## 4 Discussion  
With the purpose of shedding light onto the vast amount of clinical-related open source software, our study collected data through the GitHub API and then conducted various analyses to uncover both overall and specific characteristics among GitHub repositories. The analysis methods included a descriptive analysis, trends analysis, and topic clustering. The main findings are summarized as follows:  
Figure 4: Interactive visualization of clinical topic clusters. \begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**\# in Figure 4** & **Topic Label** & **Top Keywords** \\ \hline \hline \multirow{2}{*}{1} & \multirow{2}{*}{Medical images analysis} & Medical images, medical image processing, deep learning, medical image analysis, medical image segmentation \\ \hline \multirow{2}{*}{2} & Applications for medical practice & Android application, medical applications, prescriptions, medical practice, appointments, medical care \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Medical data reuse} & Medical data, website, API, medical services, database, MySQL \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Clinical text processing} & Medical documents, symptoms, medical history, clinical notes, natural language processing (NLP) \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{Application for doctors} & Doctors, applications, doctor appointments, medical professionals, doctor office, nearby doctor, medical staff \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{Medical research} & Medical research, clinical research, clinical data, patient registration, projects, design, clinical decision support, medical informatics \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{Medical education} & Medical students, final project, medical center, project Gutenberg book, experiments, paperwork \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{Healthcare management systems} & Patient management, medical appointments, medical store management, patient monitoring, patient management systems, platform, software \\ \hline \multirow{2}{*}{9} & \multirow{2}{*}{Clinical trials} & Clinical trials, patients, data analysis, new patients, survival analysis \\ \hline \multirow{2}{*}{10} & \multirow{2}{*}{Medical data analysis} & Patient data, medical imaging, machine learning, model, data science, gene expression, predictive model \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ten topics of all clinical GitHub repositories. API: Application programming interface. * The number of repositories and the growth rate indicated an increasing interest, which suggests the potential of utilizing open source software in both academic and practical clinical settings;
* Clinical software on machine learning, medical images, and electronic medical records attract more attention from developers, and deep learning-related repositories are becoming dominant;
* The U.S. is the biggest player in the world, taking up to 38.8%. Among developing countries, India contributes the most, with around 10%;
* Factors that affect the popularity of a GitHub repository include forks and the number of contributors. The README file size and owner type had litter influence. Other factors, such as the number of open issues, the repository size, and the number of commits, are unclear. * The LDA model clustered keywords extracted from repository descriptions into 10 groups, ranging from clinical text processing to medical education. Moreover, analysis of the topics generated by yearly data revealed that topics evolve over time. Besides the above findings, our study contributes to the scientific community by providing a reproducible method of studying GitHub repositories. Over the years, researchers have been struggling with reproducing research results such as literature studies. As mentioned above, both data collection and analysis could be reproduced with a rerun of the Python scripts. Scaling up the approach to other domains requires some additional inputs, such as search terms, and the number of topics for topic modeling.