RESUMEN
BACKGROUND: Hypospadias remains the most prevalent congenital abnormality in boys worldwide. However, the limited infrastructure and number of pediatric urologists capable of diagnosing and managing the condition hinder the management of hypospadias in Indonesia. The use of artificial intelligence and image recognition is thought to be beneficial in improving the management of hypospadias cases in Indonesia. OBJECTIVE: We aim to develop and validate a digital pattern recognition system and a mobile app based on an artificial neural network to determine various parameters of hypospadias. METHODS: Hypospadias and normal penis images from an age-matched database will be used to train the artificial neural network. Images of 3 aspects of the penis (ventral, dorsal, and lateral aspects, which include the glans, shaft, and scrotum) will be taken from each participant. The images will be labeled with the following hypospadias parameters: hypospadias status, meatal location, meatal shape, the quality of the urethral plate, glans diameter, and glans shape. The data will be uploaded to train the image recognition model. Intrarater and interrater analyses will be performed, using the test images provided to the algorithm. RESULTS: Our study is at the protocol development stage. A preliminary study regarding the system's development and feasibility will start in December 2022. The results of our study are expected to be available by the end of 2023. CONCLUSIONS: A digital pattern recognition system using an artificial neural network will be developed and designed to improve the diagnosis and management of patients with hypospadias, especially those residing in regions with limited infrastructure and health personnel. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/42853.
RESUMEN
OBJECTIVE: The aim of this study is to compute similarities between patient records in an electronic health record (EHR). This is an important problem because the availability of effective methods for the computation of patient similarity would allow for assistance with and automation of tasks such as patients stratification, medical prognosis and cohort selection, and for unlocking the potential of medical analytics methods for healthcare intelligence. However, health data in EHRs presents many challenges that make the automatic computation of patient similarity difficult; these include: temporal aspects, multivariate, heterogeneous and irregular data, and data sparsity. MATERIALS AND METHODS: We propose a new method for EHR data representation called Temporal Tree: a temporal hierarchical representation which, based on temporal co-occurrence, preserves the compound information found at different levels in health data. In addition, this representation is augmented using the doc2vec embedding technique which here is exploited for patient similarity computation. We empirically investigate our proposed method, along with several state-of-the-art benchmarks, on a dataset of real world Intensive Care Unit (ICU) EHRs, for the task of identifying patients with a specific target diagnosis. RESULTS: Our empirical results show that the Temporal Trees representation is significantly better than other traditional and state-of-the-art methods for representing patients and computing their similarities. CONCLUSION: Temporal trees capture the temporal relationships between medical, hierarchical data: this enables to effectively model the rich information provided within EHRs and thus the identification of similar patients.