Recent papers on clinical empirical identifies prioritize investment

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1. Balancing Privacy and Utility in Artificial Intelligence-Based Clinical Decision Support: Empirical Evaluation Using De-Identified Electronic Health Record Data

   2025 · Applied Sciences · Lee, Jungwoo, Lee, Kyu-Hee

   2025

2. Using AI to Identify the Biological Functions of Lectins from the Native Red Alga Eucheuma Serra: an Analysis and Review of its Empirical and Clinical Applications

   2025 · Alternative Complementary & Integrative Medicine · Society, The Artificial Intelligence International Health Management, Liao, Chien Hua

   2025

3. Utilizing an Opportunistic Clinical Study and Population‐Based Pharmacokinetic Models to Identify Rational Empiric Dosing Regimens for Piperacillin–Tazobactam in Critically Ill Patients

   2024 · The Journal of Clinical Pharmacology · Reeder, Joshua A., Creech, C. Buddy, Nation, Roger L. et al.

   2024

4. Carbapenem or new β-lactam-β-lactamase inhibitors? An Italian survey supported by SITA, SIMIT and SIAARTI to identify the factors affecting empiric antimicrobial therapy choice in real-life clinical practice

   2024 · European Journal of Clinical Microbiology & Infectious Diseases · Colaneri, Marta, Genovese, Camilla, Lombardi, Andrea et al.

   2024

5. Utilizing Opportunistic Clinical Study and Population-Based Pharmacometric Models to Identify Rational Empiric Dosing Regimens for Piperacillin-Tazobactam in Critically Ill Patients

   2024 · Yuan, Xuanzhen, Reeder, Joshua A., Creech, C. Buddy et al.

   2024

6. Adu, K.K. and Adjei, E. (2018). The phenomenon of data loss and cyber security issues in Ghana. Foresight, 20(2), 150–61. Ahmed, M.T.U., Bhuiya, N.I. and Rahman, M.M. (2017). A secure enterprise architecture focused on security and technology-transformation (SEAST), The 12th International Conference for Internet Technology and Secured Transactions, (ICITST-2017), Cambridge, UK, 11–4/12/2017. Alanazi, S.T., Anbar, M., Ebad, S.A., Karuppayah, S. and Al-Ani, H.A. (2020). Theory-based model and prediction analysis of information security compliance behavior in the Saudi healthcare sector. Symmetry, 12(9), 1544. DOI: 10.3390/sym12091544 Alateyah, S.A., Crowder, R.M. and Wills, G.B. (2013). Identified factors affecting the citizen’s intention to adopt e-government in Saudi Arabia. World Academy of Science, Engineering and Technology, 7(8), 904–12. Antonino, P., Duszynski, S., Jung, C. and Rudolph, M. (2010). Indicator-based architecture-level security evaluation in a service-oriented environment. In: The Fourth European Conference on Software Architecture: Copenhagen, Denmark, 23–26/08/2010. DOI: 10.1145/1842752.1842795. Chaturvedi, M., Gupta, M. and Bhattacharya, J. (2008). Cyber Security Infrastructure in India: A Study, Emerging Technologies in E-Government. Available at: http://www.csi-sigegov.org/emerging_pdf/9_70-84.pdf (Accessed on 15/11/2020). Dalol, M.H. (2018). Effectiveness of Accounting Information Systems in Light of Development of IT Infrastructure and Information Security. Master’s Dissertation, The Islamic University of Gaza, Gaza, Palestine. Dooley, K. (2001). Designing Large Scale LANs: Help for Network Designers. USA: O'Reilly Media. Ebad, S. (2018a) An exploratory study of ICT projects failure in emerging markets. Journal of Global Information Technology Management, 21(2), 139–60. DOI: 10.1080/1097198X.2018.1462071. Ebad, S. (2018b). The influencing causes of software unavailability: A case study from industry. Software Practice and Experience, 48(5), 1056–76. DOI: 10.1002/spe.2569. Hashizume, K., Rosado, D.G., Fernández-Medina, E. and Fernandez, E.B. (2013). An analysis of security issues for cloud computing. Journal of Internet Services and Applications, 4(5), n/a. DOI: 10.1186/1869-0238-4-5. Kirby, L. (2015). Beyond Cyber Security: Protecting Your IT Infrastructure. Available at https://uptimeinstitute.com/images/Documents/ProtectingYourITInfrastructure.pdf (accessed on 15/11/2020). Lethbridge, T.C., Sim, S.E. and Singer, J. (2005). Studying software engineers: Data collection techniques for software field studies. Empirical Software Engineering, 10(3), 311–41. Marrone, M. and Kolbe, L.M. (2011). Impact of IT service management frameworks on the IT organization. Business and Information Systems Engineering, 3(1), 5–18. Mastelic, T. and Brandic, I. (2013). TimeCap: Methodology for comparing IT infrastructures based on time and capacity metrics. In: The IEEE 6th International Conference on Cloud Computing, 131–8, Santa Clara, CA, USA, 28/06–03/07/2013. Mimura, M. and Suga, Y. (2019). Filtering malicious JavaScript code with Doc2Vec on an imbalanced dataset. In: The 14th Asia Joint Conference on Information Security (AsiaJCIS), Kobe, Japan, 24–31/08/2019. Pearlson, K.E., Saunders, C.S. and Galletta, D.F. (2019). Managing and Using Information Systems. 5th edition, USA: Wiley. Popp, K. and Meyer, R. (2011). Profit from Software Ecosystems Models, Ecosystems and Partnerships in the Software Industry. Norderstedt, Germany: Books on Demand. Priem, R. (2020). Distributed ledger technology for securities clearing and settlement: Benefits, risks, and regulatory implications. Financial Innovation, 6(11), n/a. DOI: 10.1186/s40854-019-0169-6. Rabii, L. and Abdelaziz, D. (2015). Comparison of e-readiness composite indicators, The 15th International Conference on Intelligent Systems Design and Applications (ISDA), Marrakech, Morocco, 14–16/12/2015. Sanchez-Nielsen, E., Padron-Ferrer, A. and Marreo-Estevez, F. (2011). A multi-agent system for incident management solutions on IT infrastructures. In: The 14th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2011), La Laguna, Spain, 07–11/11/2011. Schoenfisch, J, Meilicke, C., Stülpnagel, J.V. and Ortmann, J (2018). Root cause analysis in IT infrastructures using ontologies and abduction in Markov logic networks. Information Systems, 74(2), 103–16. Shang, S. and Seddon, P.B. (2000). A comprehensive framework for classifying the benefits of ERP systems. In: The 2000 American Conference of Information Systems, Long Beach, California, 10–13/08/2000. Shoffner, M., Owen, P., Mostafa, J., Lamm, B., Wang, X., Schmitt, C.P. and Ahalt S.C. (2013). The secure medical research workspace: An IT infrastructure to enable secure research on clinical data. Clinical and Translational Science, 6 (3), 222–5. Shrivastava, A.K. (2015). The impact assessment of IT Infrastructure on information security: a survey report. In: International Conference on Inf

   2021 · Scientific Journal of King Faisal University Basic and Applied Sciences · Khoufi, Emad Al

   2021

7. Clinical Findings and Pain Symptoms as Potential Risk Factors for Chronic TMD: Descriptive Data and Empirically Identified Domains from the OPPERA Case-Control Study

   2011 · Journal of Pain · Ohrbach, Richard, Fillingim, Roger B., Mulkey, Flora et al.

   2011

8. 69 Empirical ethics and palliative care: a systematic review of the ethical challenges identified by specialist palliative care practitioners in their day-to-day clinical practice

   2019 · Poster presentations · Schofield, Guy, Dittborn, Mariana, Huxtable, Richard et al.

   2019

9. Identifying clinical risk factors for antibiotic resistances in urinary tract infections and optimizing empirical treatment: a retrospective study.

   2016 · Bischoff, Sebastian

   2016

10. Dunn Index Bootstrap (DIBS): A procedure to empirically select a cluster analysis method that identifies biologically and clinically relevant molecular disease subgroups

    2015 · BMC Bioinformatics · Pawlikowska, Iwona, Liu, Zhifa, Shi, Lei et al.

    2015

11. Predictors identifying those at increased risk for STDs: a theory-guided review of empirical literature and clinical guidelines

    2014 · International Journal of STD & AIDS · Falasinnu, Titilola, Gilbert, Mark, Salway, Travis et al.

    2014

12. Clinical parameters predicting failure of empirical antibacterial therapy in early onset neonatal sepsis, identified by classification and regression tree analysis

    2009 · BMC Pediatrics · Metsvaht, Tuuli, Pisarev, Heti, Ilmoja, Mari‐Liis et al.

    2009

13. Preliminary Results of a Multicenter, International, Retrospective, Study to Validate a Clinical Prediction Rule (CPR) to Identify Critically-Ill Patients at Risk of Invasive Candidiasis (IC) for TReatment with Empirical Antifungal Therapy (TREAT Study)

    2008 · 46th Annual Meeting · Ostrosky‐Zeichner, Luis

    2008

14. The Effort to Identify Empirically Supported Psychological Treatments and Its Implications for Clinical Research, Practice, and Training: Commentary on Papers by Lester Luborsky and Hans H. Strupp

    2001 · Psychoanalytic Dialogues · Blatt, Sidney J.

    2001

15. "Detecting Latent Clinical Taxa, IV: An Empirical Study of the Maximum Covariance Method and the Normal Minimum Chi-Square Method Using Three MMPI Keys to Identify the Sexes"

    1973 · Golden, Robert R., Meehl, Paul E.

    1973

16. Identifying Latent Clinical Taxa, III: An Empirical Trial of the Normal Single-indicator Method, Using MMPI Scale 5 to Identify the Sexes

    1969 · Meehl, Paul E., Lykken, David T., Burdick, M. R. et al.

    1969