This paper proposes a method to improve the prediction accuracy of Diagnosis Procedure Combination (DPC) codes in discharge summaries. First, the discharge summary data are formatted uniformly using LLM. Second, Causal Extraction is applied to extract causal information. Third, morphological analysis is performed on the original discharge summary data to create a vector of word features. Fourth, the causal information is used to highlight the features. Fifth, training examples of the classifier are generated. Finally, machine learning methods are applied to the training examples. Experimental validation results show that causal information is effective in improving the prediction accuracy of DPC codes.

Annotation of medical images is crucial for assessing cancer treatment outcomes and defining radiotherapy targets. It also plays a key role in medical AI research as a preprocessing step for machine learning models. However, the heavy workload of medical professionals limits their capacity for extensive annotation tasks. To address this, we propose a method for segmenting sequential medical images with minimal annotation effort. Building on UniverSeg, which enables few-shot segmentation without additional training, our approach iteratively enhances segmentation by incorporating each inference result into the support set. Experiments on the HVSMR dataset show that our method outperforms baseline UniverSeg.

深層学習モデルを用いた医用画像解において，分類モデル等が推論過程で注視した画像領域を提示する技術は，推定結果に対する説明を与えるなど，診断を支援するものとして期待されている．これまでに，そのような注意領域を提示する複数の手法が提案されているが，説明性能や提示される領域の傾向は十分に分析されていない．そこで本研究では，MRI 脳腫瘍画像の分類を対象に，複数の深層学習モデルおよび注意領域提示手法に対して，その説明性能を定量的に評価するとともに，提示される注意領域を定性的に分析する．具体的には，深層学習モデルを4 つのブロックに分割し，各ブロックに対して得られる注意領域，その平均，各ブロックの分類における重要度を学習した重みを用いた加重平均，および既存の注意領域提示手法であるLayerCAM の結果を比較し，モデルごとの特性，およびそれによる注意領域に生じる差異を分析する．

電子カルテを用いた疾患予測は，患者の医療履歴を基に将来罹患する疾患を予測する技術である．その中でも，時間的知識グラフに基づく手法では，患者に対する疾患，所見などを時刻情報を含むグラフ構造を用いて表現し，時系列グラフモデルを利用して疾患を予測する．一方，時間的知識グラフを用いた既存手法では予測結果の説明性が欠如しており，医療分野における信頼性の観点で課題が残る．そこで本研究では，時間的知識グラフと医療オントロジーを統合した疾患予測モデルMedTKG に対してGNNExplainer を適用し，予測根拠となる説明サブグラフの抽出を試みる．評価実験では，抽出した説明サブグラフの定量的評価および定性的評価を通して，その妥当性を検証する．

In this paper, we present the development and evaluation of a clinical medical knowledge assessment set for large language models (LLMs), named UT-MedEval, using the detailed version of the disease ontology from the Clinical Ontology in Anatomical Structure and Disease (CONAND). UT-MedEval covers multiple medical domains, including cardiology, gastroenterology, neurology, nephrology-endocrinology, diabetes-metabolism, allergy-rheumatology, and orthopedics. It consists of 980 questions across three types of tasks (question-answering) and three response formats: free-form answers, multiple-choice (20 options), and yes/no questions. We evaluated OpenAI's GPT-4o and GPT-4o mini on this dataset. The accuracy results were as follows: GPT-4o achieved a correctness rate of 74.3% (95% CI: 71.5-77.0), while GPT-4o mini achieved 65.5% (95% CI: 62.5-68.4). The task requiring answers about the causes of diseases had the lowest accuracy.

Pancreatic cancer is one of the most difficult cancers to recovery. The survival time rate of 5 years is 10%. Analyze clinical public data, then determine the relation between metastasis sites and genetic mutation due to survival rate. The number of case data is 956 with OS < 1 year. Principal component analysis of attributes and clinical tests showed the highest contribution rate of MSI-Score as 0.45. The PCA scatter plot of subject attributes and clinical test results is divided into two groups based on genetic test information and metastasis information.