{'fraction_non_alphanumeric': 0.022417153996101363, 'fraction_numerical': 0.01949317738791423, 'mean_word_length': 5.583333333333333, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Automated plan-recognition of chemotherapy protocols.

Cancer patients are often treated with multiple sequential chemotherapy protocols ranging in complexity from simple to highly complex patterns of multiple repeating drugs. Clinical documentation procedures that focus on details of single drug events, however, make it difficult for providers and systems to efficiently abstract the sequence and nature of treatment protocols. We have developed a data driven method for cancer treatment plan recognition that takes as input pharmacy chemotherapy dispensing records and produces the sequence of identified chemotherapy protocols. Compared to a manually annotated gold standard, our method was 75% accurate and 80% precise for a breast cancer testing set (110 patients, 2,029 drug events), and 54% accurate and 63% precise for a lung cancer testing set (53 patients, 670 drug events). This method for cancer treatment plan recognition may provide clinicians and systems an abstracted view of the patient's treatment history.