CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 120-127
DOI: 10.1055/s-0039-1677911
Section 5: Decision Support
Survey
Georg Thieme Verlag KG Stuttgart

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Stefania Montani
1   DISIT, Computer Science Institute, University of Piemonte Orientale, Alessandria, Italy
,
Manuel Striani
1   DISIT, Computer Science Institute, University of Piemonte Orientale, Alessandria, Italy
› Institutsangaben
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Publikationsverlauf

Publikationsdatum:
16. August 2019 (online)

Summary

Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to “classical" knowledge-based ones, and to consider the issues raised and their possible solutions.

Methods: We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies.

Results: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data.

Conclusions: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

 
  • References

  • 1 Osheroff JA, Teich JM, Levick D, Saldana L, Velasco F, Sittig D. , et al. Improving Outcome with Clinical Decision Support: An Implemented Guide, 2nd Ed. Chicago: Healthcare Information and Management Systems Society; 2012
  • 2 Peleg M, Tu S. Decision support, knowledge representation and management in medicine. Methods Inf Med 2006;45 Suppl 1:S72-80
  • 3 Barcelona Declaration for the Proper Development and Usage of Artificial Intelligence in Europe. 2017. Mar; 1-4
  • 4 Buchanan BG, Shortliffe EH. editors. Rule-based expert systems: The MYCIN experiments of the Stanford heuristic programming project. Reading, MA: Addison-Wesley; 1984
  • 5 Kulikowski CA, Weiss SM. Representation of Expert Knowledge for Consultation: The CASNET and EXPERT Projects. Chapter 2 in: Szolovits P. editor. Artificial Intelligence in Medicine. Boulder Colorado: Westview Press; 1982
  • 6 Hatzilygeroudis I, Prentzas J. Integrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems. Expert Syst Appl 2004; 27 (01) 63-75
  • 7 Shen Y, Colloc J, Jacquet-Andrieu A, Lei K. Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system. J Biomed Inform 2015; 56: 307-17
  • 8 Chen JH, Alagappan M, Goldstein MK, Asch SM, Altman RB. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets. Int J Med Inform 2017; 102: 71-9
  • 9 Shortliffe EH, Sepulveda MJ. Clinical decision support in the era of artificial intelligence. JAMA 2017; 320 (21) 2199-200
  • 10 Parisi L, Rayi Chandran N, Manaog ML. Feature-driven machine learning to improve early diagnosis of Parkinson’s disease. Expert Syst Appl 2018; 15 (110) 182-90
  • 11 Emre Aladag A, Muderrisoglu S, Akbas NB, Zahmacioglu O, Bingol HO. Detecting suicidal ideation on forums: Proof-of-concept study. J Med Internet Res 2018; 20 (06) e215
  • 12 Gil D, Girela JL, Azorin J, De Juan A, De Juan J. Identifying central and peripheral nerve fibres with an artificial intelligence approach. Appl Soft Comput 2018; 67: 276-85
  • 13 Srividya M, Mohanavalli S, Bhalaji N. Behavioral Modeling for Mental Health using Machine Learning Algorithms. J Med Syst 2018; 42 (05) 88
  • 14 Lopez B, Torrent-Fontbona F, Vinas R, Fernandez-Real JM. Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction. Artif Intell Med 2018; 85: 43-9
  • 15 Orjuela-Canon AD, Camargo Mendoza JE, Awad Garcia CE, Vergara Vela EP. Tuberculosis diagnosis support analysis for precarious health information systems. Comput Methods Programs Biomed 2018; 157: 11-7
  • 16 Hernandez-Medrano I, Tello J, Belda C, Urena A, Salcedo I, Espinosa-Anke L. , et al. Savana: Re-using Electronic Health Records with Artificial Intelligence. Int J Int Mult Art Int 2017; 4 (07) 8-12
  • 17 Wang H, Lv Y, Chen H, Li Y, Zhang Y, Lu Z. Smart pathological brain detection system by predator-prey particle swarm optimization and single-hidden layer neural-network. Multimed Tools Appl 2018; 77 (03) 3871-85
  • 18 Vukicevic AM, Jovicic GR, Jovicic MN, Milicevic VL, Filipovic ND. Assessment of cortical bone fracture resistance curves by fusing artificial neural networks and linear regression. Comput Methods Biomech Biomed Engin 2018; 21 (02) 169-76
  • 19 Dorado-Moreno M, Pérez-Ortiz M, Gutiérrez PA, Ciria R, Briceño J, Hervás-Martínez C. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem. Artif Intell Med 2017; 77: 1-11
  • 20 Wong B, Ho GTS, Tsui E. Development of an intelligent e-healthcare system for the domestic care industry. Indl Manage Data Syst 2017; 117 (07) 1426-45
  • 21 Yoon J, Zame WR, Banerjee A, Cadeiras M, Alaa AM, van der Schaar M. Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation. PLoS One 2018; 13 (03) e0194985 eCollection 2018
  • 22 Moreira MWL, Rodrigues JJPC, Kumar N, Al-Muhtadi J, Korotaev V. Nature-Inspired Algorithm for Training Multilayer Perceptron Networks in e-health Environments for High-Risk Pregnancy Care. J Med Syst 2018; 42 (03) 51
  • 23 Schetinin V, Jakaite L, Krzanowski W. Bayesian averaging over Decision Tree models for trauma severity scoring. Artif Intell Med 2018; 84: 139-45
  • 24 Kazemi Y, Mirroshandel SA. A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med 2018; 84: 117-26
  • 25 Hernandez B, Herrero P, Rawson TM, Moore LSP, Evans B, Toumazou C. , et al. Supervised learning for infection risk inference using pathology data. BMC Med Inform Decis Mak 2017; 17 (01) 168
  • 26 Valdes G, Simone 2nd CB, Chen J, Lin A, Yom SS, Pattison AJ. , et al. Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making. Radiother Oncol 2017; 125 (03) 392-7
  • 27 Saleh E, Blaszczynski J, Moreno A, Valls A, Romero-Aroca P, de la Riva-Fernandez S. , et al. Learning ensemble classifiers for diabetic retinopathy assessment. Artif Intell Med 2018; 85: 50-63
  • 28 Al-Jarrah MA, Shatnawi H. Non-proliferative diabetic retinopathy symptoms detection and classification using neural network. J Med Eng Technol 2017; 41 (06) 498-505
  • 29 Tang Q, Liu Y, Liu H. Medical image classification via multiscale representation learning. Artif Intell Med 2017; 79: 71-8
  • 30 Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S. , et al. Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging. Radiology 2017; 285 (03) 923-31
  • 31 Gharehbaghi A, Linden M, Babic A. A Decision Support System for Cardiac Disease Diagnosis Based on Machine Learning Methods. Stud Health Technol Inform 2017; 235: 43-7
  • 32 Zhao C, Jiang J, Xu Z, Guan Y. A study of EMR-based medical knowledge network and its applications. Comput Methods Programs Biomed 2017; 143: 13-23
  • 33 Horng S, Sontag DA, Halpern Y, Jernite Y, Shapiro NI, Nathanson LA. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS One 2017; 12 (04) e0174708 eCollection 2017
  • 34 Liu ESF, Wu VWC, Harris B, Foote M, Lehman M, Chan LWC. Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis. Med Dosim 2017; 42 (02) 85-9
  • 35 Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK. , et al. Fully Automated Deep Learning System for Bone Age Assessment. J Digit Imaging 2017; 30 (04) 427-41
  • 36 Ma S, Galatzer-Levy IR, Wang X, Fenyö D, Shalev AY. A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders. AMIA Annu Symp Proc 2017; 2016: 837-43
  • 37 Schiff GD, Volk LA, Volodarskaya M, Williams DH, Walsh L, Myers SG. , et al. Screening for medication errors using an outlier detection system. J Am Med Inform Assoc 2017; 24 (02) 281-7
  • 38 Nowakova J, Prilepok M, Snasel V. Medical Image Retrieval Using Vector Quantization and Fuzzy Stree. J Med Syst. 2017; 41 (02) 18 . Erratum in: J Med Syst 2018 May; 42(5):98
  • 39 Paydar K, Niakan Kalhori SR, Akbarian M, Sheikhtaheri A. A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus. Int J Med Inform 2017; 97: 239-46
  • 40 Rajkumar S, Muttan S, Sapthagirivasan V, Jaya V, Vignesh SS. Software intelligent system for effective solutions for hearing impaired subjects. Int J Med Inform 2017; 97: 152-62
  • 41 Dusenberry MW, Brown CK, Brewer KL. Artificial neural networks: Predicting head CT findings in elderly patients presenting with minor head injury after a fall. Am J Emerg Med 2017; 35 (02) 260-7
  • 42 Finkelstein J, Jeong IC. Machine learning approaches to personalize early prediction of asthma exacerbations. Ann N Y Acad Sci 2017; 1387 (01) 153-65
  • 43 Patel TA, Puppala M, Ogunti RO, Ensor JE, He T, Shewale JB. , et al. Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods. Cancer 2017; 123 (01) 114-21
  • 44 Yoon J, Davtyan C, van der Schaar M. Discovery and Clinical Decision Support for Personalized Healthcare. IEEE J Biomed Health Inform 2017; 21 (04) 1133-45
  • 45 Semenov I, Kopanitsa G. Decision Support System Based on FHIR Profiles. Stud Health Technol Inform 2018; 249: 117-21
  • 46 Isasi I, Irusta U, Elola A, Aramendi E, Ayala U, Alonso E, Kramer-Johansen J. , et al. A Machine Learning Shock Decision Algorithm for use during Piston-driven Chest Compressions. IEEE Trans Biomed Eng. 2018 Oct 31. doi: 10.1109/ TBME.2018.2878910. [Epub ahead of print]
  • 47 Hussain D, Al-Antari MA, Al-Masni MA, Han SM, Kim TS. Femur segmentation in DXA imaging using a machine learning decision tree. J Xray Sci Technol 2018; 26 (05) 727-46
  • 48 Shirwaikar RD. Estimation of Caffeine Regimens: A Machine Learning Approach for Enhanced Clinical Decision Making at a Neonatal Intensive Care Unit (NICU). Crit Rev Biomed Eng 2018; 46 (02) 93-115
  • 49 Oude Nijeweme-d’Hollosy W, van Velsen L, Poel M, Groothuis-Oudshoorn CGM, Soer R, Hermens H. Evaluation of three machine learning models for self-referral decision support on low back pain in primary care. Int J Med Inform 2018; 110: 31-41
  • 50 Jackson R, Kartoglu I, Stringer C, Gorrell G, Roberts A, Song X. , et al. CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital. BMC Med Inform Decis Mak 2018; 18 (01) 47
  • 51 Zeng Y, Liu X, Wang Y, Shen F, Liu S, Rastegar-Mojarad M. , et al. Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches. J Med Internet Res 2017; 19 (10) e342
  • 52 Vaccaro G, Peláez JI, Gil-Montoya JA. A novel expert system for objective masticatory efficiency assessment. PLoS One 2018; 13 (01) e0190386
  • 53 Montaña D, Campos-Roca Y, Pérez CJ. A Diadochokinesis-based expert system considering articulatory features of plosive consonants for early detection of Parkinson’s disease. Comput Methods Programs Biomed 2018; 154: 89-97
  • 54 Martín-González S, Navarro Mesa, Juliá-Serdá G, Kraemer JF, Wessel N, Ravelo-García AG. Heart rate variability feature selection in the presence of sleep apnea: An expert system for the characterization and detection of the disorder. Comput Biol Med 2017; 91: 47-58
  • 55 Chung CJ, Kuo YC, Hsieh YY, Li TC, Lin CC, Liang WM. , et al. Subject-enabled analytics model on measurement statistics in health risk expert system for public health informatics. Int J Med Inform 2017; 107: 18-29
  • 56 Mukhopadhyay A, Maliapen M, Ong V, Jakes RW, Mundy LM, Jialiang L. , et al. Community-Acquired Pneumonia Case Validation in an Anonymized Electronic Medical Record-Linked Expert System. Clin Infect Dis 2017; 64 (suppl_2): S141-4
  • 57 Guidi G, Maffei N, Vecchi C, Gottardi G, Ciarmatori A, Mistretta GM. , et al. Expert system classifier for adaptive radiation therapy in prostate cancer. Australas Phys Eng Sci Med 2017; 40 (02) 337-48
  • 58 Haupt F, Berding G, Namazian A, Wilke F, Böker A, Merseburger A. , et al. Expert System for Bone Scan Interpretation Improves Progression Assessment in Bone Metastatic Prostate Cancer. Adv Ther 2017; 34 (04) 986-94
  • 59 LeCun Y, Bengio Y, Hinton GE. Deep learning. Nature 2015; 521 (7553): 436-44
  • 60 Elkin PL, Schlegel DR, Anderson M, Komm J, Ficheur G, Bisson L. Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support. Appl Clin Inform 2018; 9 (02) 432-9
  • 61 Brown D, Aldea A, Harrison R, Martin C, Bayley I. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artif Intell Med 2018; 85: 28-42
  • 62 Shen Y, Yuan K, Chen D, Colloc J, Yang M, Li Y. , et al. An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription. Artif Intell Med 2018; 86: 20-32
  • 63 Thanathomwong B. Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment. Health Inform Res 2018; 24 (01) 22-8
  • 64 Segundo U, Aldámiz-Echevarría L, López-Cuadrado J, Buenestado D, Andrade F, Pérez TA. , et al. Improvement of newborn screening using a fuzzy inference system. Expert Syst Appl 2017; 78: 301-18
  • 65 Anselma L, Mazzei A, De Michieli F. An artificial intelligence framework for compensating transgressions and its application to diet management. J Biomed Inform 2017; 68: 58-70
  • 66 Mendez JA, Leon A, Marrero A, Gonzalez-Cava JM, Reboso JA, Estevez JI. , et al. Improving the anesthetic process by a fuzzy rule based medical decision system. Artif Intell Med 2018; 84: 159-70
  • 67 Shang Y, Wang Y, Gou L, Wu C, Zhou T, Li JS. Development of a Service-Oriented Sharable Clinical Decision Support System Based on Ontology for Chronic Disease. Stud Health Technol Inform 2017; 245: 1153-7
  • 68 Nakawala H, Ferrigno G, De Momi E. Development of an intelligent surgical training system for Thoracentesis. Artif Intell Med 2018; 84: 50-63
  • 69 Abidi S. A Knowledge-Modeling Approach to Integrate Multiple Clinical Practice Guidelines to Provide Evidence-Based Clinical Decision Support for Managing Comorbid Conditions. J Med Syst 2017; 41 (12) 193
  • 70 Hao SR, Geng SC, Fan LX, Chen JJ, Zhang Q, Li LJ. Intelligent diagnosis of jaundice with dynamic uncertain causality graph model. J Zhejiang Univ Sci B 2017; 18 (05) 393-401
  • 71 Hanke S, Kreiner K, Kropf J, Scase M, Gossy C. Reasoning and Data Representation in a Health and Lifestyle Support System. Stud Health Technol Inform 2017; 235: 8-12
  • 72 Zamborlini V, da Silveira M, Pruski C, Ten Teije A, Geleijn E, van der Leeden M. , et al. Analyzing interactions on combining multiple clinical guidelines. Artif Intell Med 2017; 81: 78-93
  • 73 Hossain MS, Ahmed F, Fatema-Tuj Johora, Andersson K. A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty. J Med Syst 2017; 41 (03) 43
  • 74 Vilhena J, Rosario Martins M, Vicente H, Graneda JM, Caldeira F, Gusmao R. , et al. An Integrated Soft Computing Approach to Hughes Syndrome Risk Assessment. J Med Syst 2017; 41 (03) 40
  • 75 Khozeimeh F, Alizadehsani R, Roshanzamir M, Khosravi A, Layegh P, Nahavandi S. An expert system for selecting wart treatment method. Comput Biol Med 2017; 81: 167-75
  • 76 Seol JW, Yi W, Choi J, Lee KS. Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries. Int J Med Inform 2017; 98: 1-12
  • 77 González Aguña A, Díaz Teruel V, Satamaría Pérez A, Gómez González JL, Jiménez Rodríguez ML, Santamaría García JM. , et al. Dx-Care: A Device to Help in the Diagnosis of Care Problems. Stud Health Technol Inform 2018; 250: 256-60
  • 78 Braido F, Santus P, Corsico AG, Di Marco F, Melioli G, Scichilone N. , et al. Chronic obstructive lung disease “expert system”: validation ofa predictive tool for assisting diagnosis. Int J Chron Obstruct Pulmon Dis 2018; 13: 1747-53
  • 79 Safdari R, Arpanahi HK, Langarizadeh M, Ghazisaiedi M, Dargahi H, Zendehdel K. Design a Fuzzy Rule-based Expert System to Aid Earlier Diagnosis of Gastric Cancer. Acta Inform Med 2018; 26 (01) 19-23
  • 80 Hassanzad M, Orooji A, Valinejadi A, Velayati A. A fuzzy rule-based expert system for diagnosing cystic fibrosis. Electron Physician 2017; 9 (12) 5974-84
  • 81 Marquet P, Bedu A, Monchaud C, Saint-Marcoux F, Rérolle JP, Etienne I. , et al. Pharmacokinetic Therapeutic Drug Monitoring of Advagraf in More Than 500 Adult Renal Transplant Patients, Using an Expert System Online. Ther Drug Monit 2018; 40 (03) 285-91
  • 82 Başciftci F, Avuclu E. An expert system design to diagnose cancer by using a new method reduced rule base. Comput Methods Programs Biomed 2018; 157: 113-20
  • 83 Vijay SAA, Ganesh Kumar P. Fuzzy Expert System based on a Novel Hybrid Stem Cell (HSC) Algorithm for Classification of Micro Array Data. J Med Syst 2018; 42 (04) 61
  • 84 Paredes R, Tzou PL, van Zyl G, Barrow G, Camacho R, Carmona S. , et al. Collaborative update of a rule-based expert system for HIV-1 genotypic resistance test interpretation. PLoS One 2017; 12 (07) e0181357
  • 85 Rahmani Katigari M, Ayatollahi H, Malek M, Kamkar Haghighi M. Fuzzy expert system for diagnosing diabetic neuropathy. World J Diabetes 2017; 8 (02) 80-8
  • 86 Dreyer KJ, Geis JR. When Machines Think: Radiology’s Next Frontier. Radiology 2017; 285 (03) 713-8
  • 87 Ali T, Sungyoung L. Reconciliation of SNOMED CT and domain clinical model for interoperable medical knowledge creation. Conf Proc IEEE Eng Med Biol Soc 2017 Jul; 2654-7
  • 88 Launchbury J. A DARPA perspective on artificial intelligence. DARPA Video [Internet]. 2017. Available from: https://www.youtube.com/watch?v=-O01G3tSYpU
  • 89 Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G. , et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women with Breast Cancer. JAMA 2017; 318 (22) 2199-10
  • 90 Peleg M, Shahar Y, Quaglini S, Broens T, Budasu R, Fung N. , et al. Assessment of a personalized and distributed patient guidance system. Int J Med Inform 2017; 101: 108-30
  • 91 Peleg M. Computer-interpretable clinical guidelines: A methodological review. J Biomed Inform 2013; 46 (04) 744-63
  • 92 Sacchi L, Lanzola G, Viani N, Quaglini S. Personalization and Patient Involvement in Decision Support Systems: Current Trends. Yearb Med Inform 2015; 10 (01) 106-18
  • 93 Shay LA, Lafata JE. Where is the evidence? A systematic review of shared decision making and patient outcomes. Med Decis Making 2015; 35 (01) 114-31
  • 94 McDermott MS, While AE. Maximizing the healthcare environment: a systematic review exploring the potential of computer technology to promote self-management of chronic illness in healthcare settings. Patient Educ Couns 2013; 92 (01) 13-22
  • 95 Middleton B, Sittig DF, Wright A. Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision. Yearb Med Inform 2016; Suppl 1: S103-16