Predictive Models for Identifying Clinical Deterioration

Amin Naemi

Research output: Thesis › Ph.D. thesis

3 Article in proceedings
2 Journal article

5 results

2021
Machine learning techniques for mortality prediction in emergency departments: a systematic review
Naemi, A., Schmidt, T., Mansourvar, M., Naghavi-Behzad, M., Ebrahimi, A. & Wiil, U. K., Nov 2021, In: BMJ Open. 11, 11, 10 p., e052663.
Research output: Contribution to journal › Journal article › Research › peer-review

Open Access
File
56 Downloads (Pure)
Prediction of Length of Stay Using Vital Signs at the Admission Time in Emergency Departments
Naemi, A., Schmidt, T., Mansourvar, M., Ebrahimi, A. & Wiil, U. K., 2021, Innovation in Medicine and Healthcare: Proceedings of 9th KES-InMed 2021. Chen, Y.-W., Tanaka, S., Howlett, R. J. & Jain, L. C. (eds.). Springer, p. 143-153 (Smart Innovation, Systems and Technologies, Vol. 242).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Open Access
File
47 Downloads (Pure)
Quantifying the impact of addressing data challenges in prediction of length of stay
Naemi, A., Schmidt, T., Mansourvar, M., Ebrahimi, A. & Wiil, U. K., Dec 2021, In: BMC Medical Informatics and Decision Making. 21, 13 p., 298.
Research output: Contribution to journal › Journal article › Research › peer-review

Open Access
File
31 Downloads (Pure)
2020
Personalized Predictive Models for Identifying Clinical Deterioration Using LSTM in Emergency Departments
Naemi, A., Schmidt, T., Mansourvar, M. & Wiil, U. K., 2020, Integrated Citizen Centered Digital Health and Social Care. Värri, A., Delgado, J., Gallos, P., Hägglund, M., Häyrinen, K., Kinnunen, U.-M., Pape-Haugaard, L. B., Peltonen, L.-M., Saranto, K. & Scott, P. (eds.). IOS Press, p. 152-156 (Studies in Health Technology and Informatics, Vol. 275).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Open Access
File
95 Downloads (Pure)
Prediction of Patients Severity at Emergency Department Using NARX and Ensemble Learning
Naemi, A., Mansourvar, M., Schmidt, T. & Wiil, U. K., 2020, 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Park, T., Cho, Y.-R., Hu, X. T., Yoo, I., Woo, H. G., Wang, J., Facelli, J., Nam, S. & Kang, M. (eds.). IEEE, p. 2793-2799
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

File
128 Downloads (Pure)

Predictive Models for Identifying Clinical Deterioration

Related research output

Search results

Machine learning techniques for mortality prediction in emergency departments: a systematic review

Prediction of Length of Stay Using Vital Signs at the Admission Time in Emergency Departments

Quantifying the impact of addressing data challenges in prediction of length of stay

Personalized Predictive Models for Identifying Clinical Deterioration Using LSTM in Emergency Departments

Prediction of Patients Severity at Emergency Department Using NARX and Ensemble Learning