Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimat...Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th(R^(2) = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMSE = 4.344 d), IH(R^(2) = 0.877, RMSE = 2.721 d), and FH(R^(2) = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.展开更多
目的通过分析肿瘤专科医院中歧义病组常见原因,分析及拟定相关对策,提升病案首页填报质量,提高病案疾病诊断相关分组(diagnosis related groups,DRGs)入组率。方法回顾性研究某肿瘤专科医院2023年1—4月174份歧义病例(QY medical record...目的通过分析肿瘤专科医院中歧义病组常见原因,分析及拟定相关对策,提升病案首页填报质量,提高病案疾病诊断相关分组(diagnosis related groups,DRGs)入组率。方法回顾性研究某肿瘤专科医院2023年1—4月174份歧义病例(QY medical records,QY病例),找出未入组的原因。结果某肿瘤专科医院研究的174例QY病例中,RQY[主要诊断大类(major diagnostic category,MDC)组中R类的QY病例]127份,占比72.99%;主要原因为恶性肿瘤化疗或放疗的患者,主诊断未正确书写为恶性肿瘤的化学治疗/恶性肿瘤的放射治疗,而仍然以原恶性肿瘤为主要诊断,导致QY病例的发生。结论通过梳理QY病例产生原因,提出了一系列应对措施,包括加强院级培训,提升临床医师病案首页填写能力;提高编码员编码水平;改进医院信息化系统,增加病案首页质控提醒,并嵌入诊断和手术逻辑校验功能;以及根据临床实际问题提出分组器优化方案,全方位措施来避免QY病例的出现。这些措施旨在提高病案首页填报质量,确保病案能够正确入组到相应的DRGs组中。展开更多
基金supported by the National Key Research and Development Program of China (Grant No. 2022YFD2300700)the Open Project Program of the State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute (Grant No. 2023ZZKT20402)+1 种基金the Agricultural Science and Technology Innovation Program, the Central Public-Interest Scientific Institution Basal Research Fund, China (Grant No. CPSIBRF-CNRRI-202119)the Zhejiang ‘Ten Thousand Talents’ Plan Science and Technology Innovation Leading Talent Project, China (Grant No. 2020R52035)。
文摘Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th(R^(2) = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMSE = 4.344 d), IH(R^(2) = 0.877, RMSE = 2.721 d), and FH(R^(2) = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.
文摘目的通过分析肿瘤专科医院中歧义病组常见原因,分析及拟定相关对策,提升病案首页填报质量,提高病案疾病诊断相关分组(diagnosis related groups,DRGs)入组率。方法回顾性研究某肿瘤专科医院2023年1—4月174份歧义病例(QY medical records,QY病例),找出未入组的原因。结果某肿瘤专科医院研究的174例QY病例中,RQY[主要诊断大类(major diagnostic category,MDC)组中R类的QY病例]127份,占比72.99%;主要原因为恶性肿瘤化疗或放疗的患者,主诊断未正确书写为恶性肿瘤的化学治疗/恶性肿瘤的放射治疗,而仍然以原恶性肿瘤为主要诊断,导致QY病例的发生。结论通过梳理QY病例产生原因,提出了一系列应对措施,包括加强院级培训,提升临床医师病案首页填写能力;提高编码员编码水平;改进医院信息化系统,增加病案首页质控提醒,并嵌入诊断和手术逻辑校验功能;以及根据临床实际问题提出分组器优化方案,全方位措施来避免QY病例的出现。这些措施旨在提高病案首页填报质量,确保病案能够正确入组到相应的DRGs组中。
