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Skip search results from other journals and go to results- 2 JMIR Medical Informatics
- 2 JMIR mHealth and uHealth
- 1 JMIR Biomedical Engineering
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- 1 JMIR Serious Games
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The analysis was carried out using the Python programming language. The following libraries were implemented for computational purposes: Pandas [23], Numpy [24], Transformers [25], and Sklearn [26]. Moreover, the Matplotlib [27] library was used for data visualization.
JMIR Serious Games 2024;12:e56663
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The analysis was conducted on an Apple M1 Pro mac OS (version 14.2.1) system, using Python (version 3.10.12). We used several Python libraries for data analysis and visualization: Pandas (version 1.5.3) for data manipulation, Seaborn (version 0.11.2), and Matplotlib (version 3.7.2) for generating insightful plots, and Statannotations (version 0.6.0) to indicate statistical significance in our visual representations.
JMIR Form Res 2024;8:e57592
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To explore the impact of diverse data compression methods, the original files underwent conversion using MH (version 2.2.2), WS (version 15), and FFmpeg (version 6.1.1) in Python (version 3.10.11; Python Software Foundation) on a PC. Three distinct compression algorithms—MP3, M4 A, and WMA—at 2 bitrates—128 kbps and 320 kbps—were applied to simulate real-world scenarios where audio data are commonly subjected to different compression algorithms for storage and transmission purposes.
JMIR Biomed Eng 2024;9:e56246
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Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study
To this end, we used a computer-aided bibliometrics analysis using Python (version 3.11) and the Spyder IDE (version 5.4.3), capitalizing on Python’s capacity to aggregate and analyze medical AI articles indexed in Pub Med since 2000. Using this approach, we systematically parsed the keywords from Pub Med’s AI publication to identify patterns and focal points in medical AI research.
J Med Internet Res 2023;25:e45815
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For example, Leeson et al [51] have shown that conceptual overlaps among the findings of probabilistic TM using the Gensim toolkit in Python, the neural network application Word2 Vec, and open qualitative coding are broad but not uniform [51], demonstrating the value of a “both-and” versus an “either-or” approach to machine- versus human-optimized analyses of UGC. The clearest strength of the “both-and” approach is its ability to analyze very large textual data sets, while preserving important nuance.
JMIR Hum Factors 2022;9(3):e37350
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SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing
However, it is composed of separate modules with i Python notebooks instead of Python libraries, and currently the project seems dormant (the last update was on November 3, 2020). Open-source GENEActiv R macros also exist, even though they are specific to GENEActiv files and would require custom modification to ingest data from other devices.
The lack of open-source, general-purpose algorithms for the processing of the various base activities of daily living is a significant gap in the field.
JMIR Mhealth Uhealth 2022;10(4):e36762
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The objective of this study is to evaluate the feasibility and accuracy of an in-house developed rule-based text-extraction program written in Python to identify patients with lung cancer who developed RP after receiving curative RT. This rule-based text-extraction program written in Python is the first stage of developing a more robust NLP program.
JMIR Med Inform 2021;9(11):e29241
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Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App
All data processing up to this point was performed using Python 3.8 and WPS spreadsheets.
After initial processing with Python, the RF algorithm was applied to the resulting data using the R software package Random Forest. In addition to RF, there are several other machine-learning classification algorithms that could be appropriate for this task, such as naive Bayes [19], logistic regression [20], k-nearest neighbor [21], decision tree [22], and gradient boosting [23].
JMIR Mhealth Uhealth 2021;9(4):e23718
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We used the Python programming language to code our software. Python is one of the most popular programming languages [27], not only because of its simplicity, excellent readability, and powerful functionality, but also because third-party professionals from diverse fields are using it to develop new packages and modules, which are uploaded to a shared repository called the Python package index [28].
JMIR Med Inform 2021;9(1):e22753
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