapes that do not include obvious intermediate states and go directly to the bound state. These landscapes were regarded as funnel-like binding free energy landscapes. Furthermore, the structural flexibility based on the fluctuations of the positions of atoms was analyzed. It was shown that the fluctuations in the positions of the antigen and residues in contact with antigen tend to be smaller in the mutant than in the wild type. This result suggested that structural flexibility decreases as affinity is improved by directed evolution. This suggestion is similar to the relationship between affinity and flexibility for in vivo affinity maturation, which was suggested by Romesberg and co-workers [Jimenez, R., et al. (2003) <i>Proc. Natl. Acad. Sci. U.S.A.</i> <i>100</i>, 92–97]. Consequently, the relationship was found to be applicable up to femotomolar affinity levels