Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review
Open accessPublished version of a preprint
Abstract
Background: People with cancer frequently experience severe and distressing symptoms associated with cancer and its treatments. Predicting symptoms in patients with cancer continues to be a significant challenge for both clinicians and researchers. The rapid evolution of machine learning (ML) highlights the need for a current systematic review to improve cancer symptom prediction. Objective: This systematic review aims to synthesize the literature that has used ML algorithms to predict the development of cancer symptoms and to identify the predictors of these symptoms. This is essential for integrating new developments and identifying gaps in existing literature. Methods: We conducted this systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. We conducted a systematic search of CINAHL, Embase, and PubMed for English records published from 1984 to August 11, 2023, using the following search terms: cancer, neoplasm, specific symptoms, neural networks, machine learning, specific algorithm names, and deep learning. All records that met the eligibility criteria were individually reviewed by 2 coauthors, and key findings were extracted and synthesized. We focused on studies using ML algorithms to predict cancer symptoms, excluding nonhuman research, technical reports, reviews, book chapters, conference proceedings, and inaccessible full texts. Results: A total of 42 studies were included, the majority of which were published after 2017. Most studies were conducted in North America (18/42, 43%) and Asia (16/42, 38%). The sample sizes in most studies (27/42, 64%) typically ranged from 100 to 1000 participants. The most prevalent category of algorithms was supervised ML, accounting for 39 (93%) of the 42 studies. Each of the methods—deep learning, ensemble classifiers, and unsupervised ML—constituted 3 (3%) of the 42 studies. The ML algorithms with the best performance were logistic regression (9/42, 17%), random forest (7/42, 13%), artificial neural networks (5/42, 9%), and decision trees (5/42, 9%). The most commonly included primary cancer sites were the head and neck (9/42, 22%) and breast (8/42, 19%), with 17 (41%) of the 42 studies not specifying the site. The most frequently studied symptoms were xerostomia (9/42, 14%), depression (8/42, 13%), pain (8/42, 13%), and fatigue (6/42, 10%). The significant predictors were age, gender, treatment type, treatment number, cancer site, cancer stage, chemotherapy, radiotherapy, chronic diseases, comorbidities, physical factors, and psychological factors. Conclusions: This review outlines the algorithms used for predicting symptoms in individuals with cancer. Given the diversity of symptoms people with cancer experience, analytic approaches that can handle complex and nonlinear relationships are critical. This knowledge can pave the way for crafting algorithms tailored to a specific symptom. In addition, to improve prediction precision, future research should compare cutting-edge ML strategies such as deep learning and ensemble methods with traditional statistical models.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI model performance varies based on population characteristics, data completeness, and external validation.
“Model performance varies based on population characteristics, data completeness, and external validation.18”
From Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation. (2026) · cites this paper as [18]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
2 retracted
Zhou, S., Fei, S., Han, H.-H., Li, J., Yang, S., & Zhao, C. (2021) · BioMed Research International · doi:10.1155/2021/6666453 · Cited by 21 · see citing works
RetractedHu, C., Li, Q., Shou, J., Zhang, F.-X., Li, X., Wu, M., Xu, M.-J., & Xu, L. (2021) · BioMed Research International · doi:10.1155/2021/9201235 · Cited by 15 · see citing works
RetractedSung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699 · see citing works
Machine learning algorithm validation with a limited sample size
Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0224365 · Cited by 1,901 · see citing works
Integration of oncology and palliative care: a Lancet Oncology Commission
Kaasa, S., Loge, J. H., Aapro, M., Albreht, T., Anderson, R., Bruera, E. D., Brunelli, C., Caraceni, A. T., Cervantes, A. M. R., Currow, D. C., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30415-7 · Cited by 834 · see citing works
Are the symptoms of cancer and cancer treatment due to a shared biologic mechanism?
Cleeland, C. S., Bennett, G. J., Dantzer, R., Dougherty, P. M., Dunn, A. J., Meyers, C. A., Miller, A. H., Payne, R., Reuben, J. M., Wang, X. S., et al. (2003) · Cancer · doi:10.1002/cncr.11382 · Cited by 528 · see citing works
Symptom Burden: Multiple Symptoms and Their Impact as Patient-Reported Outcomes
Cleeland, C. S. (2007) · JNCI Monographs · doi:10.1093/jncimonographs/lgm005 · Cited by 455 · see citing works
The Role of Artificial Intelligence in Early Cancer Diagnosis
Hunter, B., Hindocha, S., & Lee, R. W. (2022) · Cancers · doi:10.3390/cancers14061524 · Cited by 367 · see citing works
Balki, I., Amirabadi, A., Levman, J., Martel, A. L., Emersic, Z., Meden, B., Garcia-Pedrero, A., Ramirez, S. C., Kong, D., Moody, A. R., et al. (2019) · Canadian Association of Radiologists Journal · doi:10.1016/j.carj.2019.06.002 · Cited by 299 · see citing works
Cabitza, F., Campagner, A., Soares, F., García de Guadiana-Romualdo, L., Challa, F., Sulejmani, A., Seghezzi, M., & Carobene, A. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106288 · Cited by 284 · see citing works
Machine Learning (ML) in Medicine: Review, Applications, and Challenges
Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Naqvi, R. A. (2021) · Mathematics · doi:10.3390/math9222970 · Cited by 275 · see citing works
Salivary Gland Hypofunction and Xerostomia in Head and Neck Radiation Patients
Jensen, S. B., Vissink, A., Limesand, K. H., & Reyland, M. E. (2019) · JNCI Monographs · doi:10.1093/jncimonographs/lgz016 · Cited by 229 · see citing works
A Scoping Review of Machine Learning in Psychotherapy Research
Aafjes-van Doorn, K., Kamsteeg, C., Bate, J., & Aafjes, M. (2021) · Psychotherapy Research · doi:10.1080/10503307.2020.1808729 · Cited by 179 · see citing works
Gabryś, H. S., Buettner, F., Sterzing, F., Hauswald, H., & Bangert, M. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00035 · Cited by 173 · see citing works
Artificial Intelligence-Assisted Gastroenterology— Promises and Pitfalls
Ruffle, J. K., Farmer, A. D., & Aziz, Q. (2018) · The American Journal of Gastroenterology · doi:10.1038/s41395-018-0268-4 · Cited by 161 · see citing works
van Dijk, L. V., Brouwer, C. L., van der Schaaf, A., Burgerhof, J. G. M., Beukinga, R. J., Langendijk, J. A., Sijtsema, N. M., & Steenbakkers, R. J. H. M. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.07.007 · Cited by 125 · see citing works
Sheikh, K., Lee, S. H., Cheng, Z., Lakshminarayanan, P., Peng, L. C., Han, P., McNutt, T., Quon, H., & Lee, J. (2019) · Radiation Oncology · doi:10.1186/s13014-019-1339-4 · Cited by 111 · see citing works
Men, K., Geng, H., Zhong, H., Yong Fan, A., Lin, A., & Xiao, Y. (2019) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2019.06.009 · Cited by 102 · see citing works
van Dijk, L. V., Thor, M., Steenbakkers, R. J. H. M., Apte, A., Zhai, T., Borra, R., Noordzij, W., Estilo, C. L., Lee, N. Y., Langendijk, J. A., et al. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.06.012 · Cited by 97 · see citing works
Depression and prostate cancer: A focused review for the clinician
Fervaha, G., Izard, J., Tripp, D. A., Rajan, S., Leong, D. P., & Siemens, D. R. (2019) · Urologic Oncology Seminars and Original Investigations · doi:10.1016/j.urolonc.2018.12.020 · Cited by 97 · see citing works
Abdollahi, H., Mostafaei, S., Cheraghi, S., Shiri, I., Mahdavi, S. R., & Kazemnejad, A. (2018) · Physica Medica · doi:10.1016/j.ejmp.2017.10.008 · Cited by 95 · see citing works
Symptom clusters in patients receiving chemotherapy: A systematic review
Harris, C., Kober, K. M., Conley, Y. P., Dhruva, A., Hammer, M. J., & Miaskowski, C. A. (2021) · BMJ Supportive & Palliative Care · doi:10.1136/bmjspcare-2021-003325 · Cited by 94 · see citing works
Physical symptoms and working performance in female breast cancer survivors: a systematic review
Zomkowski, K., de Souza, B. C., da Silva, F. P., Moreira, G. M., Cunha, N. D. S., & Sperandio, F. F. (2017) · Disability and Rehabilitation · doi:10.1080/09638288.2017.1300950 · Cited by 86 · see citing works
Arroyo, O., Vaíllo, Y. A., Martínez, P., & Garrido, M. J. G. (2018) · Supportive Care in Cancer · doi:10.1007/s00520-018-4394-8 · Cited by 81 · see citing works
Lee, S. H., Han, P., Hales, R. K., Voong, K. R., Noro, K., Sugiyama, S., Haller, J., McNutt, T., & Lee, J. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab8531 · Cited by 68 · see citing works
Lötsch, J., Sipilä, R. M., Tasmuth, T., Kringel, D., Estlander, A., Meretoja, T. J., Kalso, E. A., & Ultsch, A. (2018) · Breast Cancer Research and Treatment · doi:10.1007/s10549-018-4841-8 · Cited by 67 · see citing works
Jiang, W., Lakshminarayanan, P., Hui, X., Han, P., Cheng, Z., Bowers, M., Shpitser, I., Siddiqui, S., Taylor, R. H., Quon, H., et al. (2018) · Advances in Radiation Oncology · doi:10.1016/j.adro.2018.11.008 · Cited by 64 · see citing works
Symptom Management and Palliative Care for Patients with Cancer
Yates, P. M. (2017) · Nursing Clinics of North America · doi:10.1016/j.cnur.2016.10.006 · Cited by 59 · see citing works
Juwara, L., Arora, N., Gornitsky, M., Saha‐Chaudhuri, P., & Velly, A. M. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104170 · Cited by 56 · see citing works
Insomnia and Fatigue Symptom Trajectories in Breast Cancer: A Longitudinal Cohort Study
Bean, H. R., Diggens, J., Ftanou, M., Weihs, K. L., Stanton, A. L., & Wiley, J. F. (2021) · Behavioral Sleep Medicine · doi:10.1080/15402002.2020.1869005 · Cited by 55 · see citing works
Tam, W., Tang, A., Woo, B., & Goh, Y. S. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-026271 · Cited by 49 · see citing works
Developing and validating a prediction model for lymphedema detection in breast cancer survivors
Wei, X., Lu, Q., Jin, S., Li, F., Zhao, Q., Cui, Y., Jin, S., Cao, Y., & Fu, M. R. (2021) · European Journal of Oncology Nursing · doi:10.1016/j.ejon.2021.102023 · Cited by 49 · see citing works
Kourou, K. D., Manikis, G. C., Poikonen‐Saksela, P., Mazzocco, K., Pat‐Horenczyk, R., Sousa, B., Oliveira-Maia, A. J., Mattson, J., Roziner, I., Pettini, G., et al. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104266 · Cited by 41 · see citing works
Masukawa, K., Aoyama, M., Yokota, S., Nakamura, J., Ishida, R., Nakayama, M., & Miyashita, M. (2022) · Palliative Medicine · doi:10.1177/02692163221105595 · Cited by 41 · see citing works
On, J., Park, H., & Yoo, S. (2021) · European Journal of Oncology Nursing · doi:10.1016/j.ejon.2021.102066 · Cited by 39 · see citing works
Prediction of vestibular schwannoma recurrence using artificial neural network
Abouzari, M., Goshtasbi, K., Sarna, B., Khosravi, P., Reutershan, T., Mostaghni, N., Lin, H. W., & Djalilian, H. R. (2020) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.362 · Cited by 38 · see citing works
Coping Strategy Influences Quality of Life in Patients With Advanced Lung Cancer by Mediating Mood
van de Wiel, M., Derijcke, S., Galdermans, D., Daenen, M., Surmont, V. F. M., Droogh, E. D., Lefebure, A., Saenen, E., Vandenbroucke, E., Morel, A.-M., et al. (2020) · Clinical Lung Cancer · doi:10.1016/j.cllc.2020.09.010 · Cited by 37 · see citing works
Saeidzadeh, S., Kamalumpundi, V., Chi, N., Nair, R., & Gilbertson‐White, S. (2021) · Palliative Medicine · doi:10.1177/02692163211006317 · Cited by 37 · see citing works
Self-citationAhmed, H. A., Soliman, H., & Elmogy, M. M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105622 · Cited by 36 · see citing works
Symptom Clusters in Patients With Gynecologic Cancer Receiving Chemotherapy
Pozzar, R. A., Hammer, M. J., Cooper, B. A., Kober, K. M., Chen, L.-M., Paul, S. M., Conley, Y. P., Levine, J. D., & Miaskowski, C. A. (2021) · Oncology nursing forum · doi:10.1188/21.onf.441-452 · Cited by 34 · see citing works
Abu‐Odah, H., Molassiotis, A., & Liu, J. Y. W. (2022) · BMC Palliative Care · doi:10.1186/s12904-022-00959-8 · Cited by 33 · see citing works
Liu, Y. H., Jin, J., & Liu, Y. J. (2021) · Supportive Care in Cancer · doi:10.1007/s00520-021-06657-0 · Cited by 30 · see citing works
Low, C. A., Li, M., Vega, J., Durica, K. C., Ferreira, D., Tam, V., Hogg, M. E., Zeh, H. J., Doryab, A., & Dey, A. K. (2021) · JMIR Cancer · doi:10.2196/27975 · Cited by 29 · see citing works
Learning from data to predict future symptoms of oncology patients
Papachristou, N., Puschmann, D., Barnaghi, P. M., Cooper, B. A., Hu, X., Maguire, R., Apostolidis, K., Conley, Y. P., Hammer, M. J., Katsaragakis, S., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0208808 · Cited by 28 · see citing works
Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes
Wang, Y., Canahuate, G. M., van Dijk, L. V., Mohamed, A., Fuller, C. D., Zhang, X., & Marai, G. E. (2021) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1145/3472163.3472177 · Cited by 28 · see citing works
Xu, X., Lu, J., Xu, Q., Hua, H., Xu, L., & Chen, L. (2020) · Supportive Care in Cancer · doi:10.1007/s00520-020-05483-0 · Cited by 25 · see citing works
Olling, K., Nyeng, D. W., & Wee, L. (2018) · Technical Innovations & Patient Support in Radiation Oncology · doi:10.1016/j.tipsro.2018.01.002 · Cited by 24 · see citing works
Moscato, S., Orlandi, S., Giannelli, A., Ostan, R., & Chiari, L. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871990 · Cited by 24 · see citing works
Sun, C., Li, M., Lan, L., Pei, L., Zhang, Y., Tan, G., Zhang, Z., & Huang, Y. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1096468 · Cited by 22 · see citing works
Breast Cancer Patients' Depression Prediction by Machine Learning Approach
Cvetković, J. (2017) · Cancer Investigation · doi:10.1080/07357907.2017.1363892 · Cited by 21 · see citing works
Ueno, T., Ichikawa, D., Shimizu, Y., Narisawa, T., Tsuji, K., Ochi, E., Sakurai, N., Iwata, H., & Matsuoka, Y. (2021) · Japanese Journal of Clinical Oncology · doi:10.1093/jjco/hyab169 · Cited by 21 · see citing works
Slováček, L., Slováčková, B., Slánská, I., Petera, J., Priester, P., Filip, S., & Kopecký, J. (2009) · Neoplasma · doi:10.4149/neo_2009_06_467 · Cited by 20 · see citing works
A predictive model for pain response following radiotherapy for treatment of spinal metastases
Wakabayashi, K., Koide, Y., Aoyama, T., Shimizu, H., Miyauchi, R., Tanaka, H., Tachibana, H., Nakamura, K., & Kodaira, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-92363-0 · Cited by 20 · see citing works
El-Aziz, N. A., Khallaf, S., Abozaid, W., ElGohary, G. M., El-Fattah, O. A., Alhawari, M., Khaled, S. A. A., Abdelhaffez, A., Kamel, E., & Mohamed, S. A. A. (2020) · BMC Cancer · doi:10.1186/s12885-020-07451-7 · Cited by 18 · see citing works
Symptom prevalence and severity in palliative cancer medicine
Webber, K., Davies, A. N., Leach, C., & Waghorn, M. (2021) · BMJ Supportive & Palliative Care · doi:10.1136/bmjspcare-2020-002357 · Cited by 17 · see citing works
Prediction of evening fatigue severity in outpatients receiving chemotherapy: less may be more
Kober, K. M., Roy, R., Dhruva, A., Conley, Y. P., Chan, R. J., Cooper, B. A., Olshen, A. B., & Miaskowski, C. A. (2021) · Fatigue Biomedicine Health & Behavior · doi:10.1080/21641846.2021.1885119 · Cited by 16 · see citing works
Haun, M. W., Simon, L., Sklenarova, H., Zimmermann‐Schlegel, V., Friederich, H., & Hartmann, M. (2021) · Cancer Medicine · doi:10.1002/cam4.4048 · Cited by 15 · see citing works
Mosa, A. S. M., Rana, M. K. Z., Islam, H., Hossain, A. K. M. M., & Yoo, I. (2021) · JMIR mhealth and uhealth · doi:10.2196/27024 · Cited by 14 · see citing works
Guo, W., Gao, G., Dai, J., & Sun, Q. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/4312117 · Cited by 14 · see citing works
Li, M.-Y., Zhang, J., Zha, Y., Li, Y., Hu, B., Zheng, S., & Zhou, J. (2022) · BMC Oral Health · doi:10.1186/s12903-022-02269-0 · Cited by 14 · see citing works
Xuyi, W., Seow, H. Y., & Sutradhar, R. (2020) · Cancer Medicine · doi:10.1002/cam4.3685 · Cited by 13 · see citing works
Kurisu, K., Inada, S., Maeda, I., Ogawa, A., Iwase, S., Akechi, T., Morita, T., Oyamada, S., Yamaguchi, T., Imai, K., et al. (2021) · Palliative & Supportive Care · doi:10.1017/s1478951521001565 · Cited by 13 · see citing works
Du, L., Du, J., Yang, M., Xu, Q., Huang, J., Tan, W., Xu, T., Wang, L., Nie, W., & Zhao, L. (2023) · Supportive Care in Cancer · doi:10.1007/s00520-022-07570-w · Cited by 13 · see citing works
Chao, M., Naqa, I. E., Bakst, R. L., Lo, Y., & Peñagarícano, J. (2022) · Acta Oncologica · doi:10.1080/0284186x.2022.2073187 · Cited by 10 · see citing works
Shaikh, N. F., Shen, C., LeMasters, T., Dwibedi, N., Ladani, A. P., & Sambamoorthi, U. (2023) · Cancer Informatics · doi:10.1177/11769351231165161 · Cited by 10 · see citing works
Prediction model of critical weight loss in cancer patients during particle therapy
Zhang, Z., Zhu, Y., Zhang, L., Wang, Z., & Wan, H. (2017) · Japanese Journal of Clinical Oncology · doi:10.1093/jjco/hyx145 · Cited by 7 · see citing works
Fanizzi, A., Scognamillo, G., Nestola, A., Bambace, S., Bove, S., Comes, M. C., Cristofaro, C., Didonna, V., Rito, A. D., Errico, A., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.993395 · Cited by 7 · see citing works
Wang, X., Eichhorn, J. M., Haq, I., & Baghal, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251026 · Cited by 5 · see citing works
Baglione, A. N., Cai, L., Bahrini, A., Posey, I. R., Boukhechba, M., & Chow, P. I. (2022) · JMIR Medical Informatics · doi:10.2196/30712 · Cited by 5 · see citing works
Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more
Kober, K. M., Roy, R., Conley, Y. P., Dhruva, A., Hammer, M. J., Levine, J. D., Olshen, A. B., & Miaskowski, C. A. (2023) · Supportive Care in Cancer · doi:10.1007/s00520-023-07723-5 · Cited by 4 · see citing works
Construction of a predictive model for cognitive impairment risk in patients with advanced cancer
Zhu, X., Zhuang, S., Zhou, X., Wang, L., Guo, Y., Peng, W., Yahong, H., Longting, M., & Jing, W. (2023) · International Journal of Nursing Practice · doi:10.1111/ijn.13140 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Geron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. 15 October 2019: O'Reilly Media, Inc. 859. ISBN: 9781492032649
Cited by
Sorted by citations
Oncu, E., & Çiftçi, F. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110488 · Cited by 33 · see citing works
Bandyopadhyay, A., Albashayreh, A., Zeinali, N., Fan, W., & Gilbertson‐White, S. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae082 · Cited by 14 · see citing works
Self-citationFinkelstein, J., Smiley, A., Echeverria, C., & Mooney, K. H. (2024) · Bioengineering · doi:10.3390/bioengineering11111172 · Cited by 13 · see citing works
Clinical Decision Support Systems for cancer symptom management: A scoping review
Chi, J., Zhong, C., Tang, J., Guo, B., Zhang, Y., Deng, S., Guo, Z., & Wu, Y. (2025) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2025.105317 · Cited by 7 · see citing works
Hou, M., Zhu, Y., Zhou, H., Zhou, S., Zhang, J., Zhang, Y., & Liu, X. (2025) · Clinical and Experimental Medicine · doi:10.1007/s10238-025-01818-5 · Cited by 6 · see citing works
Finkelstein, J., Smiley, A., Echeverria, C., & Mooney, K. H. (2025) · Diagnostics · doi:10.3390/diagnostics15080956 · Cited by 5 · see citing works
Xu, M. Q., Jiang, Z. S., Liao, W. Y., Kang, Y., Feng, X. Y., Jiang, K., Jiang, Q., Cong, Z. Z., Luo, J., Wu, L., et al. (2025) · JMIR Cancer · doi:10.2196/68027 · Cited by 3 · see citing works
González, L. M., Márquez, G., Parra-Soto, S. L., Cardona-Valencia, M., & Taramasco, C. (2025) · Journal of Medical Internet Research · doi:10.2196/79187 · Cited by 3 · see citing works
Bubulac, L., Georgescu, T., Zivari, M., Popescu‐Spineni, D., Albu, C.-C., Bobu, A., Nemeth, S., Bogdan-Andreescu, C. F., Gurghean, A. L., & Alecu, A. A. (2025) · Bioengineering · doi:10.3390/bioengineering12111259 · Cited by 3 · see citing works
Perception of AI Symptom Models in Oncology Nursing: Mixed Methods Evaluation Study
Nicholson, B. L., Sloss, E. A., Smiley, A., Finkelstein, J., & Mooney, K. H. (2026) · JMIR Nursing · doi:10.2196/82283 · Cited by 2 · see citing works
Chae, S., Bae, J., Maitra, P., Lopez, K. D., Sutamtewagul, G., Topaz, M., & Rakel, B. (2026) · Cancer Nursing · doi:10.1097/ncc.0000000000001570 · Cited by 2 · see citing works
Valencia-Muntalà, L., Gómez‐Vaquero, C., Berbel-Arcobé, L., Benavent, D., Vidal-Montal, P., Juanola, X., Narváez, J., & Solé, J. M. N. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1418995 · Cited by 2 · see citing works
Hybrid Deep Learning for Early Detection of Symptom Escalation in Patients with Cancer
Finkelstein, J., Smiley, A., Huo, X., Echeverria, C., & Mooney, K. H. (2025) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice65758.2025.00018 · Cited by 2 · see citing works
Hundal, J., Veettil, A. A. V., Chung, C., & Hosseini, M. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-517280 · Cited by 1 · see citing works
Reunamo, A., Moen, H., Salanterä, S., & Lähteenmäki, P. M. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1585309 · Cited by 1 · see citing works
Cascella, M., Guerra, C., Feo, R. D., Lisio, F. D., Giordano, P., Esposito, W., Cisale, G., Cerrone, V., Esposito, D., Bruno, M. P., et al. (2026) · Journal of Anesthesia Analgesia and Critical Care · doi:10.1186/s44158-026-00389-5 · Cited by 1 · see citing works
Wang, T., Wang, H., Li, M., Bressington, D. T., Terry, D., Wang, G., Kwok, W. H., & Tan, J.-Y. B. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106643 · Cited by 1 · see citing works
Reheman, M., Li, Y., Chen, Y., Yan, Q., & Hu, X. (2026) · Journal of Nursing Scholarship · doi:10.1111/jnu.70123 · Cited by 1 · see citing works
USPSTF Dismisses Predictive Medicine and Data Science
Alemi, F., & Lee, K. H. (2025) · Quality Management in Health Care · doi:10.1097/qmh.0000000000000528
Zeinali, N., Albashayreh, A., Fan, W., & White, S. G. (2025) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001418
Self-citationMasood, F. (2026) · Journal of Psychosocial Oncology · doi:10.1080/07347332.2026.2649035
Zhang, Y., Wu, Y., Wang, Y., Yang, J., Chen, L. X., & Xu, C. (2026) · International Journal of Women s Health · doi:10.2147/ijwh.s613006
Hossain, A., Rashid, M. M., Alam, T., Riipa, M. B., Hassan, M., Imran, M. A. U., Mohammad, N., Ahmed, F., & Parvin, M. R. (2026) · Digital Health · doi:10.1177/20552076261470185
Kroenke, K., Grzegorczyk, V., Lee, M., Herrin, J. S., Mitchell, S. A., Chlan, L. L., Ruddy, K. J., Pachman, D. R., Griffin, J. M., Ridgeway, J. L., et al. (2026) · Supportive Care in Cancer · doi:10.1007/s00520-026-11234-4
Zhang, J., Cai, C., Tan, X., Chen, W., Zhao, X., 杜敢琴, & Jiang, H. (2026) · PLoS ONE · doi:10.1371/journal.pone.0343457
Artificial intelligence for symptom assessment and patient-reported outcomes in neuro-oncology
Yogendran, L. V., Avery, C., Crooms, R. C., Ozair, A., Siegel, C. L., Guterman, I., Rhee, J. Y., Binder, J., Minor, M., Alder, L., et al. (2026) · Journal of Neuro-Oncology · doi:10.1007/s11060-026-05832-9
- Authors
- Zeinali, N., Youn, N., Albashayreh, A., Fan, W., & Gilbertson White, S.
- Affiliations
- University of Iowa (US)
- Venue
- JMIR Cancer
- Publisher
- JMIR Publications Inc.
- Published
- 2024-03-19
- Volume
- 10
- Pages
- e52322
- ISSN
- 2369-1999 (ISSN-L)
- OpenAlex
- W4391039240
- Cited by
- 26
- References
- 71
- Citations by year
- 2026: 12 · 2025: 11 · 2024: 3
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Cancer (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Cancer Institute (P30CA086862)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Oncology › Cancer survivorship and care
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- machine learning, xerostomia, depression, fatigue, nausea and vomiting, cognitive impairment, sleep disorders, head and neck cancer, breast cancer, supervised learning, random forest, artificial neural networks, logistic regression, ensemble classifiers, unsupervised learning, deep learning, symptom burden
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 ›
Are you an author, or do you see a problem with this page? Request a review
