基于黑板模型的心电图解释系统BBIS—ECG设计

作者提出一种基于黑板模型的心电信号解释系统的设计方案，该系统模拟心电专家诊断的思维过程，通过样本学习自动获取模糊规则，以启发性知识控制诊断流程，并通过对模糊量隶属度函数和模拟规则可信度因子的计算，以双向非精确推理进行诊断。同时，将计算模型纳入知识库，应用信号处理的基本原理在信号处理与信号解释之间也建立了双向作用，实现了真正意义上的定性推理与定量计算的结合。并利用同一通道信号的上下文信息及不同通道间     

基于黑板模型的心电图解释系统BBIS－ECG设计

作者提出一种基于黑板模型的心电信号解释系统的设计方案，该系统模拟心电专家诊断的思维过程，通过样本学习自动获取模糊规则，以启发性知识控制诊断流程，并通过对模糊量隶属度函数和模糊规则可信度因子的计算，以双向非精确推理进行诊断。同时，将计算模型纳入知识库，应用信号处理的基本原理在信号处理与信号解释之间也建立了双向作用，实现了真正意义上的定性推理与定量计算的结合。并利用同一通道信号的上下文信息及不同通道间的相关信息协助诊断，更有效地利用了数据信息。     

基于双层建模的知识表达方法在医学知识库构建中的应用

医学知识库是临床决策支持系统(CDSS)的重要组成部分。目前部署在医院内部的CDSS通常针对特定的需求(如合理用药、辅助诊断),其知识库所涵盖的知识种类比较单一。构建一个整合多种不同医学知识种类的协同知识库,能够使得不同类型的知识内容作为一个有机整体提供更加全面和有效的决策支持应用。提出一种基于双层建模的知识表达方法,首先分析和归纳临床信息化环境下影响诊疗决策的各类医学知识,如诊断知识、治疗知识、药学知识等,并对它们之间的协同关系进行定义,形成认知模型,然后针对特定的知识推理和计算的需求,为每一种知识类型选取具体的知识表达形式,形成最终的计算模型。基于该双层建模方法所研发的临床决策支持系统在三甲医院已运行4年多,实践表明该系统有效解决不同知识类型的集成和推理的需要,为构建整合和全面的知识应用奠定基础。     

软组织和骨肿瘤免疫组织化学检测专家共识（2022版）

免疫组织化学检测已成为助力软组织和骨肿瘤诊疗的重要辅助手段。近年来,随着肿瘤分子遗传学技术的不断进步,已研发出软组织和骨肿瘤基因改变特征性蛋白产物抗体,采用免疫组织化学检测可替代相应分子遗传学检测,并对其病理诊断与鉴别诊断起到重要辅助作用,同时也对肿瘤靶向治疗和生物学行为预测具有一定指导意义。本共识总结软组织和骨肿瘤免...     

动脉血气分析和治疗建议软件的开发和应用

目的 开发一种可以快速、准确判断呼吸衰竭和酸碱失衡类型并提出相应治疗建议的动脉血气分析软件。方法 用Delphi7．0编写全部程序，适用Windows98、2000和XP操作系统。使用MicrosoftAeess建立数据库。运用相关知识和公式建立知识库和推理判断库。结果 本软件由动脉血气图分析、酸碱紊乱分型、电解质紊乱判断和呼吸衰竭分型等功能模块组成，并能对呼吸衰竭、酸碱紊乱和电解质紊乱等提出治疗建议，临床使用表明可快速判断呼吸衰竭、酸碱失衡并对治疗提出建议。结论 本软件界面简洁，操作方便，结果准确可靠，分析时间短，在临床有较大的实用价值。     

用于医学辅助诊断的神经网络方法的应用研究

医学辅助诊断专家系统是人工智能领域的一个重要组成部分，本文尝度用神经网络方法实现医学辅助诊断，人工神经网络（ＡＮＮ）通过对实际病例的学习，将有关疾病症状与疾病类型的知识分布存储于网络权重中，然后利用这些知识进行联想推理，这与传统的知识基符号推理方法有着本质的区别。作者采用该方法于胃、食管疾病的辅助诊断中，取得了初步实验结果。     

基于元推理和演绎机制的中医知识库中的知识消化

本文采用人工智能的Prolog逻辑语言,以构建中医知识库为目的,运用元推理和演绎机制对中医知识库进行知识消化,最终使中医知识库中的知识更合理和精确.     

一种基于产生式规则的保健知识库架构设计

产生式规则是应用较为普遍的医学知识表达形式,本文提出了一种建立产生式保健知识库框架的基本方法.通过把构成医学诊断知识的主诉、体征、检验、检查四要素归纳为四元组的表达形式,并将其分解为不可再分的知识原子,引入确定性因子,建立了符合产生式系统推理的知识表达模型;并且通过层次化的树状知识结构,使医学专家能够直观、方便的建立和管理领域知识.     

基于 Shapiro 模型推理算法的智能知识获取在中医知识库中的实现

本文应用现代计算机技术,将Shapiro模型推理算法运用于中医知识库中,使该知识库系统具有智能知识获取的能力.     

恶性骨肿瘤保肢术临床应用的回眸与展望

恶性骨肿瘤的治疗已是临床应用娴熟的方法。新辅助化疗的理念得到普遍认可，保肢术已成为治疗肢体恶性骨肿瘤的重要手段。恶性骨肿瘤的治疗不仅要求提高患者的生存率，而且要将保存肢体的良好功能，最大可能提高患者的生活质量作为最终目标。     

The diagnostic rules of peripheral lung cancer preliminary study based on data mining technique

Objective: To discuss the clinical and imaging diagnostic rules of peripheral lung cancer by data mining technique, and to explore new ideas in the diagnosis of peripheral lung cancer, and to obtain early-stage technology and knowledge support of computer-aided detecting (CAD). Methods: 58 cases of peripheral lung cancer confirmed by clinical pathology were collected. The data were imported into the database after the standardization of the clinical and CT findings attributes were identified. The data was studied comparatively based on Association Rules (AR) of the knowledge discovery process and the Rough Set (RS) reduction algorithm and Genetic Algorithm(GA) of the generic data analysis tool (ROSETTA), respectively. Results: The genetic classification algorithm of ROSETTA generates 5 000 or so diagnosis rules. The RS reduction algorithm of Johnson's Algorithm generates 51 diagnosis rules and the AR algorithm generates 123 diagnosis rules. Three data mining methods basically consider gender, age,cough, location, lobulation sign, shape, ground-glass density attributes as the main basis for the diagnosis of peripheral lung cancer. Conclusion: These diagnosis rules for peripheral lung cancer with three data mining technology is same as clinical diagnostic rules, and these rules also can be used to build the knowledge base of expert system. This study demonstrated the potential values of data mining technology in clinical imaging diagnosis and differential diagnosis.     

Knowledge discovery approach to automated cardiac SPECT diagnosis

The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologist's diagnosis. The system is able to provide a set of computer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because of the high correctness of diagnoses.     

知识发现及其在临床医学上的应用

目前海量的数据存储已经远远超过了人的理解能力 ,尤其是现代医学数据量越来越大 ,而传统的统计技术及数据管理工具已经力不从心 ,知识发现及数据挖掘工具在处理海量数据库时显示了它们的长处。本文首先介绍了知识发现及数据挖掘的概念 ,又详细介绍了知识发现的一个重要工具粗糙集理论的有关基础知识并举例介绍了其应用。     

Visualization and interactive analysis of blood parameters with InfoZoom

This paper describes the application of the data analysis tool InfoZoom to a database containing the results of blood examinations for about 400 patients with a suspect of thrombosis. The main goal was to find correlations between the measurements and the occurrence of a thrombosis. No automatic method for data mining is used. Instead, InfoZoom uses a novel technique to display data sets as highly compressed tables which always fit completely onto the screen. The user can interactively explore animated tabular views of the data. In this way, the user gets a feeling of the data, detects interesting knowledge, and gains a deep understanding of the data set.     

Molecular diagnosis of human cancer type by gene expression profiles and independent component analysis

The precise diagnosis of cancer type based on microarray data is of particular importance and is also a challenging task. We have devised a novel pattern recognition procedure based on independent component analysis (ICA). Different from the conventional cancer classification methods, which are limited in their clinical applicability of cancer diagnosis, our method extracts explicitly, by ICA algorithm, a set of specific diagnostic patterns of normal and tumor tissues corresponding to a set of biomarkers for clinical use. We validated our procedure with the colon and prostate cancer data sets and achieved good diagnosis (>90%) on the data sets studied here. This technique is also suitable for the identification of diagnostic expression patterns for other human cancers and demonstrates the feasibility of simple and accurate molecular cancer diagnostics for clinical implementation.     

医学数据挖掘的技术、方法及应用

医学数据挖掘是提高医院信息管理水平，为疾病的诊断和治疗提供科学的、准确的决策，促进远程医疗和社区医疗发展的需要。本文对医学数据挖掘的关键技术——数据的预处理、多属性信息的融合、挖掘算法的高效性与鲁棒性、提供知识的准确性与可靠性等进行了论述；阐述了基于计算智能的医学数据挖掘方法，介绍了人工神经网络、模糊逻辑、遗传算法、粗糙集理论和支持向量机在医学数据挖掘中的应用；最后对医学数据挖掘的特点和亟待解决的问题进行了总结。     

A comprehensive diagnostic system for hematological diseases based on the WHO classification of tumors of hematopoietic and lymphoid tissues

The WHO classification of tumors of hematopoietic and lymphoid tissues is widely used as a standard for the diagnosis of hematological diseases. However, complicated algorithms developed through the integration of a large amount of laboratory information together with clinical characteristics are employed in most of the diagnostic criteria. Physicians usually have only a limited time to interpret laboratory findings and collate all this information for making a final diagnosis. In order to support their clinical practice, our clinical laboratory has implemented a comprehensive diagnostic system, which provides a summarized report of hematological diseases based on the related laboratory data when the microscopic evaluation of a bone marrow smear or flow cytometric analysis of a lymph node is ordered. The report describes possible disease classifications and suggests further examinations to be performed. This comprehensive diagnostic system is helpful for not only the standardization of the diagnosis but also efficient diagnostic procedures and, thus, subsequent medical care.     

生物医学数据挖掘

在介绍数据挖掘的背景、定义和发展现状的基础上 ,重点讲述这门技术在生物医学方面的应用特点 ,总结了医学数据库中知识发现的实现过程 ,包括数据预处理、数据挖掘算法、知识发现的分类、结论的评价以及应用等     

An environment for knowledge discovery in biology

This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported.     

A technique for identifying three diagnostic findings using association analysis

In diagnosing diseases in clinical practice, a combination of three clinical findings is often used to represent each disease. This is largely because it is often difficult or impractical to assess for all possible combinations of symptoms and abnormal exam findings that occur in any particular disease. For most diseases, diagnostic triads are based on empirical observations. In this study, we determined diagnostic triads for chronic diseases using data mining procedures. We also verified the combinations’ validity as well as our procedure for determining them. We used symptoms and examination findings from 477 patients with chronic diseases, collected as part of a 35-year longitudinal study begun in 1968. For each patient there were 295 items from examinations in internal medicine, dermatology, ophthalmology, dentistry and blood tests. We judged each item to be either normal or abnormal, and restricted the analysis to the abnormal findings. To analyze such an exhaustive assortment, we used the data mining technique of association analysis. The analysis generated three clinical findings for each disease. Diseases were defined based on blood tests. Searching through all 295 items to find the three most useful clinical findings would be impractical on a commodity PC. However, by excluding normal items, we were able to sufficiently reduce the total number of combinations so as to make combinatorial analysis on a PC feasible. In addition to more accurate diagnoses, we believe our technique can identify those diagnostic data that are more cost effective in terms of time and other resources required for their collection.