Semantic Classification of Diseases in Discharge Summaries Using a Context-aware Rule-based Classifier

Objective

Automated and disease-specific classification of textual clinical discharge summaries is of great importance in human life science, as it helps physicians to make medical studies by providing statistically relevant data for analysis. This can be further facilitated if, at the labeling of discharge summaries, semantic labels are also extracted from text, such as whether a given disease is present, absent, questionable in a patient, or is unmentioned in the document. The authors present a classification technique that successfully solves the semantic classification task.

Design

The authors introduce a context-aware rule-based semantic classification technique for use on clinical discharge summaries. The classification is performed in subsequent steps. First, some misleading parts are removed from the text; then the text is partitioned into positive, negative, and uncertain context segments, then a sequence of binary classifiers is applied to assign the appropriate semantic labels.

Measurement

For evaluation the authors used the documents of the i2b2 Obesity Challenge and adopted its evaluation measures: F₁-macro and F₁-micro for measurements.

Results

On the two subtasks of the Obesity Challenge (textual and intuitive classification) the system performed very well, and achieved a F₁-macro = 0.80 for the textual and F₁-macro = 0.67 for the intuitive tasks, and obtained second place at the textual and first place at the intuitive subtasks of the challenge.

Conclusions

The authors show in the paper that a simple rule-based classifier can tackle the semantic classification task more successfully than machine learning techniques, if the training data are limited and some semantic labels are very sparse.     

CTB染色法临床应用的评价

目的:探讨妇科白带涂片多项检查快速染色技术(简称CTB)的临床价值。方法:CTB染色、生理盐水涂片法、革兰氏染色涂片法比较。结果:1672份妇科阴道白带涂片标本,采用CTB染色法霉菌和滴虫阳性率显著高于常规盐水法(P<0.005);CTB染色法和革兰氏染色法检测肾形双球菌阳性率相近(P>0.05);1672份标本中,CTB染色法共检出阴道加德那菌415例,线索细胞254例,纤毛菌40例,弯曲弧菌形态细菌1例。结论:CTB法与盐水法相比具有检测项目多,方法简便,病原体形态清晰,检出率高,不需特殊设备,可重复读片,快速等优点,是白带中病原体检测的一种较好的方法。     

A Text Mining Approach to the Prediction of Disease Status from Clinical Discharge Summaries

Objective

The authors present a system developed for the Challenge in Natural Language Processing for Clinical Data—the i2b2 obesity challenge, whose aim was to automatically identify the status of obesity and 15 related co-morbidities in patients using their clinical discharge summaries. The challenge consisted of two tasks, textual and intuitive. The textual task was to identify explicit references to the diseases, whereas the intuitive task focused on the prediction of the disease status when the evidence was not explicitly asserted.

Design

The authors assembled a set of resources to lexically and semantically profile the diseases and their associated symptoms, treatments, etc. These features were explored in a hybrid text mining approach, which combined dictionary look-up, rule-based, and machine-learning methods.

Measurements

The methods were applied on a set of 507 previously unseen discharge summaries, and the predictions were evaluated against a manually prepared gold standard. The overall ranking of the participating teams was primarily based on the macro-averaged F-measure.

Results

The implemented method achieved the macro-averaged F-measure of 81% for the textual task (which was the highest achieved in the challenge) and 63% for the intuitive task (ranked 7^th out of 28 teams—the highest was 66%). The micro-averaged F-measure showed an average accuracy of 97% for textual and 96% for intuitive annotations.

Conclusions

The performance achieved was in line with the agreement between human annotators, indicating the potential of text mining for accurate and efficient prediction of disease statuses from clinical discharge summaries.     

Dense Annotation of Free-Text Critical Care Discharge Summaries from an Indian Hospital and Associated Performance of a Clinical NLP Annotator

Electronic Health Record (EHR) use in India is generally poor, and structured clinical information is mostly lacking. This work is the first attempt aimed at evaluating unstructured text mining for extracting relevant clinical information from Indian clinical records. We annotated a corpus of 250 discharge summaries from an Intensive Care Unit (ICU) in India, with markups for diseases, procedures, and lab parameters, their attributes, as well as key demographic information and administrative variables such as patient outcomes. In this process, we have constructed guidelines for an annotation scheme useful to clinicians in the Indian context. We evaluated the performance of an NLP engine, Cocoa, on a cohort of these Indian clinical records. We have produced an annotated corpus of roughly 90 thousand words, which to our knowledge is the first tagged clinical corpus from India. Cocoa was evaluated on a test corpus of 50 documents. The overlap F-scores across the major categories, namely disease/symptoms, procedures, laboratory parameters and outcomes, are 0.856, 0.834, 0.961 and 0.872 respectively. These results are competitive with results from recent shared tasks based on US records. The annotated corpus and associated results from the Cocoa engine indicate that unstructured text mining is a viable method for cohort analysis in the Indian clinical context, where structured EHR records are largely absent.     

Using Implicit Information to Identify Smoking Status in Smoke-blind Medical Discharge Summaries

As part of the 2006 i2b2 NLP Shared Task, we explored two methods for determining the smoking status of patients from their hospital discharge summaries when explicit smoking terms were present and when those same terms were removed. We developed a simple keyword-based classifier to determine smoking status from de-identified hospital discharge summaries. We then developed a Naïve Bayes classifier to determine smoking status from the same records after all smoking-related words had been manually removed (the smoke-blind dataset). The performance of the Naïve Bayes classifier was compared with the performance of three human annotators on a subset of the same training dataset (n = 54) and against the evaluation dataset (n = 104 records). The rule-based classifier was able to accurately extract smoking status from hospital discharge summaries when they contained explicit smoking words. On the smoke-blind dataset, where explicit smoking cues are not available, two Naïve Bayes systems performed less well than the rule-based classifier, but similarly to three expert human annotators.     

卒中患者早期出院支持治疗的临床评价

1文献类型 治疗。 2证据水平 1a。 3文献来源 Langhorne P, Taylor G, Marray G, et al. Earlysupported discharge services for stroke patients： ameta-analysis of individual patients data[J]. Lancet,2005 ： 501-506.     

临床推理研究进展

近30年来国外对临床推理进行了深入的研究,在方法学和理论上取得了很大的进展。按照时间将研究划为三个阶段:①尝试将临床推理理解为一般技巧-临床推理过程研究;②与知识数量和记忆能力相关的记忆-推理研究;③与不同心理表达相关的研究。从中可以归纳出两个观点:首先,尚不能认为推理具有一般过程的变量特征。其次,专业素质与记忆中多个协调的心理表达方式相关。不同的表达方式可能应用于不同的环境,但对引起策略变化的环境特征却知之甚少。专业能力存在于多种知识表达方式之中,通过传授特定的策略、技巧和知识培养临床推理能力并不现实,医学生通过大量精心设计的病例实践来熟悉和理解概念性知识,积累解决问题的经验才是培养临床推理能力的关键。     

A Rule-based Approach for Identifying Obesity and Its Comorbidities in Medical Discharge Summaries

Objective

Evaluate the effectiveness of a simple rule-based approach in classifying medical discharge summaries according to indicators for obesity and 15 associated co-morbidities as part of the 2008 i2b2 Obesity Challenge.

Methods

The authors applied a rule-based approach that looked for occurrences of morbidity-related keywords and identified the types of assertions in which those keywords occurred. The documents were then classified using a simple scoring algorithm based on a mapping of the assertion types to possible judgment categories.

Measurements

Results for the challenge were evaluated based on macro F-measure. We report micro and macro F-measure results for all morbidities combined and for each morbidity separately.

Results

Our rule-based approach achieved micro and macro F-measures of 0.97 and 0.77, respectively, ranking fifth out of the entries submitted by 28 teams participating in the classification task based on textual judgments and substantially outperforming the average for the challenge.

Conclusions

As shown by its ranking in the challenge results, this approach performed relatively well under conditions in which limited training data existed for some judgment categories. Further, the approach held up well in relation to more complex approaches applied to this classification task. The approach could be enhanced by the addition of expert rules to model more complex medical reasoning.     

A System for Classifying Disease Comorbidity Status from Medical Discharge Summaries Using Automated Hotspot and Negated Concept Detection

Objective

Free-text clinical reports serve as an important part of patient care management and clinical documentation of patient disease and treatment status. Free-text notes are commonplace in medical practice, but remain an under-used source of information for clinical and epidemiological research, as well as personalized medicine. The authors explore the challenges associated with automatically extracting information from clinical reports using their submission to the Integrating Informatics with Biology and the Bedside (i2b2) 2008 Natural Language Processing Obesity Challenge Task.

Design

A text mining system for classifying patient comorbidity status, based on the information contained in clinical reports. The approach of the authors incorporates a variety of automated techniques, including hot-spot filtering, negated concept identification, zero-vector filtering, weighting by inverse class-frequency, and error-correcting of output codes with linear support vector machines.

Measurements

Performance was evaluated in terms of the macroaveraged F1 measure.

Results

The automated system performed well against manual expert rule-based systems, finishing fifth in the Challenge's intuitive task, and 13^th in the textual task.

Conclusions

The system demonstrates that effective comorbidity status classification by an automated system is possible.     

乳头溢液细胞学检查的临床意义

目的:探讨乳头溢液的病因及其细胞学检查的临床意义。方法:回顾性分析142例病理性乳头溢液病例;探讨乳头溢液细胞学检查的临床意义。结果:本组142例病理性乳头溢液病例中,乳腺癌占7.0%,导管内乳头状瘤55.6%,乳腺增生症16.2%,导管扩张症12.7%,乳腺炎8.5%;乳头溢液细胞学检查诊断准确率为81.7%(116/142)。结论:乳头溢液细胞学检查在诊断乳腺导管内乳头状瘤、防止和早期诊断乳腺癌中具有重要意义。     

建立临床医学专业学位临床综合能力评价体系的研究与探讨

结合东南大学近年来对临床医学专业学位研究生临床综合能力的培养及量化考核指标的研究与探讨,为本校客观评价医学专业学位研究生的临床综合能力提供了依据。     

宫颈细胞学ASC的临床评价及处理探讨

目的:探讨宫颈细胞学检查为ASC(非典型鳞状上皮细胞)的临床评价及进一步处理方法。方法:2008年1月～2011年1月我院细胞学检查为ASC的妇女386例,307例行阴道镜检查及活检,其中113例同时进行HPV-DNA检测。结果:307例ASC患者病理诊断为炎症、CINⅠ、CINⅡ、CINⅢ、SCC、湿疣的百分比分别为65.15%、11.08%、10.75%、10.10%、0.98%、1.95%。HPV检测阳性率为55.75%,HPV阳性组病理为CIN的检出率为65.08%,明显高于阴性组的36%(P<0.01)。结论:细胞学结果为ASC患者,宫颈病变占相当的比率,应高度重视,尤其是HPV阳性病例。进一步行阴道镜检查和镜下活检是比较恰当的一种处理方式,可减少癌前病变的漏检。     

多路腹腔镜数字化处理系统在临床教学中的应用

录像是外科临床教学的一种重要资料,通过多路腹腔镜数字化处理系统,可获得大量腹内脏器病变及手术操作的录像资料.本文阐述在外科临床教学中,适当采用录像示教、手术演示等具体的教学方法 及效果.     

临床教员应传授的诊断推理模式

临床医学院教员的核心任务之一是教会学生正确的诊断方法，并制定合理的治疗方案。本文归纳了临床诊断推理过程的两种主要模式，即分析法和非分析法，前者是有意识的，而后者的无意识的。这两种模式各有其优势和缺陷，只有灵活结合两种诊断推理模式，才能掌握正确的诊断方法，以适应在特定背景条件下的灵活做出的正确诊断。     

腹腔镜数字图像处理系统的临床实验研究

本文通过临床运用最新研发的腹腔镜数字图像处理系统,与原腹腔镜数字图象处理软件系统对比观察系统的稳定性及精确性,探讨其临床应用的价值.     

辩论过程中论证的建构与评估

论证是辩论的核心 ,成功的辩论总是以论证的建构和评估为前提。建构一个正确而有效的论证首先需要确定彼此的争议所在 ,明确陈述己方主张并作为结论 ,进而提出理由以支持结论 ,然后考察相反的观点与论证 ,最后以可理解的方式对论证全过程加以组织和表达。论证的评估不仅包括对论证前提 (理由 )真实性的评估 ,而且包括对理由对推断的支持关系的评估。由于辩论是展开于主体间的、以消除争议谋求共识为目的的言语行为 ,其中的证明、反驳与辩护往往是以提问和回答的相互交替为表现形式 ,因此辩论过程中经常使用的是“批判性提问策略”的论证评估方法。     

关于脂蛋白（a）的临床评价

用ＴｉｎｔＥｌｉｚｅ［ＴＭ］Ｌｐ（ａ）试剂盒、ＥＬＩＳＡ方法，对４０４例不同疾病患者血清脂蛋白（ａ）［Ｌｐ（ａ）］水平进行了对照观察。结果表明，疾病组血清Ｌｐ（ａ）水平均高于正常对照组，其中脑梗塞组、肾病综合征组和恶性肿瘤组Ｌｐ（ａ）水平升高非常明显，与正常对照组相比，差异有非常显著意义（Ｐ＜０．０１）；尿毒症组Ｌｐ（ａ）明显升高，差异有显著性意义（Ｐ＜０．０５）；高脂血症组、炎症组和ＣＲＰ（＋）组ＬＰ（ａ）水平虽亦升高，但与对照组相比，差异无统计学意义（Ｐ＞０．０５）。     

电子病历自然语言处理测评发展

分析自然语言处理在医学领域应用存在障碍的原因,提出电子病历自然语言处理测评的方法,介绍历年来有关电子病历自然语言处理测评内容及其发展情况,包括文本检索会议、医学自然语言处理测评、SHARe/CLEF测评、I2B2测评等。     

乳头溢液40例临床分析

目的探讨乳头溢液的诊断和治疗方法。方法回顾性分析40例乳头溢液患者的临床资料。结果40例乳头溢液中血性溢液28例,浆液血性溢液5例,浆液性溢液7例。病理结果：导管内乳头状瘤26例,导管扩张6例,乳腺增生7例,乳腺癌1例。结论乳管造影、乳管纤维镜对乳头溢液病因有较大诊断价值;乳头血性溢液多属病理性,需手术治疗,乳腺区段切除术是乳头溢液可靠的治疗方法。