This case carried out a detailed comparative examination of operation times (decision time and activity time) between cases of differing urgency, in order to evaluate efficiency in the emergency-medical-response setting. In a medical setting that demands prompt and appropriate response, making optimal decisions and taking optimal action within limited time is extremely important, and establishing …
This case applied the multivariate methods of cluster analysis and factor analysis to deeply understand the diversity of disease within a group of patients with a particular cardiovascular disease. In chronic diseases—especially cardiovascular disease—even with the same diagnostic name, symptom profiles, physiological indicators, and responses to treatment often differ greatly from patient to patient. Categorizing …
This case is a statistical analysis of how video teaching materials simulating events that actually occur in the medical setting affect trainees’ “learning effect” and “mental load” in training for healthcare workers. Reconciling improved quality of medical education with learners’ well-being is an important challenge, and evaluating which training modules are most effective while not …
In this case, I visualized the transition from initial diagnosis to final diagnosis by multidisciplinary discussion (MDD) in the diagnostic process for complex respiratory diseases. In a medical setting where diagnostic accuracy and the optimization of patient care are required, this shows how discussion among multiple specialists contributes to confirming the diagnosis. Dr.DataScience illustrated the …
In this case study, at the client’s request, I describe a multifaceted analysis of the factors that influence healthcare workers’ “clinical skill evaluation score” and “patient-care management ability score.” Identifying which factors are truly important from among a wide range of candidate variables, and clarifying their degree of influence, provides insights essential for human-resource development …
In this case, I evaluated the generalizability of a predictive model the client had built. For this important validation, Dr.DataScience used a method called “K-fold cross-validation.” In particular, when the number of data points is limited and many elements (covariates) are used in the model, balancing model training and performance evaluation becomes a very difficult …
In this case study, I describe how Dr.DataScience applied optimal statistical methods within the constraints of a limited sample size and complex data characteristics to extract reliable insights, in response to a client’s wish to evaluate the effectiveness of a complex intervention program (including rehabilitation, learning therapy, music therapy, animal-assisted therapy, etc.) from multiple aspects—cognitive …
In this case study, I describe how Dr.DataScience used a wide range of statistical methods and rigorous data handling to help a client who wished to deeply evaluate, using their data on perioperative treatment for a certain cancer type (e.g., particular pre- and post-operative therapies A and B), how treatment effects and patient characteristics influence …
In this case study, I describe how Dr.DataScience used advanced survival analysis and effective data-visualization methods to help a client who wished to objectively evaluate the treatment effects of two anticancer drugs and, further, to clarify how those effects differ according to patient characteristics (subgroup effects). In cancer treatment, accurately evaluating a drug’s efficacy and …
In this case study, I describe how Dr.DataScience used advanced statistical methods to help a client who wished to clarify, within the diverse medical data they held, the latent associations among multiple symptom groups or laboratory-value groups, or the influence that particular patient attributes or treatment interventions have on clinical outcomes. In the medical setting, …
