PDF(Pubmed)
Abstract：
UNASSIGNED: In recent years, natural language processing (NLP) techniques have progressed, and their application in the medical field has been tested. However, the use of NLP to detect symptoms from medical progress notes written in Japanese, remains limited. We aimed to detect 2 gastrointestinal symptoms that interfere with the continuation of chemotherapy-nausea/vomiting and diarrhea-from progress notes using NLP, and then to analyze factors affecting NLP.
UNASSIGNED: In this study, 200 patients were randomly selected from 5277 patients who received intravenous injections of cytotoxic anticancer drugs at Kagawa University Hospital, Japan, between January 2011 and December 2018. We aimed to detect the first occurrence of nausea/vomiting (Group A) and diarrhea (Group B) using NLP. The NLP performance was evaluated by the concordance with a review of the physicians\' progress notes used as the gold standard.
UNASSIGNED: Both groups showed high concordance: 83.5% (95% confidence interval [CI] 74.1-90.1) in Group A and 97.7% (95% CI 91.3-99.9) in Group B. However, the concordance was significantly better in Group B (P = .0027). There were significantly more misdetection cases in Group A than in Group B (15.3% in Group A; 1.2% in Group B, P = .0012) due to negative findings or past history.
UNASSIGNED: We detected occurrences of nausea/vomiting and diarrhea accurately using NLP. However, there were more misdetection cases in Group A due to negative findings or past history, which may have been influenced by the physicians\' more frequent documentation of nausea/vomiting.
UNASSIGNED: In this study, 200 patients were randomly selected from 5277 patients who received intravenous injections of cytotoxic anticancer drugs at Kagawa University Hospital, Japan, between January 2011 and December 2018. We aimed to detect the first occurrence of nausea/vomiting (Group A) and diarrhea (Group B) using NLP. The NLP performance was evaluated by the concordance with a review of the physicians\' progress notes used as the gold standard.
UNASSIGNED: Both groups showed high concordance: 83.5% (95% confidence interval [CI] 74.1-90.1) in Group A and 97.7% (95% CI 91.3-99.9) in Group B. However, the concordance was significantly better in Group B (P = .0027). There were significantly more misdetection cases in Group A than in Group B (15.3% in Group A; 1.2% in Group B, P = .0012) due to negative findings or past history.
UNASSIGNED: We detected occurrences of nausea/vomiting and diarrhea accurately using NLP. However, there were more misdetection cases in Group A due to negative findings or past history, which may have been influenced by the physicians\' more frequent documentation of nausea/vomiting.
摘要：
未经批准：近年来，自然语言处理(NLP)技术已经取得进展，并对其在医疗领域的应用进行了测试。然而,使用NLP从用日语写的医学进展笔记中检测症状，仍然有限。我们旨在检测2种胃肠道症状，干扰化疗的继续-恶心/呕吐和腹泻-从进展笔记使用NLP，然后分析影响NLP的因素。
未经批准：在这项研究中，从香川大学医院接受静脉注射细胞毒性抗癌药物的5277例患者中随机选择200例，Japan,2011年1月至2018年12月。我们旨在使用NLP检测首次出现的恶心/呕吐（A组）和腹泻（B组）。通过与作为金标准的医生进步记录的一致性来评估NLP性能。
未经证实：两组均显示高度一致性：A组为83.5%（95%置信区间[CI]74.1-90.1），B组为97.7%（95%CI91.3-99.9）。B组的一致性明显更好（P=.0027）。A组的误检病例明显多于B组（A组15.3%；B组1.2%，P=.0012）由于负面发现或过去的历史。
UNASSIGNED：我们使用NLP准确检测了恶心/呕吐和腹泻的发生。然而,A组因阴性结果或既往史而出现更多漏检病例,这可能受到医生更频繁的恶心/呕吐记录的影响。
未经批准：在这项研究中，从香川大学医院接受静脉注射细胞毒性抗癌药物的5277例患者中随机选择200例，Japan,2011年1月至2018年12月。我们旨在使用NLP检测首次出现的恶心/呕吐（A组）和腹泻（B组）。通过与作为金标准的医生进步记录的一致性来评估NLP性能。
未经证实：两组均显示高度一致性：A组为83.5%（95%置信区间[CI]74.1-90.1），B组为97.7%（95%CI91.3-99.9）。B组的一致性明显更好（P=.0027）。A组的误检病例明显多于B组（A组15.3%；B组1.2%，P=.0012）由于负面发现或过去的历史。
UNASSIGNED：我们使用NLP准确检测了恶心/呕吐和腹泻的发生。然而,A组因阴性结果或既往史而出现更多漏检病例,这可能受到医生更频繁的恶心/呕吐记录的影响。
