Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia.
Research Group on Artificial Intelligence, Universitat Rovira i Virgili, Tarragona, Spain.
Comput Methods Programs Biomed. 2018 May;158:207-228. doi: 10.1016/j.cmpb.2018.02.012.
In medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care centers use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions. Consequently, medical records and health care information systems are promising sources of data from which we can detect temporal medical knowledge.
The objectives were to (1) Analyze and classify the sorts of time constraints in medical processes, (2) Propose a formalism to represent these sorts of clinical time constraints, (3) Use these formalisms to enable the automatic generation of temporal models from clinical data, and (4) Study the adherence of these intervention models to CPG recommendations.
In order to achieve these objectives, we carried out four studies: The identification of the sort of times involved in the long-term diagnostic and therapeutic medical procedures of fifty patients, the supervision of the indications about time contained in six CPGs on chronic diseases, the study of the time structures of two standard data models, as well as ten languages to computerize CPGs. Based on the provided studies, we synthesized two representation formalisms: Micro- and macro-temporality. We developed three algorithms for automatic generation of generalized time constraints in the form of micro- and macro-temporalities from clinical databases, which were double tested.
A full classification of time constraints for medical procedures is proposed. Two formalisms called micro- and macro-temporality are introduced and validated to represent these time constraints. Time constraints were generated automatically from the data about 8781 Arterial Hypertension (AH) patients. The generated macro-temporalities restricted visits to be between 1-7 weeks, whereas CPGs recommend 2-4 weeks. Micro-temporal constraints on drug-dosage therapies distinguished between the initial dosage and the target dosage, with visits every 1-6 weeks, and 2-5 months, respectively. Our algorithms obtained semi-complete maps of dosage increments and the maximum dosages for 7 drug types. Data-based time limits for lifestyle change counsels and blood pressure (BP) check-ups were fixed to 6 and 3 months, for patients with low- and high-BP, respectively, when CPGs specify a general 3-6 month range.
Experience-based temporal knowledge detected using our algorithms complements the evidence-based knowledge about clinical procedures contained in the CPGs. Our temporal model is simple and highly descriptive when dealing with general or specific time constraints' representations, offering temporal knowledge representation of varying detail. Therefore, it is capable of capturing all the temporal knowledge we can find in medical procedures, when dealing with chronic diseases. With our model and algorithms, an adherence analysis emerges naturally to detect CPG-compliant interventions, but also deviations whose causes and possible rationales can call into question CPG recommendations (e.g., our analysis of AH patients showed that the time between visits recommended by CPGs were too long for a proper drug therapy decision, dosage titration, or general follow-up).
在医疗实践中，长期干预很常见，因此需要及时规划相关流程。遗憾的是，临床实践指南（CPG）和其他医学知识库中很难找到基于证据的时间说明。与此同时，医疗机构使用医疗记录和信息系统来记录临床流程和患者的数据，包括有关就诊、处方和其他临床操作的时间信息。因此，医疗记录和医疗保健信息系统是有前途的数据来源，可以从中检测到时间相关的医学知识。
（1）分析和分类医疗流程中的各种时间限制；（2）提出一种表示这些临床时间限制的形式化方法；（3）使用这些形式化方法从临床数据中自动生成时间模型；（4）研究这些干预模型对 CPG 建议的遵循情况。
为了实现这些目标，我们进行了四项研究：确定 50 名患者长期诊断和治疗医疗程序中涉及的时间类型；监督 6 项慢性疾病 CPG 中包含的时间说明；研究两个标准数据模型的时间结构以及十种计算机化 CPG 语言。基于提供的研究，我们综合了两种表示形式化方法：微观和宏观时间性。我们开发了三种从临床数据库中自动生成微时间性和宏时间性的广义时间约束的算法，并进行了双重测试。
提出了一个完整的医疗程序时间限制分类。引入并验证了两种称为微观和宏观时间性的形式化方法来表示这些时间限制。从 8781 名高血压（AH）患者的数据中自动生成了时间限制。生成的宏观时间性将就诊限制在 1-7 周之间，而 CPG 建议为 2-4 周。药物剂量治疗的微观时间限制区分了初始剂量和目标剂量，就诊时间分别为 1-6 周和 2-5 个月。我们的算法获得了 7 种药物类型的剂量递增和最大剂量的半完整图。针对低血压和高血压患者，生活方式改变咨询和血压检查的数据基础时间限制分别固定为 6 个月和 3 个月，而 CPG 则规定一般为 3-6 个月。
使用我们的算法检测到的基于经验的时间知识补充了 CPG 中包含的基于证据的临床程序知识。我们的时间模型在处理一般或特定时间限制的表示时简单且具有高度描述性，提供了不同详细程度的时间知识表示。因此，它能够在处理慢性疾病时捕获我们在医疗程序中发现的所有时间知识。使用我们的模型和算法，可以自然地进行依从性分析以检测符合 CPG 的干预措施，还可以检测到可能导致质疑 CPG 建议的偏差（例如，我们对 AH 患者的分析表明，CPG 建议的就诊间隔时间对于适当的药物治疗决策、剂量调整或一般随访来说过长）。
