肺癌被认为是最危险的癌症之一，其生存率为 5 年，位居全球最致命的三种癌症之列。有效对抗肺癌需要早期发现并及时进行针对性干预。然而，确保早期检测是一项重大挑战，从而催生了创新方法。人工智能的出现为预测癌症提供了革命性的解决方案。在标志着医疗保健领域发生重大转变的同时，增强人工智能模型的必要性仍然是一个焦点，特别是在精准医疗领域。本研究引入了一种混合深度学习模型，结合了卷积神经网络 (CNN) 和双向长短期记忆网络 (BiLSTM)，专为从患者的医疗记录中检测肺癌而设计。与 MIMIC IV 数据集的对比分析揭示了该模型的优越性，MCC 为 96.2%，准确度为 98.1%，优于 LSTM 和 BioBERT，MCC 为 93.5%，准确度为 97.0%，MCC 为 95.5，准确度为分别为 98.0%。另一项全面比较是使用 Yelp Review Polarity 数据集与最新结果进行的。值得注意的是，我们的模型明显优于比较模型，展示了其卓越的性能和在该领域的潜在影响。这项研究标志着向精确和早期肺癌检测迈出的重大一步，强调了精准医学中人工智能模型细化的持续必要性。

Lung cancer is considered one of the most dangerous cancers, with a 5-year survival rate, ranking the disease among the top three deadliest cancers globally. Effectively combating lung cancer requires early detection for timely targeted interventions. However, ensuring early detection poses a major challenge, giving rise to innovative approaches. The emergence of artificial intelligence offers revolutionary solutions for predicting cancer. While marking a significant healthcare shift, the imperative to enhance artificial intelligence models remains a focus, particularly in precision medicine. This study introduces a hybrid deep learning model, incorporating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), designed for lung cancer detection from patients' medical notes. Comparative analysis with the MIMIC IV dataset reveals the model's superiority, achieving an MCC of 96.2% with an Accuracy of 98.1%, and outperforming LSTM and BioBERT with an MCC of 93.5 %, an accuracy of 97.0% and MCC of 95.5 with an accuracy of 98.0% respectively. Another comprehensive comparison was conducted with state-of-the-art results using the Yelp Review Polarity dataset. Remarkably, our model significantly outperforms the compared models, showcasing its superior performance and potential impact in the field. This research signifies a significant stride toward precise and early lung cancer detection, emphasizing the ongoing necessity for Artificial Intelligence model refinement in precision medicine.