胸痛高危早筛产品

公司的数位新加坡籍创始人及其研究团队自2008年开始研究心率变异性及基于AI的胸痛分诊，迄今已有15年，在一类期刊上已发表了20篇论文，并有着相关专利。相关技术背景如下：

  1. Ong MEH, P Pavitra, Chan YH, Lin Z, Jerry Overton, Kevin R. Ward, Fei DY. An observational, prospective study exploring the use of heart rate variability as a predictor of clinical outcomes in pre-hospital ambulance patients. Resuscitation. 2008 Sep; 78(3):289-97. IF=3.601

 

  1. Liu N, Lin Z, Koh ZX, Huang GB, Ser W, Ong MEH. Patient Outcome Prediction with Heart Rate Variability and Vital Signs. Journal of Signal Processing Systems 2011 Aug; 64(2): 265-78. IF=0.607

 

  1. Ong MEH, Ng CHL, Goh KJY, Liu N, Koh ZX, Shahidah A, Zhang TT, Fook-Chong S, Lin Z. Prediction of Cardiac Arrest in Critically Ill Patients presenting to the Emergency Department Using a Machine Learning Score Incorporating Heart Rate Variability Compared with the Modified Early Warning Score. Crit Care 2012; 16(3):R108. IF= 4.61

 

  1. Liu N, Lin Z, Cao J, Koh ZX, Zhang T, Ser W, Huang G, Ong MEH. An Intelligent Scoring System and Its Application to Cardiac Arrest Prediction. IEEE Transactions on Information Technology in Biomedicine 2012; 16 (6): 1324-1331. IF= 1.68

 

  1. Ong MEH, Goh JY, Fook-Chong S, Haaland B, Khin LW, Koh ZX, Shahidah N, Lin Z.  Heart Rate Variability Risk Score for Prediction of Acute Cardiac Complications in ED Chest Pain Patients. American Journal of Emergency Medicine 2013; 31(8):1201-7. IF=1.152

 

  1. Liu N, Cao J, Lin Z, Pek PP, Koh ZX, Ong MEH. Evolutionary voting based extreme learning machines. Mathematical Problems in Engineering 2014; 1-7. IF=1.082

 

  1. Liu N, Koh ZX, Goh J, Lin Z, Haaland B, Ting BP, Ong MEH. Prediction of Adverse Cardiac Events in Emergency Department Patients with Chest Pain Using Machine Learning for Variable Selection. BMC Medical Informatics and Decision Making 2014; 14(1):75. IF=1.50

 

  1. Liu N, Goh J, Lin Z, Koh ZX, Fook-Chong S, Haaland B, Wai KL, Ting BP, Shahidah N, Ong MEH. Validation of a Risk Scoring Model for prediction of Acute Cardiac complications in Chest Pain Patients Presenting to the Emergency Department. International Journal of Cardiology; 176(2014):1091-1093. IF=6.175

 

  1. Liu N, Koh ZX, Chua EC, Tan LM, Lin Z, Mirza B, Ong MEH. Risk Scoring for Prediction of Acute Cardiac Complications from Imbalanced Clinical Data. IEEE Journal of Biomedical and Health Informatics, 2014; 18(6):1894-1902. IF= 2.072

 

  1. Liu N, Cao J, Koh ZX, Pek PP, Ong MEH. Risk stratification with extreme learning machine: a retrospective study on emergency department patients. Mathematical Problems in Engineering, 2014; 2014(248938): 1-6. IF=1.082

 

  1. Liu N, Lee MAB, Ho AFW, Haaland B, Fook-Chong S, Koh ZX, Pek PP, Chua ECP, Ting BP, Lin Z, Ong MEH. Risk Stratification for Prediction of Adverse Coronary Events in Emergency Department Chest Pain Patients with a Machine Learning Score Compared with the TIMI Score. International Journal of Cardiology 2014; 177(2014):1095-1097. IF=6.175

 

  1. Liu T, Lin Z, Ong MEH, Koh ZX, Pek PP, Yeo YK, Oh B, Ho AFW, Liu N. Manifold ranking based scoring system with its application to cardiac arrest prediction: a retrospective study in emergency department patients. Computers in Biology and Medicine 2015; 67(2015):74-82. IF=1.521. Awarded Meritorious Paper

 

  1. Heldeweg MLA, Liu N, Koh ZX, Fook-Chong S, Lye WK, Harms M, Ong MEH. A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain. Critical Care 2016; 20(1):179. IF=4.950

 

  1. Sakamoto JT, Liu N, Koh ZX, Fung NXJ, Heldeweg MLA, Ng JCJ, Ong MEH. Comparing HEART, TIMI and GRACE scores for prediction of 30-day major adverse cardiac events in high acuity chest pain patients in the emergency department. International Journal of Cardiology; 221 (2016): 759-764. IF=4.638

 

  1. Liu N, Sakamoto JT, Cao J, Koh ZX, Ho AFW, Lin Z, Ong MEH. Ensemble-Based Risk Scoring with Extreme Learning Machine for Prediction of Adverse Cardiac Events. Cognitive Computation. 2017; 9(4): 545-554. IF=3.441

 

  1. Sakamoto JT, Liu N, Koh ZX, Guo D, Heldeweg MLA, Ng JCJ, Ong MEH. Integrating heart rate variability, vital signs, electrocardiogram, and troponin to triage chest pain patients in the ED. American Journal of Emergency Medicine 2018; 36(2):185-192. IF=1.494

 

  1. Sakamoto JT, Liu N, Koh ZX, Guo D, Heldeweg ML, Ng JC, Ong ME. Heart Rate Variability Analysis in Patients Who Have Bradycardia Presenting to the Emergency Department with Chest Pain. The Journal of Emergency Medicine 2018; 54(3):273-280. IF=1.175

 

  1. Liu N, Ng JCJ, Ting CE, Sakamoto JT, Ho AFW, Koh ZX, Pek PP, Lim SH, Ong MEH. Clinical scores for risk stratification of chest pain patients in the Emergency Department: an updated systematic review. Journal of Emergency and Critical Care Medicine 2018; 2:16

 

  1. N Liu; D Guo; ZX Koh; A Ho; F Xie; T Tagami; J Sakamoto; PP Pek; B Chakraborty; SH Lim; JWJ Tan; MEH Ong. Heart Rate n-Variability (HRnV) and Its Application to Risk Stratification of Chest Pain Patients in the Emergency Department. BMC Cardiovascular Disorders. April 2020. (2020) 20:168. Open Access.  IF:2.298

 

  1. Liu N, Chee ML, Koh ZX, Leow SL, Ho AFW, Guo D, Ong MEH. Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department. BMC Med Res Methodol. 2021 Apr 17;21(1):74. IF=4.402

 

  1. System and Method for Predicting Acute Cardiopulmonary Events and Survivability of a Patient, Prof Marcus Ong Eng Hock; A/Prof Lin Zhiping; A/Prof Ser Wee; Prof Huang Guangbin, US 10,888,282 B2

 

  1. System and Method of Determining a Risk Score for Triage, Prof Marcus Ong Eng Hock; A/Prof Liu Nan, US 10,299,689 B2

AutoScore和FedScore

1. Xie F, Chakraborty B, Ong MEH, Goldstein BA, Liu N. AutoScore: A machine learning-based automatic clinical score generator and its application to mortality prediction using electronic health records. JMIR Medical Informatics 2020; 8(10): e21798.

 

 

2. “SERP IP” is related to the inventions titled “The Score for Emergency Risk Prediction (SERP)”, NUS ID Ref: 2023-388; SERP IP includes: the Copyrights in and relating to the Score for Emergency Risk Prediction (SERP) as disclosed in the article: Xie F, Ong MEH, Liew JNMH, et al. , “Development and assessment of an interpretable machine learning triage tool for estimating mortality after emergency admissions” (“SERP Copyrights”)

 

 

3. LIU Nan, LI Siqi, ONG Eng Hock Marcus, Federation of Scoring Systems， PCT/SG2023/050574，Aug 18, 2023

 

 

4. Li, S., Shang, Y., Wang, Z., Wu, Q., Hong, C., Ning, Y., ... & Liu, N. (2024). Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data. arXiv preprint arXiv:2403.05229.

 

 

5. Li, S., Ning, Y., Ong, M.E., Chakraborty, B., Hong, C., Xie, F., ... & Liu, N. (2023). FedScore: A privacy-preserving framework for federated scoring system development. Journal of Biomedical Informatics, 2023,104485, ISSN 1532-0464 https://doi.org/10.1016/j.jbi.2023.104485

 

 

6. Ang Y, Li S, Ong MEH, et al. Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department. Scientific Reports 2022 May; 12: 7111.

 

 

7. Chong SL, Niu C, Ong GYK, et al. Febrile infants risk score at triage (FIRST) for the early identification of serious bacterial infections. Scientific Reports 2023 Sep; 13: 15845.

 

 

8. Liu N, Liu M, Chen X, et al. Development and validation of interpretable prehospital return of spontaneous circulation (P-ROSC) score for out-of-hospital cardiac arrest patients using machine learning. eClinicalMedicine 2022 Jun; 48: 101422.

 

 

9. Wong XY, Ang YK, Li K, et al. Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework. Resuscitation 2022 Jan; 170: 126-133.

 

 

10. Xie F, Liu N, Yan L, et al. Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions. eClinicalMedicine 2022 Mar; 45: 101315.

 

 

11. Xie F, Ong MEH, Liew JNMH, et al. Development and assessment of an interpretable machine learning triage tool for estimating mortality after emergency admissions. JAMA Network Open 2021 Aug; 4(8): e2118467.

 

 

12. Ning Y, Li S, Ong ME, Xie F, Chakraborty B, Ting DS, Liu N. A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study. PLOS Digital Health 2022; 1(6): e0000062.

 

 

13. Yuan H, Xie F, Ong MEH, Ning Y, Chee ML, Saffari SE, Abdullah HR, Goldstein BA, Chakraborty B, Liu N. AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. Journal of Biomedical Informatics 2022; 129: 104072.

 

 

14. Saffari SE, Ning Y, Xie F, Chakraborty B, Volovici V, Vaughan R, Ong MEH, Liu N, AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes, BMC Medical Research Methodology 2022; 22: 286.

 

 

15. Xie F, Ning Y, Yuan H, Goldstein BA, Ong MEH, Liu N, Chakraborty B. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. Journal of Biomedical Informatics 2022; 125: 103959.

 

 

16. Xie F, Ning Y, Liu M, Li S, Saffari SE, Yuan H, Volovici V, Ting DSW, Goldstein BA, Ong MEH, Vaughan R, Chakraborty B, Liu N. A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes. STAR Protocols 2023 Jun; 4(2): 102302.

护工绩效管理

公司的科技顾问有多年在人体步态识别领域的研究经验，发表论文若干且有软件著作权。相关技术背景如下：

瀚新智分平台的核心技术，AutoScore和FedScore已发表论文10余篇，并有相应专利和版权。相关技术背景如下：

 

1. 汪涛; 汪泓章; 夏懿（通信）; 张德祥, "基于卷积神经网络与注意力模型的人体步态识别," 传感技术学报，31(10)，2021.

 

2. L.-W. Ge, J. Zhang, Y. Xia, P. Chen, B. Wang, and C.-H. Zheng, "Deep spatial attention hashing network for image retrieval," Journal Of Visual Communication And Image Representation, vol. 63, p. 102577, 2019.

 

3. 软件著作权：基于VUE移动端框架的人体运动参数计算系统V1.0