Pienaar Michael A, Sempa Joseph B, Luwes Nicolaas, George Elizabeth C, Brown Stephen C
Department of Paediatrics and Child Health, Paediatric Critical Care Unit, University of the Free State, Bloemfontein, South Africa.
Department of Biostatistics, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa.
Front Pediatr. 2023 Feb 21;11:1005579. doi: 10.3389/fped.2023.1005579. eCollection 2023.
Delays in identification, resuscitation and referral have been identified as a preventable cause of avoidable severity of illness and mortality in South African children. To address this problem, a machine learning model to predict a compound outcome of death prior to discharge from hospital and/or admission to the PICU was developed. A key aspect of developing machine learning models is the integration of human knowledge in their development. The objective of this study is to describe how this domain knowledge was elicited, including the use of a documented literature search and Delphi procedure.
A prospective mixed methodology development study was conducted that included qualitative aspects in the elicitation of domain knowledge, together with descriptive and analytical quantitative and machine learning methodologies.
A single centre tertiary hospital providing acute paediatric services.
Three paediatric intensivists, six specialist paediatricians and three specialist anaesthesiologists.
None.
The literature search identified 154 full-text articles reporting risk factors for mortality in hospitalised children. These factors were most commonly features of specific organ dysfunction. 89 of these publications studied children in lower- and middle-income countries. The Delphi procedure included 12 expert participants and was conducted over 3 rounds. Respondents identified a need to achieve a compromise between model performance, comprehensiveness and veracity and practicality of use. Participants achieved consensus on a range of clinical features associated with severe illness in children. No special investigations were considered for inclusion in the model except point-of-care capillary blood glucose testing. The results were integrated by the researcher and a final list of features was compiled.
The elicitation of domain knowledge is important in effective machine learning applications. The documentation of this process enhances rigour in such models and should be reported in publications. A documented literature search, Delphi procedure and the integration of the domain knowledge of the researchers contributed to problem specification and selection of features prior to feature engineering, pre-processing and model development.
在南非儿童中，识别、复苏和转诊的延迟已被确定为可预防的疾病严重程度和死亡率的原因。为解决这一问题，开发了一种机器学习模型，用于预测出院前和/或入住儿科重症监护病房（PICU）之前的复合死亡结局。开发机器学习模型的一个关键方面是在其开发过程中整合人类知识。本研究的目的是描述如何获取这些领域知识，包括使用文献检索记录和德尔菲法。
进行了一项前瞻性混合方法开发研究，包括获取领域知识的定性方面，以及描述性、分析性定量和机器学习方法。
一家提供急性儿科服务的单中心三级医院。
三名儿科重症监护医师、六名儿科专科医生和三名麻醉专科医生。
无。
文献检索确定了154篇全文文章，报告了住院儿童死亡率的风险因素。这些因素最常见的是特定器官功能障碍的特征。其中89篇出版物研究了低收入和中等收入国家的儿童。德尔菲法包括12名专家参与者，分三轮进行。受访者认为有必要在模型性能、全面性、准确性和实用性之间达成妥协。参与者就一系列与儿童重症相关的临床特征达成了共识。除即时检验毛细血管血糖检测外，未考虑将任何特殊检查纳入模型。研究人员整合了结果，并编制了最终的特征列表。
领域知识的获取在有效的机器学习应用中很重要。这一过程的记录提高了此类模型的严谨性，应在出版物中报告。文献检索记录、德尔菲法以及研究人员领域知识的整合有助于在特征工程、预处理和模型开发之前明确问题并选择特征。
