JMIR Medical Informatics - Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non–Small Cell Lung Cancer With Electronic Medical Records: Develo

JMIR Medical Informatics - Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non–Small Cell Lung Cancer With Electronic Medical Records: Develo

Background: Lymph node metastasis (LNM) is critical for treatment decision making of patients with resectable non–small cell lung cancer, but it is difficult to precisely diagnose preoperatively. Electronic medical records (EMRs) contain a large volume of valuable information about LNM, but some key information is recorded in free text, which hinders its secondary use. Objective: This study aims to develop LNM prediction models based on EMRs using natural language processing (NLP) and machine learning algorithms. Methods: We developed a multiturn question answering NLP model to extract features about the primary tumor and lymph nodes from computed tomography (CT) reports. We then combined these features with other structured clinical characteristics to develop LNM prediction models using machine learning algorithms. We conducted extensive experiments to explore the effectiveness of the predictive models and compared them with size criteria based on CT image findings (the maximum short

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@iBotamon 非小細胞肺癌のリンパ節転移がpN2以上か否か術前に知りたいとき, カルテや画像の情報をrandom forestに入れるとAUC 0.79で判定でき, これはCTリンパ節短径>10mmや人間の総合的判断より高精度。 さらに画像所見抽出の部分は質問応答モデルを読影レポートに使うと自動化可能 t.co/Tym6zDXTTz

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