Fang, ShiaofenZhu, ZitongBen Miled, ZinaXia, YuniZheng, Jiangyu2023-08-312023-08-312023-08https://hdl.handle.net/1805/35289IUPUIMachine learning (ML) models can help improve health care services. However, they need to be practical to gain wide adoption. A methodology is proposed in this study to evaluate the utility of different data modalities and cohort segmentation strategies when designing these models. The methodology is used to compare models that predict emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications and cohort segmentation is based on age group and disease severity. The proposed methodology is applied to models developed using a cohort of insomnia patients and a cohort of general non- insomnia patients under different data modalities and segmentation strategies. All models are evaluated using the traditional intra-cohort testing. In addition, to establish the need for disease- specific segmentation, transfer testing is recommended where the same insomnia test patients used for intra-cohort testing are submitted to the general-patient model. The results indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. For insomnia patients, the best ED and IH models using both data modalities or either one of the modalities achieved an area under the receiver operating curve (AUC) of 0.71 and 78, respectively. Our results also show that an insomnia-specific model is not necessary when predicting future ED visits but may have merit when predicting IH visits. As such, we recommend the evaluation of disease-specific models using transfer testing. Based on these initial findings, a language model was pretrained using diagnosis codes. This model can be used for the prediction of future ED and IH visits for insomnia and non-insomnia patients.en-USCC0 1.0 UniversalInsomniaMachine learningHealthThesis