Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States of America.
VHA Boston CSP Informatics, Department of Veterans Affairs, Boston, MA, United States of America.
PLoS One. 2023 Jan 25;18(1):e0280931. doi: 10.1371/journal.pone.0280931. eCollection 2023.
Natural language processing of medical records offers tremendous potential to improve the patient experience. Sentiment analysis of clinical notes has been performed with mixed results, often highlighting the issue that dictionary ratings are not domain specific. Here, for the first time, we re-calibrate the labMT sentiment dictionary on 3.5M clinical notes describing 10,000 patients diagnosed with lung cancer at the Department of Veterans Affairs. The sentiment score of notes was calculated for two years after date of diagnosis and evaluated against a lab test (platelet count) and a combination of data points (treatments). We found that the oncology specific labMT dictionary, after re-calibration for the clinical oncology domain, produces a promising signal in notes that can be detected based on a comparative analysis to the aforementioned parameters.
病历的自然语言处理具有极大改善患者体验的潜力。对临床记录的情感分析取得了不同的结果，往往突出了词典评分不是特定于领域的问题。在这里，我们首次在 350 万份描述 10000 名退伍军人事务部肺癌患者的临床记录上重新校准了 labMT 情感词典。在诊断日期后的两年内计算了记录的情感评分，并与实验室测试（血小板计数）和数据点组合（治疗）进行了评估。我们发现，经过临床肿瘤学领域的重新校准，肿瘤学专用的 labMT 词典在可以根据与上述参数的比较分析检测到的记录中产生了有希望的信号。
