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Cited by 14Artificial Intelligence in HealthcareArtificial Intelligence in Healthcare and EducationElectronic Health Records Systems

Review on enhancing clinical decision support system using machine learning

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Masood, Anum, Naseem, Usman, Rashid, Junaid, Kim, Jungeun, Razzak, Imran·2024·CAAI Transactions on Intelligence Technology
DOI 10.1049/cit2.12286
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Abstract

Clinical decision‐making is a complex patient‐centred process. For an informed clinical decision, the input data is very thorough ranging from detailed family history, environmental history, social history, health‐risk assessments, and prior relevant medical cases. Identifying the need for structured input data to enable clinical decision‐making and quality reporting, such that it is crucial for the end‐users is still a challenge. The Clinical Decision Support Systems (CDSS) enhanced using Machine Learning (ML) approaches are described. CDSS aids in the detection and classification of various diseases but they cannot fully capture the environmental, clinical, and social constraints that are taken into consideration by the clinician in the diagnosis process. The authors provide an overview of state‐of‐the‐art healthcare CDSS. The authors initially collected 3165 research articles for this review out of which approximately 3148 records were identified from databases while 17 records were from other sources. A total of 1309 unique articles obtained from the searches were included in the study which was further rigorously evaluated for final inclusion. A generic architecture of computer‐based decision support systems using ML is provided. However, the study does not include the comparison of these CDSS in terms of their performance because of heterogeneity in the disease type, modality used for diagnosis, and the ML approach used for detection in CDSS.

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  • By integrating and analyzing patient symptoms, medical history, test results, and other risk factors, CDSS have the capability to support disease detection and identification.

    “By integrating and analyzing patient symptoms, medical history, test results, and other risk factors, CDSS have the capability to support disease detection and identification [214,215], such as the subtle or ambiguous patterns that clinicians often overlook [216,217].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [215]

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  1. Yamada, R.H.: An overview of computers in medicine. Can. Fam. Physician 14(3), 15-17 (1968)
  2. Masood, A.: Pulmonary Nodules Detection and Classification in Chest CT Using Deep Learning Techniques, vol. 6. Shanghai Jiao Tong Univ., Shanghai (2019). Tech. Rep
  3. Khan, M.A., et al.: Skin lesion segmentation and classification: a unified framework of deep neural network features fusion and selection. Expet Syst. 39(7) (2022). https://doi.org/10.1111/exsy.12497 · DOI
  4. Musen, M.A., Middleton, B., Greenes, R.A.: Clinical Decision-Support Systems. Biomedical informatics, pp. 795-840. Springer, Cham (2021)
  5. Wu, G., et al.: Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. Eur. Respir. J. 56, 2 (2020)
  6. Panigrahi, N., Ayus, I., Jena, O.P.: An Expert System-Based Clinical Decision Support System for Hepatitis-B Prediction & Diagnosis, pp. 57-75. Machine Learning for Healthcare Applications (2021)
  7. Dramburg, S., et al.: The potential of clinical decision support systems for prevention, diagnosis, and monitoring of allergic diseases. Front. Immunol. 11, 2116 (2020). https://doi.org/10.3389/fimmu.2020.02116 · DOI
  8. Casal-Guisande, M., et al.: Design and development of an intelligent clinical decision support system applied to the evaluation of breast cancer risk. J. Personalized Med. 12(2), 169 (2022). https://doi.org/10. 3390/jpm12020169 · DOI
  9. Comito, C., Falcone, D., Forestiero, A.: AI-driven clinical decision sup- port: enhancing disease diagnosis exploiting patients similarity. IEEE Access 10, 6878-6888 (2022). https://doi.org/10.1109/access.2022. 3142100 · DOI
  10. Wolever, R.Q., et al.: A systematic review of the literature on health and wellness coaching: defining a key behavioral intervention in healthcare. Glob. Adv. Health Med. 2(4), 38-57 (2013). https://doi.org/10.7453/ gahmj.2013.042 · DOI
  11. Yung, A., et al.: Computer-based decision tools for shared therapeutic decision-making in oncology: systematic review. JMIR Canc. 7(4), e31616 (2021). https://doi.org/10.2196/31616 · DOI
  12. Vogel, S., et al.: Development of a clinical decision support system for smart algorithms in emergency medicine. Stud. Health Technol. Inf. 289, 224-227 (2022). https://doi.org/10.3233/shti210900 · DOI
  13. Liao, P.-H., Tsuei, Yu-C., Chu, W.: Application of machine learning in developing decision-making support models for decompressed verte- broplasty. Healthcare 10(2), 214 (2022). MDPI. https://doi.org/10. 3390/healthcare10020214 · DOI
  14. Noble, W.S.: What is a support vector machine? Nat. Biotechnol. 24(12), 1565-1567 (2006). https://doi.org/10.1038/nbt1206-1565 · DOI
  15. Hearst, M.A., et al.: Support vector machines. IEEE Intell. Syst. Their Appl. 13(4), 18-28 (1998). https://doi.org/10.1109/5254.708428 · DOI · Paper page
  16. Freund, Y., Mason, L.: The alternating decision tree learning algorithm. ICML 99 (1999)
  17. Aalen, O.O.: A linear regression model for the analysis of life times. Stat. Med. 8(8), 907-925 (1989). https://doi.org/10.1002/sim.4780080803 · DOI
  18. Davison, A.C., Tsai, C.-L.: Regression Model Diagnostics, pp. 337-353. International Statistical Review/Revue Internationale de Statistique (1992)
  19. Altman, N.S.: An introduction to kernel and nearest-neighbor nonpara- metric regression. Am. Statistician 46(3), 175-185 (1992). https://doi. org/10.2307/2685209 · DOI
  20. Schneider, A., Hommel, G., Blettner, M.: Linear regression analysis: part 14 of a series on evaluation of scientific publications. Deutsch. Ärztebl. Int. 107(44), 776 (2010). https://doi.org/10.3238/arztebl.2010.0776 · DOI
  21. Wang, D., Tan, D., Liu, L.: Particle swarm optimization algorithm: an overview. Soft Comput. 22(2), 387-408 (2018). https://doi.org/10. 1007/s00500-016-2474-6 · DOI · Paper page
  22. Gad, A.G.: Particle swarm optimization algorithm and its applications: a systematic review. Arch. Comput. Methods Eng. 29(5), 2531-2561 (2022). https://doi.org/10.1007/s11831-021-09694-4 · DOI
  23. Su, J., Zhang, H.: Full Bayesian network classifiers. In: Proceedings of the 23rd International Conference on Machine Learning (2006)
  24. Shen, B., et al.: Discriminative parameter learning of general Bayesian network classifiers. In: Proceedings 15th IEEE International Confer- ence on Tools with Artificial Intelligence. IEEE (2003)
  25. Rish, I.: An empirical study of the naive Bayes classifier. IJCAI 2001 Worksh. Emp. Meth. Artif. Intell. 3(22) (2001)
  26. Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorob. 7, 21 (2013). https://doi.org/10.3389/fnbot.2013.00021 · DOI · Paper page
  27. Castro-Martín, L., et al.: On the use of gradient boosting methods to improve the estimation with data obtained with self-selection procedures. Mathematics 9(23), 2991 (2021). https://doi.org/10.3390/math9232991 · DOI
  28. Konukoglu, E., Glocker, B.: Random forests in medical image computing. In: Handbook of Medical Image Computing and Computer Assisted Intervention, pp. 457-480. Academic Press (2020)
  29. Tripepi, G., et al.: Linear and logistic regression analysis. Kidney Int. 73(7), 806-810 (2008). https://doi.org/10.1038/sj.ki.5002787 · DOI
  30. Olanow, C.W., Watts, R.L., Koller, W.C.: An algorithm (decision tree) for the management of Parkinson's disease (2001):: treatment Guide- lines. Neurology 56(suppl 5), S1-S88 (2001). https://doi.org/10.1212/ wnl.56.suppl_5.s1 · DOI
  31. Katipamula, S., Reddy, T.A., Claridge, D.E.: Multivariate Regression Modeling, pp. 177-184 (1998)
  32. Criminisi, A., Shotton, J. (eds.) Decision Forests for Computer Vision and Medical Image Analysis. Springer Science & Business Media (2013)
  33. Sandeep Kumar, E., Satya Jayadev, P.: Deep learning for clinical deci- sion support systems: a review from the panorama of smart healthcare. Deep Learn. Techniq. Biomed. Health Inform., 79-99 (2020)
  34. Kose, U., et al.: Deep Learning for Medical Decision Support Systems. Springer, Berlin (2021)
  35. Hamza, A., et al.: D2BOF-COVIDNet: a framework of deep Bayesian optimization and fusion-assisted optimal deep features for COVID-19 classification using chest X-ray and MRI scans. Diagnostics 13(1), 101 (2023). https://doi.org/10.3390/diagnostics13010101 · DOI
  36. Unger, M.: Data acquisition and the implications of machine learning in the development of a Clinical Decision Support system. In: 2021 IEEE/ACM 1st Workshop on AI Engineering-Software Engineering for AI (WAIN). IEEE (2021)
  37. Xu, Q., et al.: Interpretability of clinical decision support systems based on artificial intelligence from technological and medical perspective: a systematic review. J. Healthc. Eng. 2023, 1-13 (2023). https://doi.org/ 10.1155/2023/9919269 · DOI
  38. Sanchez-Martinez, S., et al.: Machine learning for clinical decision- making: challenges and opportunities in cardiovascular imaging. Front. Cardiovasc. Med. 8 (2020). https://doi.org/10.3389/fcvm.2021.765693 · DOI
  39. Huang, J., et al.: An empirical analysis of three-stage data-preprocessing for analogy-based software effort estimation on the ISBSG data. In: 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS). IEEE (2017)
  40. Mangiameli, P., West, D., Rampal, R.: Model selection for medical diagnosis decision support systems. Decis. Support Syst. 36(3), 247-259 (2004). https://doi.org/10.1016/s0167-9236(02)00143-4 · DOI
  41. Ahmad, M., et al.: Diagnostic decision support system of chronic kidney disease using support vector machine. In: 2017 Second International Conference on Informatics and Computing (ICIC). IEEE (2017)
  42. Safdar, S., et al.: Machine Learning Based Decision Support Systems (DSS) for Heart Disease Diagnosis: A Review. Artificial Intelligence Review (2018)
  43. Karthikeyan, A., et al.: Machine learning based clinical decision support system for early COVID-19 mortality prediction. Front. Public Health 9 (2021). https://doi.org/10.3389/fpubh.2021.626697 · DOI
  44. Baig, M.M., et al.: Machine learning-based clinical decision support system for early diagnosis from real-time physiological data. In: 2016 IEEE Region 10 Conference (TENCON) (2016)
  45. Clausen, C.E., et al.: Testing an individualized digital decision assist system for the diagnosis and management of mental and behavior disorders in children and adolescents. BMC Med. Inf. Decis. Making 20(1), 1-9 (2020). https://doi.org/10.1186/s12911-020-01239-2 · DOI
  46. Tuppad, A., Patil, S.D.: Machine learning for diabetes clinical decision support: a review. Adv. Computat. Intell. 2(2), 1-24 (2022). https://doi. org/10.1007/s43674-022-00034-y · DOI
  47. Moreira, M.W.L., et al.: A comprehensive review on smart decision support systems for health care. IEEE Syst. J. 13(3), 3536-3545 (2019). https://doi.org/10.1109/jsyst.2018.2890121 · DOI
  48. Guerrero, J.I., Miró-Amarante, G., Martín, A.: Decision support system in health care building design based on case-based reasoning and reinforcement learning. Expert Syst. Appl. 187, 116037 (2022). https:// doi.org/10.1016/j.eswa.2021.116037 · DOI
  49. Ganu, L., Arun, B.: Hybrid combination of machine learning techniques for diagnosis of liver impairment disease in clinical decision support system. In: Advanced Machine Intelligence and Signal Processing, pp. 479-492. Springer, Singapore (2022)
  50. Asri, H., et al.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. Proc. Comput. Sci. 83, 1064-1069 (2016). https://doi.org/10.1016/j.procs.2016.04.224 · DOI
  51. Kamnitsas, k., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Brainlesion: Glioma, Mul- tiple Sclerosis, Stroke and Traumatic Brain Injuries: Third International Workshop, BrainLes 2017. Springer International Publishing (2018). Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers 3
  52. Li, X., et al.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imag. 37(12), 2663-2674 (2018). https://doi.org/10.1109/tmi.2018.2845918 · DOI
  53. Jansen, M.J.A., Kuijf, H.J., Pluim, J.P.W.: Automatic classification of focal liver lesions based on clinical DCE-MR and T2-weighted images: a feasibility study. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE (2018)
  54. Chougrad, H., Hamid, Z., Omar, A.: Deep convolutional neural net- works for breast cancer screening. Comput. Methods Progr. Biomed. 157, 19-30 (2018). https://doi.org/10.1016/j.cmpb.2018.01.011 · DOI
  55. Trivizakis, E., et al.: Extending 2-D convolutional neural networks to 3- D for advancing deep learning cancer classification with application to MRI liver tumor differentiation. IEEE J. Biomed. Health. Inform. 23(3), 923-930 (2018). https://doi.org/10.1109/jbhi.2018.2886276 · DOI
  56. Masood, A., et al.: Computer-assisted decision support system in pul- monary cancer detection and stage classification on CT images. J. Biomed. Inf. 79, 117-128 (2018). https://doi.org/10.1016/j.jbi.2018.01.005 · DOI · Paper page
  57. Antonelli, M., et al.: Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiol- ogists. Eur. Radiol. 29(9), 4754-4764 (2019). https://doi.org/10.1007/ s00330-019-06244-2 · DOI
  58. Aich, S., et al.: A supervised machine learning approach using different feature selection techniques on voice datasets for prediction of Par- kinson's disease. In: 2019 21st International Conference on Advanced Communication Technology (ICACT). IEEE (2019)
  59. Masood, A., et al.: Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT. IEEE J. Transl. Eng. Health Med. 8, 1-13 (2019). https://doi.org/10.1109/ jtehm.2019.2955458 · DOI
  60. Akamine, Y., et al.: Application of hierarchical clustering to multi- parametric MR in prostate: differentiation of tumor and normal tissue with high accuracy. Magn. Reson. Imag. 74, 90-95 (2020). https://doi. org/10.1016/j.mri.2020.09.011 · DOI
  61. Wei, M., et al.: A benign and malignant breast tumor classification method via efficiently combining texture and morphological features on ultrasound images. Comput. Math. Methods Med. 2020, 1-12 (2020). https://doi.org/10.1155/2020/5894010 · DOI
  62. Akil, M., et al.: Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi- class weighted cross-entropy. Med. Image Anal. 63, 101692 (2020). https://doi.org/10.1016/j.media.2020.101692 · DOI
  63. Khan, M.A., et al.: VGG19 network assisted joint segmentation and classification of lung nodules in CT images. Diagnostics 11(12), 2208 (2021). https://doi.org/10.3390/diagnostics11122208 · DOI
  64. Akter, N., et al.: Glaucoma diagnosis using multi-feature analysis and a deep learning technique. Sci. Rep. 12(1), 8064 (2022). https://doi.org/ 10.1038/s41598-022-12147-y · DOI
  65. Khan, A., et al.: A two-stream deep neural network-based intelligent system for complex skin cancer types classification. Int. J. Intell. Syst. 37(12), 10621-10649 (2021). https://doi.org/10.1002/int.22691 · DOI
  66. Zhong, A.Y., et al.: Automated patient-level prostate cancer detection with quantitative diffusion magnetic resonance imaging. Eur. Urol. Open Sci. 47, 20-28 (2023). https://doi.org/10.1016/j.euros.2022.11.009 · DOI
  67. Swain, D., et al.: A robust chronic kidney disease classifier using ma- chine learning. Electronics 12(1), 212 (2023). https://doi.org/10.3390/ electronics12010212 · DOI
  68. Klimont, M., et al.: Deep learning-based segmentation and volume calculation of pediatric lymphoma on contrast-enhanced computed tomographies. J. Personalized Med. 13(2), 184 (2023). https://doi.org/ 10.3390/jpm13020184 · DOI
  69. Depto, D.S., et al.: Quantifying imbalanced classification methods for leukemia detection. Comput. Biol. Med. 152, 106372 (2023). https:// doi.org/10.1016/j.compbiomed.2022.106372 · DOI
  70. Masood, A., et al.: Automatic computer aided system for lung cancer in chest CTs using MD-RFCN combined with tri-level region proposal network. In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), vol. 17, pp. 1377-1380 (2019)
  71. Pontes, B., et al.: A data mining based clinical decision support system for survival in lung cancer. Rep. Practical Oncol. Radiother. 26(6), 839-848 (2021). https://doi.org/10.5603/rpor.a2021.0088 · DOI
  72. Ben-Cohen, A., et al.: CT image-based decision support system for categorization of liver metastases into primary cancer sites: initial re- sults. Acad. Radiol. 24(12), 1501-1509 (2017). https://doi.org/10. 1016/j.acra.2017.06.008 · DOI
  73. Salunkhe, S.S., et al.: Portable healthcare computing and clinical decision support system enabled by artificial intelligence. Int. J. Eng. Syst. Model Simulat. 13(3), 228-233 (2022). https://doi.org/10.1504/ijesms.2022. 123955 · DOI
  74. Diraco, G., Leone, A., Siciliano, P.: Clinical decision support system for multisensory stimulation therapy in dementia: a preliminary study. In: Italian Forum of Ambient Assisted Living. Springer, Cham (2022)
  75. Raji-Lawal, H.Y., et al.: Decision support system for dementia patients using intuitionistic fuzzy similarity measure. Soft Comput. Lett. 2, 100005 (2020). https://doi.org/10.1016/j.socl.2020.100005 · DOI
  76. Khan, M.A., et al.: Multimodal brain tumor classification using deep learning and robust feature selection: a machine learning application for radiologists. Diagnostics 10(8), 565 (2020). https://doi.org/10.3390/ diagnostics10080565 · DOI
  77. Khan, M.A., et al.: Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algo- rithm. Int. J. Imag. Syst. Technol. 33(2), 572-587 (2022). https://doi. org/10.1002/ima.22831 · DOI
  78. Gatsios, D., Rigas, G., Konitsiotis, S.: Design of a clinical decision support system powered by mhealth for the management of Parkinson's disease. In: 2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM). IEEE (2021)
  79. Tzallas, A.T., et al.: PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease. Sensors 14(11), 21329-21357 (2014). https://doi.org/10.3390/s141121329 · DOI
  80. Park, J., et al.: Prostate cancer trajectory-map: clinical decision support system for prognosis management of radical prostatectomy. Prostate Int. 9(1), 25-30 (2021). https://doi.org/10.1016/j.prnil.2020.06.003 · DOI
  81. van Wijk, Y., et al.: Decision support systems in prostate cancer treat- ment: an overview. BioMed Res. Int. 2019, 1-10 (2019). https://doi. org/10.1155/2019/4961768 · DOI
  82. Yu, S.H., et al.: Early experience with Watson for Oncology: a clinical decision-support system for prostate cancer treatment recommenda- tions. World J. Urol. 39(2), 407-413 (2021). https://doi.org/10.1007/ s00345-020-03214-y · DOI
  83. Hegde, R.B., et al.: Automated decision support system for detection of leukemia from peripheral blood smear images. J. Digit. Imag. 33(2), 361-374 (2020). https://doi.org/10.1007/s10278-019-00288-y · DOI
  84. Moghaddasi, H., et al.: A clinical decision support system for increasing compliance with protocols in chemotherapy of children with acute lymphoblastic leukemia. Cancer Inf. 21, 11769351221084812 (2022). https://doi.org/10.1177/11769351221084812 · DOI
  85. Negm, A.E.S., Kandil, A.H., Hassan, O.A.E.F.: Decision support system for lymphoma classification. Curr. Med. Imag. 13(1), 89-98 (2017). https://doi.org/10.2174/1573405612666160519124752 · DOI
  86. Khan, M.A., et al.: GestroNet: a framework of saliency estimation and optimal deep learning features based gastrointestinal diseases detection and classification. Diagnostics 12(11), 2718 (2022). https://doi.org/10. 3390/diagnostics12112718 · DOI
  87. Morgan, E.R., et al.: Using tablet-based technology in patient education about systemic therapy options for early-stage breast cancer: a pilot study. Curr. Oncol. 22(5), 364-369 (2015). https://doi.org/10.3747/co.22.2476 · DOI
  88. Siminoff, L.A., et al.: A decision aid to assist in adjuvant therapy choices for breast cancer. Psycho Oncol.: J. Psychol. Social Behav. Dimens. Canc. 15(11), 1001-1013 (2006). https://doi.org/10.1002/pon.1040 · DOI
  89. Peele, P.B., et al.: Decreased use of adjuvant breast cancer therapy in a randomized controlled trial of a decision aid with individualized risk information. Med. Decis. Making 25(3), 301-307 (2005). https://doi. org/10.1177/0272989x05276851 100. · DOI
  90. Cuypers, M., et al.: Uptake and usage of an online prostate cancer treatment decision aid in Dutch clinical practice: a quantitative analysis from the Prostate Cancer Patient Centered Care trial. Health Inf. J. 25(4), 1498-1510 (2019). https://doi.org/10.1177/1460458218779110 101. · DOI
  91. Yao, K., et al.: An in-visit decision aid for surgeons to address decision making for bilateral mastectomy for newly diagnosed breast cancer patients. Ann. Surg Oncol. 26(13), 4372-4380 (2019). https://doi.org/ 10.1245/s10434-019-07912-9 102. · DOI
  92. Yao, K., et al.: Impact of an in-visit decision aid on patient knowledge about contralateral prophylactic mastectomy: a pilot study. Ann. Surg Oncol. 24(1), 91-99 (2017). https://doi.org/10.1245/s10434-016- 5556-x 103. · DOI
  93. Papandreou, P., et al.: Application of clinical decision support system to assist breast cancer patients with lifestyle modifications during the COVID-19 pandemic: a randomised controlled trial. Nutrients 13(6), 2115 (2021). https://doi.org/10.3390/nu13062115 104. · DOI
  94. Timotijevic, L., et al.: Designing a mHealth clinical decision support system for Parkinson's disease: a theoretically grounded user needs approach. BMC Med. Inf. Decis. Making 20(1), 1-21 (2020). https:// doi.org/10.1186/s12911-020-1027-1 105. · DOI
  95. Miles, A., et al.: Use of a computerised decision aid (DA) to inform the decision process on adjuvant chemotherapy in patients with stage II colorectal cancer: development and preliminary evaluation. BMJ Open 7(3), e012935 (2017). https://doi.org/10.1136/bmjopen-2016-012935 106. · DOI
  96. Masood, A., et al.: Automated decision support system for lung cancer detection and classification via enhanced RFCN with multilayer fusion RPN. IEEE Trans. Ind. Inf. 16(12), 7791-7801 (2020). https://doi.org/ 10.1109/tii.2020.2972918 107. · DOI
  97. Henton, M., et al.: Development, feasibility, and small-scale imple- mentation of a web-based prognostic tool-surveillance, epidemiology, and end results cancer survival calculator. JMIR Canc. 3(2), e7120 (2017). https://doi.org/10.2196/cancer.7120 108. · DOI
  98. Wyatt, K.D., et al.: A personalized, web-based breast cancer decision making application: a pre-post survey. BMC Med. Inf. Decis. Making 19(1), 1-11 (2019). https://doi.org/10.1186/s12911-019-0924-7 109. · DOI
  99. Raj, S.X., et al.: COMBAT study-Computer based assessment and treatment-a clinical trial evaluating impact of a computerized clinical decision support tool on pain in cancer patients. Scand. J. Pain 17(1), 99-106 (2017). https://doi.org/10.1016/j.sjpain.2017.07.016 110. · DOI
  100. Schaaf, J., et al.: Evaluation of a clinical decision support system for rare diseases: a qualitative study. BMC Med. Inf. Decis. Making 21(1), 1-11 (2021). https://doi.org/10.1186/s12911-021-01435-8 111. · DOI
  101. Klarenbeek, S.E., et al.: Barriers and facilitators for implementation of a computerized clinical decision support system in lung cancer multidis- ciplinary team meetings-a qualitative assessment. Biology 10(1), 9 (2020). https://doi.org/10.3390/biology10010009 112. · DOI
  102. Cooley, M.E., et al.: Clinical decision support for symptom management in lung cancer patients: a group RCT. J. Pain Symptom Manag. 63(4), 572-580 (2022). https://doi.org/10.1016/j.jpainsymman.2021.12.006 113. · DOI
  103. Ankolekar, A., et al.: Clinician perspectives on clinical decision support systems in lung cancer: implications for shared decision-making. Health Expect. 25(4), 1342-1351 (2022). https://doi.org/10.1111/hex.13457 114. van Leeuwen, P.J., et al.: A multiparametric magnetic resonance imaging- based risk model to determine the risk of significant prostate cancer prior to biopsy. BJU Int. 120(6), 774-781 (2017). https://doi.org/10.1111/bju. 13814 115. · DOI
  104. Tosoian, J.J., et al.: Prediction of pathological stage based on clinical stage, serum prostate-specific antigen, and biopsy Gleason score: Partin Tables in the contemporary era. BJU Int. 119(5), 676-683 (2017). https://doi. org/10.1111/bju.13573 116. · DOI
  105. Garabedian, P.M., et al.: Human-centered design of clinical decision support for management of hypertension with chronic kidney disease. BMC Med. Inf. Decis. Making 22(1), 1-12 (2022). https://doi.org/10. 1186/s12911-022-01962-y · DOI

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Sorted by citations

Authors
Masood, A., Naseem, U., Rashid, J., Kim, J., & Razzak, I.
Affiliations
  • Boston Children's Hospital (US)
  • Macquarie University (AU)
  • Sejong University (KR)
  • Kongju National University (KR)
  • UNSW Sydney (AU)
Corresponding author
  • Rashid, Junaid
  • Kim, Jungeun
ORCID
Venue
CAAI Transactions on Intelligence Technology
Publisher
Institution of Engineering and Technology (IET)
Published
2024-02-06
ISSN
  • 2468-2322 (ISSN-L)
  • 2468-6557
DOI
10.1049/cit2.12286
OpenAlex
W4391611262
Cited by
14
References
6
Citations by year
2026: 9 · 2025: 4 · 2024: 1
FWCI
4.27
Type
journal article
Language
en
Open access
Licence
Locations
  • CAAI Transactions on Intelligence Technology (publishedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
Funders
  • National Research Foundation
  • National Research Foundation of Korea
  • Ministry of Science and ICT, South Korea
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
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Keywords
clinical decision support systems, machine learning in healthcare, disease detection, disease classification, clinical decision-making, patient-centered care, structured clinical data

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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