Dai Yang, Wen Huei Hsun, Yang Joanna, Gupta Neepa, Rhee Connie, Horowitz Carol R, Mohottige Dinushika, Nadkarni Girish N, Coca Steven, Chan Lili
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Barbara T Murphy Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Kidney360. 2025 May 1;6(5):776-783. doi: 10.34067/KID.0000000694. Epub 2025 Jan 29.
Natural language processing can be used to identify patient symptoms from the electronic health records with good performance when compared with manual chart review. Natural language processing–extracted patient symptom burden does not reflect patient burden due to under-recognition and underdocumentation by health care professionals.
Patients on hemodialysis have a high burden of emotional and physical symptoms. These symptoms are often under-recognized. Natural language processing (NLP) can be used to identify patient symptoms from electronic health records (EHRs). However, whether symptom documentation matches patient-reported burden is unclear.
We conducted a prospective study of patients seen at an ambulatory nephrology practice from September 2020 to April 2021. We collected symptom surveys from patients, nurses, and physicians. We then developed an NLP algorithm to identify symptoms from the patients' EHRs and validated the performance of this algorithm using manual chart review and patient surveys as a reference standard. Using patient surveys as the reference standard, we compared symptom identification by () physicians, () nurses, () physicians or nurses, and () NLP.
We enrolled 97 patients into our study, 63% were female, 49% were non-Hispanic Black, and 41% were Hispanic. The most common symptoms reported by patients were fatigue (61%), cramping (59%), dry skin (53%), muscle soreness (43%), and itching (41%). Physicians and nurses significantly under-recognized patients' symptoms (sensitivity 0.51 [95% confidence interval (CI), 0.40 to 0.61] and 0.63 [95% CI, 0.52 to 0.72], respectively). Nurses were better at identifying symptoms when patients reported more severe symptoms. There was no difference in results by patients' sex or ethnicity. NLP had a sensitivity of 0.92, specificity of 0.95, positive predictive value of 0.75, and negative predictive value of 0.99 with manual EHR review as the reference standard and a sensitivity of 0.58 (95% CI, 0.47 to 0.68), specificity of 0.73 (95% CI, 0.48 to 0.89), positive predictive value of 0.92 (95% CI, 0.82 to 0.97), and negative predictive value of 0.24 (95% CI, 0.14 to 0.38) compared with patient surveys.
Although patients on hemodialysis report high prevalence of symptoms, symptoms are under-recognized and underdocumented. NLP was accurate at identifying symptoms when they were documented. Larger studies in representative populations are needed to assess the generalizability of the results of the study.
与人工病历审查相比，自然语言处理可用于从电子健康记录中识别患者症状，且性能良好。由于医疗保健专业人员识别不足和记录不充分，自然语言处理提取的患者症状负担并不能反映患者的实际负担。
接受血液透析的患者存在较高的情感和身体症状负担。这些症状往往未得到充分识别。自然语言处理（NLP）可用于从电子健康记录（EHR）中识别患者症状。然而，症状记录是否与患者报告的负担相符尚不清楚。
我们对2020年9月至2021年4月在门诊肾病科就诊的患者进行了一项前瞻性研究。我们收集了患者、护士和医生的症状调查问卷。然后，我们开发了一种NLP算法，用于从患者的电子健康记录中识别症状，并以人工病历审查和患者调查作为参考标准来验证该算法的性能。以患者调查作为参考标准，我们比较了（）医生、（）护士、（）医生或护士以及（）自然语言处理识别症状的情况。
我们纳入了97名患者进行研究，其中63%为女性，49%为非西班牙裔黑人，41%为西班牙裔。患者报告的最常见症状为疲劳（61%）、抽筋（59%）、皮肤干燥（53%）、肌肉酸痛（43%）和瘙痒（41%）。医生和护士对患者症状的识别明显不足（敏感性分别为0.51[95%置信区间（CI），0.40至0.61]和0.63[95%CI，0.52至0.72]）。当患者报告症状更严重时，护士在识别症状方面表现更好。患者的性别或种族对结果没有影响。以人工电子健康记录审查作为参考标准时，自然语言处理的敏感性为0.92，特异性为0.95，阳性预测值为0.75，阴性预测值为0.99；与患者调查相比，敏感性为0.58（95%CI，0.47至0.68），特异性为0.73（95%CI，0.48至0.89），阳性预测值为0.92（95%CI，0.82至0.97），阴性预测值为0.24（95%CI，0.14至0.38）。
尽管接受血液透析的患者报告症状的患病率较高，但症状未得到充分识别和记录。自然语言处理在症状被记录时能够准确识别。需要在代表性人群中进行更大规模的研究，以评估本研究结果的普遍性。
