IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research

Can the pharmaceutical industry reduce attrition rates?

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How many drug targets are there?

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Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047

Big data analytics in healthcare: promise and potential

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Clinical development success rates for investigational drugs

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Edward Jenner and the History of Smallpox and Vaccination

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Cognitive computing

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The $2.6 Billion Pill — Methodologic and Policy Considerations

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The role of serendipity in drug discovery

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National Health Expenditure Projections, 2014–24: Spending Growth Faster Than Recent Trends

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Keratoconus-susceptibility gene identification by corneal thickness genome-wide association study and artificial intelligence IBM Watson

Hosoda, Y., Miyake, M., Meguro, A., Tabara, Y., Iwai, S., Ueda‐Arakawa, N., Nakano, E., Mori, Y., Yoshikawa, M., Nakanishi, H., et al. (2020) · Communications Biology · doi:10.1038/s42003-020-01137-3 · Cited by 36

Metabolic adaptation to calorie restriction

Guijas, C., Montenegro-Burke, J. R., Cintrón-Colón, R., Domingo-Almenara, X., Sánchez-Alavez, M., Aguirre, C., Shankar, K., Majumder, E. L. ‐., Billings, E., Conti, B., et al. (2020) · Science Signaling · doi:10.1126/scisignal.abb2490 · Cited by 39

Smart Healthcare

Yin, H., Akmandor, A. O., Mosenia, A., & Jha, N. K. (2018) · Foundations and Trends® in Electronic Design Automation · doi:10.1561/1000000054 · Cited by 35

An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

Tang, V., Siu, P. K. Y., Choy, K. L., Lam, H. Y., Ho, G. T. S., Lee, C. K. M., & Tsang, Y. P. (2019) · Expert Systems · doi:10.1111/exsy.12369 · Cited by 35

Imaging Biobanks in Oncology: European Perspective

Neri, E., & Regge, D. (2016) · Future Oncology · doi:10.2217/fon-2016-0239 · Cited by 33

Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges

Babu, A., Onesimu, J. A., & Sagayam, K. M. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202129701074 · Cited by 33

Identification and interpretation of gait analysis features and foot conditions by explainable AI

Özateş, M. E., Yaman, A., Salami, F., Campos, S., Wolf, S. I., & Schneider, U. (2024) · Scientific Reports · doi:10.1038/s41598-024-56656-4 · Cited by 38

PAPA: A parallel programming assistant powered by IBM Watson cognitive computing technology

Memeti, S., & Sabri Pllana, A. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.01.001 · Cited by 32

Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering EducationPreprint

Abedi, M., Alshybani, I., Shahadat, M., & Murillo, M. S. (2023) · Qeios · doi:10.32388/md04b0 · Cited by 51

BDI personal medical assistant agents: The case of trauma tracking and alerting

Croatti, A., Montagna, S., Ricci, A., Gamberini, E., Albarello, V., & Agnoletti, V. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.12.002 · Cited by 31

Boundary work in value co-creation practices: the mediating role of cognitive assistants

Mele, C., Spena, T. R., Marzullo, M., & Ruggiero, A. (2021) · Journal of service management · doi:10.1108/josm-10-2020-0381 · Cited by 34

Knowledge-based problem solving in physical product development––A methodological review

Burggräf, P., Wagner, J., & Weißer, T. (2020) · Expert Systems with Applications X · doi:10.1016/j.eswax.2020.100025 · Cited by 30

Small molecule inhibitors of α-synuclein oligomers identified by targeting early dopamine-mediated motor impairment in C. elegans

Chen, K. S., Menezes, K. S., Rodgers, J. B., O’Hara, D. M., Tran, N. A., Fujisawa, K., Ishikura, S., Khodaei, S., Chau, H., Cranston, A., et al. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00497-6 · Cited by 30

Doctor Ex Machina: A Critical Assessment of the Use of Artificial Intelligence in Health Care

Svensson, A. M., & Jotterand, F. (2021) · The Journal of Medicine and Philosophy A Forum for Bioethics and Philosophy of Medicine · doi:10.1093/jmp/jhab036 · Cited by 30

A treatment recommender clinical decision support system for personalized medicine: method development and proof-of-concept for drug resistant tuberculosis

Verboven, L., Calders, T., Callens, S. F. J., Black, J. M., Maartens, G., Dooley, K. E., Potgieter, S. T., Warren, R. M., Laukens, K., & Rie, A. V. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01790-0 · Cited by 30

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Truong, T. T. T., Panizzutti, B., Kim, J. H., & Walder, K. R. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14071464 · Cited by 33

Determinants of artificial intelligence-assisted diagnostic system adoption intention: A behavioral reasoning theory perspective

Li, W., & Wang, J. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102643 · Cited by 28

The DIY Digital Medical Centre

Timmis, K. N., & Timmis, K. N. (2017) · Microbial Biotechnology · doi:10.1111/1751-7915.12817 · Cited by 29

Decision-making under uncertainty in environmental health policy: new approaches

Reis, J. J., & Spencer, P. S. (2019) · Environmental Health and Preventive Medicine · doi:10.1186/s12199-019-0813-9 · Cited by 29

Utilisation of district health information system and its associated factors among health professionals working at public health facilities of the southwest of Ethiopia: cross-sectional survey

Kanfe, S. G., Debele, G. R., Berhanu, R. D., Ngusie, H. S., & Ahmed, M. H. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-046578 · Cited by 29

What Can Big Data Offer the Pharmacovigilance of Orphan Drugs?

Price, J. (2016) · Clinical Therapeutics · doi:10.1016/j.clinthera.2016.11.009 · Cited by 31

Improved Kalman filter based differentially private streaming data release in cognitive computing

Wang, J., Luo, J., Liu, X., Li, Y., Liu, S., Zhu, R., & Anjum, A. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.03.050 · Cited by 28

Artificial Intelligence Empowered Cyber Threat Detection and Protection for Power Utilities

Hasan, K., Shetty, S. S., & Ullah, S. (2019) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic48465.2019.00049 · Cited by 28

A Cognitive-Driven Building Renovation for Improving Energy Efficiency: The Experience of the ELISIR Project

Rinaldi, S., Bellagente, P., Ciribini, A. L. C., Tagliabue, L. C., Poli, T., Mainini, A. G., Speroni, A., Cadena, J. D. B., & Spagnolo, S. L. (2020) · Electronics · doi:10.3390/electronics9040666 · Cited by 28

Automated clinical knowledge graph generation framework for evidence based medicine.

Alam, F., Giglou, H. B., & Malik, K. M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120964 · Cited by 27

A Decision Support System with Intelligent Recommendation for Multi-disciplinary Medical Treatment

Zhu, N., Cao, J. D., Shen, K., Chen, X., & Zhu, S. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3352573 · Cited by 27

Optimizing drug development in oncology by clinical trial simulation: Why and how?

Gal, J., Milano, G. A., Ferrero, J.-­., Saâda-Bouzid, E., Viotti, J., Chabaud, S., Gougis, P., Tourneau, C. L., Schiappa, R., Paquet, A. C., et al. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx055 · Cited by 28

Evaluating the effect of artificial intelligence on pharmaceutical product and drug discovery in China

Sampene, A. K., & Nyirenda, F. (2024) · Future Journal of Pharmaceutical Sciences · doi:10.1186/s43094-024-00632-2 · Cited by 28

Towards FAIRer Biological Knowledge Networks Using a Hybrid Linked Data and Graph Database Approach

Brandizi, M., Singh, A., Rawlings, C., & Hassani‐Pak, K. (2018) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2018-0023 · Cited by 26

AGATHA: Automatic Graph Mining And Transformer based Hypothesis Generation Approach

Sybrandt, J., Tyagin, I., Shtutman, M., & Safro, I. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3412684 · Cited by 26

Cognitive analysis of metabolomics data for systems biology

Majumder, E. L. ‐., Billings, E., Benton, H. P., Martin, R. L., Palermo, A., Guijas, C., Rinschen, M. M., Domingo-Almenara, X., Montenegro-Burke, J. R., Tagtow, B. A., et al. (2021) · Nature Protocols · doi:10.1038/s41596-020-00455-4 · Cited by 28

Evaluating Clinical Genome Sequence Analysis by Watson for Genomics

Itahashi, K., Kondo, S., Kubo, T., Fujiwara, Y., Kato, M., Ichikawa, H., Koyama, T., Tokumasu, R., Xu, J. L., Huettner, C. S., et al. (2018) · Frontiers in Medicine · doi:10.3389/fmed.2018.00305 · Cited by 26

Concordance between treatment recommendations provided by IBM Watson for Oncology and a multidisciplinary tumor board for breast cancer in China

Zhao, X., Zhang, Y., Ma, X., Chen, Y., Xi, J., Yin, X., Kang, H., Guan, H., Dai, Z., Liu, D., et al. (2020) · Japanese Journal of Clinical Oncology · doi:10.1093/jjco/hyaa051 · Cited by 25

Introduction to cognitive computing and its various applications

Palwe, S., & Gunjal, A. S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85769-7.00009-4 · Cited by 29

Digital Diabetes Data and Artificial Intelligence: A Time for Humility Not Hubris

Kerr, D. E., & Klonoff, D. C. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818796508 · Cited by 26

Bots and Beasts

Thagard, P. R. (2021) · The MIT Press eBooks · doi:10.7551/mitpress/14102.001.0001 · Cited by 24

A case study extension methodology for performance measurement diagnosis in nonprofit organizations

Cestari, J. M. A. P., de Lima, E. P., Deschamps, F., Aken, E. M. V., Treinta, F. T., & Moura, L. F. (2018) · International Journal of Production Economics · doi:10.1016/j.ijpe.2018.06.018 · Cited by 25

Holonic Reengineering to Foster Sustainable Cyber-Physical Systems Design in Cognitive Manufacturing

Martín-Gómez, A. M., Ávila-Gutiérrez, M. J., & Aguayo-González, F. (2021) · Applied Sciences · doi:10.3390/app11072941 · Cited by 27

Using the IBM SPSS SW Tool with Wavelet Transformation for CO2 Prediction within IoT in Smart Home Care

Vaňuš, J., Kubíček, J., Gorjani, O. M., & Koziorek, J. (2019) · Sensors · doi:10.3390/s19061407 · Cited by 23

Emerging opportunities to harness real world data: An introduction to data sources, concepts, and applications

O'Leary, C. P., & Cavender, M. A. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13948 · Cited by 23

Validation of a Visual-Based Analytics Tool for Outcome Prediction in Polytrauma Patients (WATSON Trauma Pathway Explorer) and Comparison with the Predictive Values of TRISS

Niggli, C., Pape, H., Niggli, P., & Mica, L. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10102115 · Cited by 23

Pharmacovigilance in Crisis: Drug Safety at a Crossroads

Price, J. (2018) · Clinical Therapeutics · doi:10.1016/j.clinthera.2018.02.013 · Cited by 23

IoT Based Development Boards for Smart Healthcare Applications

Bansal, M., & Gandhi, B. (2018) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2018.8777572 · Cited by 23

Energy systems engineering - a guided tour

Demirhan, C. D., Tso, W. W., Ogumerem, G. S., & Pistikopoulos, E. N. (2019) · BMC Chemical Engineering · doi:10.1186/s42480-019-0009-5 · Cited by 22

Microbial Metabolite 3-Indolepropionic Acid Mediates Immunosuppression

Guijas, C., Horton, L. E., Hoang, L. T., Domingo-Almenara, X., Billings, E., Ware, B. C., Sullivan, B. M., & Siuzdak, G. E. (2022) · Metabolites · doi:10.3390/metabo12070645 · Cited by 20

A lightweight acquisition of expert rules for interoperable clinical decision support systems

Cánovas-Segura, B., Morales, A., Juárez, J. M., Campos, M., & Palacios, F. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.01.007 · Cited by 22

Enabling artificial intelligence in high acuity medical environments

Kasparick, M., Andersen, B., Franke, S., Rockstroh, M., Golatowski, F., Timmermann, D., Ingenerf, J., & Neumuth, T. (2019) · Minimally Invasive Therapy & Allied Technologies · doi:10.1080/13645706.2019.1599957 · Cited by 21

Deep learning techniques for optimizing medical big data

Tariq, M. I., Tayyaba, S., Ashraf, M. W., & Bălaş, V. E. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819061-6.00008-2 · Cited by 23

The role of innovation ambidexterity on the relationship between cognitive computing capabilities and entrepreneurial quality: A comparative study of India and China

Gupta, S., Modgil, S., Wong, C. W. Y., & Kar, A. K. (2023) · Technovation · doi:10.1016/j.technovation.2023.102835 · Cited by 22

Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities

Crisafulli, S., Ciccimarra, F., Bellitto, C., Carollo, M., Carrara, E., Stagi, L., Triola, R., Capuano, A., Chiamulera, C., Moretti, U., et al. (2024) · Frontiers in Drug Safety and Regulation · doi:10.3389/fdsfr.2024.1356405 · Cited by 29

Data resources for the identification and interpretation of actionable mutations by clinicians

Prawira, A., Pugh, T. J., Stockley, T. L., & Siu, L. L. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx023 · Cited by 21

Knowledge-based biomedical Data Science

Hunter, L. (2017) · Data Science · doi:10.3233/ds-170001 · Cited by 21

A Personal Medical Digital Assistant Agent for Supporting Human Operators in Emergency Scenarios

Croatti, A., Montagna, S., & Ricci, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70887-4_4 · Cited by 21

Diabetic complication prediction using a similarity-enhanced latent Dirichlet allocation model

Ding, S., Li, Z., Liu, X., Huang, H., & Yang, S. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.037 · Cited by 21

Yapay Zeka İle Sanatsal Üretim Pratiğinde Sanatçının Rolü ve Değişen Sanat Olgusu

GÜNEY, E., & Yavuz, H. (2021) · Sanat ve Tasarım Dergisi · doi:10.18603/sanatvetasarim.719639 · Cited by 20

Innovations in intellectual property rights management

Modic, D., Hafner, A., Damij, N., & Zajc, L. Č. (2019) · European Journal of Management and Business Economics · doi:10.1108/ejmbe-12-2018-0139 · Cited by 20

A Cognitive Enabled, Edge-Computing Architecture for Future Generation IoT Environments

Cicirelli, F., Guerrieri, A., Spezzano, G., & Vinci, A. (2019) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2019.8767246 · Cited by 20

Novel dynamic load balancing algorithm for cloud-based big data analytics

Aghdashi, A., & Mirtaheri, S. L. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-04024-8 · Cited by 20

Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering EducationPreprint

Abedi, M., Alshybani, I., Shahadat, M., & Murillo, M. S. (2023) · Qeios · doi:10.32388/md04b0.2 · Cited by 21

Augmented Intelligence: Enhancing Human Decision Making

Kim, J. J., Davis, T., & Hong, L. (2022) · Educational communications and technology: issues and innovations · doi:10.1007/978-3-030-84729-6_10 · Cited by 28

Teeth Reconstruction Using Artificial Intelligence: Trends, Perspectives, and Prospects

Chau, R. C. W., Thu, K. M., Hsung, T.-C., & Lam, W. Y. H. (2023) · Journal of the California Dental Association · doi:10.1080/19424396.2023.2199910 · Cited by 21

AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions

Zeng, Q., Huang, C., & Zhu, J. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1656603 · Cited by 20

Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation

Sanapalli, B. K. R., Palit, S., Deshpande, A., Tokala, R., Sigalapalli, D. K., & Sanapalli, V. (2026) · Antibiotics · doi:10.3390/antibiotics15020233 · Cited by 18

Reflection machines: increasing meaningful human control over Decision Support Systems

Cornelissen, N. A. J., van Eerdt, R. J. M., Schraffenberger, H., & Haselager, P. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09645-y · Cited by 20

The Role of Artificial Intelligence in Digital Health

Chang, A. C. (2023) · Health informatics · doi:10.1007/978-3-031-33902-8_6 · Cited by 19

Part of the Steamroller and Not Part of the Road: Better Blood Pressure Management Through Automation

Joosten, A., & Rinehart, J. B. (2017) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000002201 · Cited by 18

COBIT 5 compliance: best practices cognitive computing risk assessment and control checklist

Wyk, J. V., & Rudman, R. J. (2019) · Meditari Accountancy Research · doi:10.1108/medar-04-2018-0325 · Cited by 18

Integration of Federated Learning and Blockchain in Healthcare: A TutorialPreprint

Shahsavari, Y., Dambri, O. A., Baseri, Y., Hafid, A. S., & Makrakis, D. (2024) · doi:10.36227/techrxiv.171340632.25905526/v1 · Cited by 20

Digital transformation in the economics of complexity: the role of predictive models in strategic management

Iscaro, V., Castaldi, L., Maresca, P., & Mazzoni, C. (2021) · Journal of strategy and management · doi:10.1108/jsma-02-2021-0059 · Cited by 25

AI Assisted Clinical Diagnosis Treatment, and Development Strategy

Kong, M. C., He, Q., & Li, L. (2018) · Strategic Study of CAE · doi:10.15302/j-sscae-2018.02.013 · Cited by 17

Achieving veracity: A study of the development and use of an information system for data analysis in preventive healthcare

Mønsted, T. (2018) · Health Informatics Journal · doi:10.1177/1460458218796665 · Cited by 16

Bidirectional Cognitive Computing Model for Uncertain Concepts

Xu, C., & Wang, G. (2019) · Cognitive Computation · doi:10.1007/s12559-019-09666-8 · Cited by 16

A Survey of Fog Computing: Fundamental, Architecture, Applications and Challenges

Sanketh, R. S., Roopa, Y. M., & Reddy, P. V. N. (2019) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac47947.2019.9032645 · Cited by 16

Systematic risk identification and assessment using a new risk map in pharmaceutical R&D

Schuhmacher, A., Brieke, C., Gassmann, O., Hinder, M., & Hartl, D. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.06.015 · Cited by 17

Harnessing an Artificial Intelligence Platform to Dynamically Individualize Combination Therapy for Treating Colorectal Carcinoma in a Rat Model

Ding, X., Chang, V. H. S., Li, Y., Li, X., Xu, H., Ho, C., Ho, D., & Yen, Y. (2019) · Advanced Therapeutics · doi:10.1002/adtp.201900127 · Cited by 17

Multi‐Level Control of Conductive Filament Evolution and Enhanced Resistance Controllability of the Cu‐Cone Structure Embedded Conductive Bridge Random Access Memory

Kim, H. J., Kim, J., Park, T. G., Yoon, J. H., & Hwang, C. S. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100209 · Cited by 16

CIT: Integrated cognitive computing and cognitive agent technologies based cognitive architecture for human-like functionality in artificial systems

Chandiok, A., & Chaturvedi, D. K. (2018) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2018.07.020 · Cited by 15

Why Are We Still Creating Individual Case Safety Reports?

Streefland, M. (2018) · Clinical Therapeutics · doi:10.1016/j.clinthera.2018.10.012 · Cited by 15

Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance

Rudie, J. D., Duda, J., Duong, M. T., Chen, P., Xie, L., Kurtz, R. M., Ware, J. B., Choi, J., Mattay, R. R., Botzolakis, E. J., et al. (2021) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00470-1 · Cited by 17

Analysis of Artificial Intelligence and Natural Language Processing Significance as Expert Systems Support for E-Health Using Pre-Train Deep Learning Models

Mah, P. M. (2022) · Acadlore Transactions on AI and Machine Learning · doi:10.56578/ataiml010201 · Cited by 16

New challenges to psycho‐oncology research: Precision medicine oncology and targeted therapies

McFarland, D. C., Blackler, L., & Holland, J. C. (2017) · Psycho-Oncology · doi:10.1002/pon.4371 · Cited by 14

On the Logical Design of a Prototypical Data Lake System for Biological Resources

Che, H., & Duan, Y. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.553904 · Cited by 14

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

Islam, M. R., Prome, S. A., Ratan, M. R., Kamal, A. R. M., & Xu, G. (2021) · Human-Centric Intelligent Systems · doi:10.2991/hcis.k.210704.003 · Cited by 14

Der intelligente Operationssaal

Kenngott, H., Wagner, M., Preukschas, A. A., & Müller-Stich, B. P. (2016) · Der Chirurg · doi:10.1007/s00104-016-0308-9 · Cited by 14

Redesigning and Reinvention of Retail Industry Through Artificial Intelligence (AI)

Mukthar, K. P. J., Sivasubramanian, K., Ramirez-Asís, E., & Muñoz, M. E. G. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-99000-8_3 · Cited by 16

AI-Powered Business Intelligence Dashboards : A Cross-Sector Analysis of Transformative Impact and Future Directions

Jain, A. M. (2023) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit23564514 · Cited by 18

A Conceptual Framework for Integration of Evidence-Based Design with Lighting Simulation Tools

Davoodi, A., Johansson, P., Henricson, M., & Aries, M. M. (2017) · Buildings · doi:10.3390/buildings7040082 · Cited by 13

The concept of justifiable healthcare and how big data can help us to achieve it

Biesen, W. V., Straeten, C. V. D., Sterckx, S., Steen, J., Diependaele, L., & Decruyenaere, J. M. A. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01444-7 · Cited by 13

The evolution of personalized medicine: literature review

Dolgopolov, I. S., & Rykov, M. Y. (2022) · Research and Practical Medicine Journal · doi:10.17709/2410-1893-2022-9-3-9 · Cited by 14

Complementing Agents with Cognitive Services: A Case Study in Healthcare

Montagna, S., Mariani, S., Gamberini, E., Ricci, A., & Zambonelli, F. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01621-7 · Cited by 14

A Survey of Process Mining Competitions: The BPI Challenges 2011–2018

Lopes, I. F., & Ferreira, D. R. (2019) · Lecture notes in business information processing · doi:10.1007/978-3-030-37453-2_22 · Cited by 12

Developing Concept Enriched Models for Big Data Processing Within the Medical Domain

Gudivada, A., Philips, J., & Tabrizi, N. (2020) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.2020070105 · Cited by 12

Modern Information Technology for Cancer Research: What’s in IT for Me? An Overview of Technologies and Approaches

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Hybrid natural language processing for high-performance patent and literature mining in IBM Watson for Drug Discovery

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