Filtering by
Contraceptive methods are vital in maintaining women’s health and preventing unintended pregnancy. When a woman uses a method that reflects her personal preferences and lifestyle, the chances of low adoption and misuse decreases. The research aim of this project is to develop a web-based decision aid tailored to college women that assists in the selection of contraceptive methods. For this reason, My Contraceptive Choice (MCC) is built using the gaps identified in existing resources provided by Planned Parenthood and Bedsider, along with feedback from a university student focus group. The tool is a short quiz that is followed by two pages of information and resources for a variety of different contraceptive methods commonly used by college women. The evaluation phase of this project includes simulated test cases, a Google Forms survey, and a second focus group to assess the tool for accuracy and usability. From the survey, 130 of the 150 (80.7%) responses believe that the recommendations provided can help them select a birth control method. Furthermore, 136 of the 150 (90.0%) responses believe that the layout of the tool made it easy to navigate. The second focus group feedback suggests that the MCC tool is perceived to be accurate, usable, and useful to the college population. Participants believe that the MCC tool performs better overall compared to the Planned Parenthood quiz in creating a customized recommendation and Bedsider in overall usability. The test cases reveal that there are further improvements that could be made to create a more accurate recommendation to the user. In conclusion, the new MCC tool accomplishes the aim of creating a beneficial resource to college women in assisting with the birth control selection process.
further process output from a machine learning based named entity recognition (NER) tool for the purposes of (1) linking references to radiological images with the corresponding clinical findings and (2) extracting primary and incidental findings.
Methods: The project’s system utilized a regular expression to extract image references. All CTPA reports were first processed with NER software to obtain the text and spans of clinical findings. A heuristic was used to determine the appropriate clinical finding that should be linked with a particular image reference. Another regular expression was used to extract primary findings from NER output; the remaining findings were considered incidental. Performance was
assessed against a gold standard, which was based upon a manually annotated version of the CTPA reports used in this project.
Results: Extraction of image references achieved a 100% accuracy. Linkages between these references and exact gold standard spans of the clinical findings achieved a precision of 0.24, a recall of 0.22, and an F1 score of 0.23. Linkages with partial spans of clinical findings as determined by the gold standard achieved a precision of 0.71, a recall of 0.67, and an F1 score of 0.69. Primary and incidental finding extraction achieved a precision of 0.67, a recall of 0.80, and
an F1 score of 0.73.
Discussion: Various elements reduced system performance such as the difficulty of exactly matching the spans of clinical findings from NER output with those found in the gold standard. The heuristic linking clinical findings and image references was especially sensitive to NER false positives and false negatives due to its assumption that the appropriate clinical finding was that which was immediately prior to the image reference. Although the system did not perform as well as hoped, lessons were learned such as the need for clear research methodology and proper gold standard creation; without a proper gold standard, problem scope and system performance cannot be properly assessed. Improvements to the system include creating a more robust heuristic, sifting NER false positives, and training the NER tool used on a dataset of CTPA reports.