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Dai HJ, Chen CC, Mir TH, Wang TY, Wang CK, Chang YC, Yu SJ, Shen YW, Huang CJ, Tsai CH, Wang CY, Chen HJ, Weng PS, Lin YX, Chen SW, Tsai MJ, Juang SF, Wu SY, Tsai WT, Huang MY, Huang CJ, Yang CJ, Liu PZ, Huang CW, Huang CY, Wang WYC, Chong IW, Yang YH. Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data. Comput Struct Biotechnol J 2024; 24:322-333. [PMID: 38690549 PMCID: PMC11059324 DOI: 10.1016/j.csbj.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 05/02/2024] Open
Abstract
Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system's reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient's reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.
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Affiliation(s)
- Hong-Jie Dai
- Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
- National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Center for Big Data Research, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Chien-Chang Chen
- Electromagnetic Sensing Control and AI Computing System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
| | - Tatheer Hussain Mir
- Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
- National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan
| | - Ting-Yu Wang
- Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
- National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan
| | - Chen-Kai Wang
- Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC
- Advanced Technology Laboratory, Chunghwa Telecom Laboratories, Taoyuan, Taiwan, ROC
| | - Ya-Chen Chang
- National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan
| | - Shu-Jung Yu
- Center for Big Data Research, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Yi-Wen Shen
- Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
| | - Cheng-Jiun Huang
- Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
| | - Chia-Hsuan Tsai
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Ching-Yun Wang
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Hsiao-Jou Chen
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Pei-Shan Weng
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - You-Xiang Lin
- Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
| | - Sheng-Wei Chen
- Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
| | - Ming-Ju Tsai
- Division of Pulmonary and Critical Care Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Shian-Fei Juang
- Department of Medical Information, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
| | - Su-Ying Wu
- Department of Medical Information, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
| | - Wen-Tsung Tsai
- Department of Medical Information, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
| | - Ming-Yii Huang
- Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Department of Radiation Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Chih-Jen Huang
- Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
| | - Chih-Jen Yang
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Division of Pulmonary and Critical Care Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Ping-Zun Liu
- Health Promotion Administration, Ministry of Health and Welfare, Taipei 10341, Taiwan
| | - Chiao-Wen Huang
- Health Promotion Administration, Ministry of Health and Welfare, Taipei 10341, Taiwan
| | - Chi-Yen Huang
- Health Promotion Administration, Ministry of Health and Welfare, Taipei 10341, Taiwan
| | | | - Inn-Wen Chong
- Division of Chest Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
| | - Yi-Hsin Yang
- National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan
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Hsu E, Hanson H, Coyle L, Stevens J, Tourassi G, Penberthy L. Machine learning and deep learning tools for the automated capture of cancer surveillance data. J Natl Cancer Inst Monogr 2024; 2024:145-151. [PMID: 39102883 DOI: 10.1093/jncimonographs/lgae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/05/2024] [Accepted: 03/25/2024] [Indexed: 08/07/2024] Open
Abstract
The National Cancer Institute and the Department of Energy strategic partnership applies advanced computing and predictive machine learning and deep learning models to automate the capture of information from unstructured clinical text for inclusion in cancer registries. Applications include extraction of key data elements from pathology reports, determination of whether a pathology or radiology report is related to cancer, extraction of relevant biomarker information, and identification of recurrence. With the growing complexity of cancer diagnosis and treatment, capturing essential information with purely manual methods is increasingly difficult. These new methods for applying advanced computational capabilities to automate data extraction represent an opportunity to close critical information gaps and create a nimble, flexible platform on which new information sources, such as genomics, can be added. This will ultimately provide a deeper understanding of the drivers of cancer and outcomes in the population and increase the timeliness of reporting. These advances will enable better understanding of how real-world patients are treated and the outcomes associated with those treatments in the context of our complex medical and social environment.
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Affiliation(s)
- Elizabeth Hsu
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA
| | - Heidi Hanson
- Advanced Computing for Health Sciences, Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Linda Coyle
- Information Management Services Inc, Calverton, MD, USA
| | | | - Georgia Tourassi
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Lynne Penberthy
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA
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Sushil M, Zack T, Mandair D, Zheng Z, Wali A, Yu YN, Quan Y, Lituiev D, Butte AJ. A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports. J Am Med Inform Assoc 2024:ocae146. [PMID: 38900207 DOI: 10.1093/jamia/ocae146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/27/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024] Open
Abstract
OBJECTIVE Although supervised machine learning is popular for information extraction from clinical notes, creating large annotated datasets requires extensive domain expertise and is time-consuming. Meanwhile, large language models (LLMs) have demonstrated promising transfer learning capability. In this study, we explored whether recent LLMs could reduce the need for large-scale data annotations. MATERIALS AND METHODS We curated a dataset of 769 breast cancer pathology reports, manually labeled with 12 categories, to compare zero-shot classification capability of the following LLMs: GPT-4, GPT-3.5, Starling, and ClinicalCamel, with task-specific supervised classification performance of 3 models: random forests, long short-term memory networks with attention (LSTM-Att), and the UCSF-BERT model. RESULTS Across all 12 tasks, the GPT-4 model performed either significantly better than or as well as the best supervised model, LSTM-Att (average macro F1-score of 0.86 vs 0.75), with advantage on tasks with high label imbalance. Other LLMs demonstrated poor performance. Frequent GPT-4 error categories included incorrect inferences from multiple samples and from history, and complex task design, and several LSTM-Att errors were related to poor generalization to the test set. DISCUSSION On tasks where large annotated datasets cannot be easily collected, LLMs can reduce the burden of data labeling. However, if the use of LLMs is prohibitive, the use of simpler models with large annotated datasets can provide comparable results. CONCLUSIONS GPT-4 demonstrated the potential to speed up the execution of clinical NLP studies by reducing the need for large annotated datasets. This may increase the utilization of NLP-based variables and outcomes in clinical studies.
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Affiliation(s)
- Madhumita Sushil
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, United States
| | - Travis Zack
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, United States
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, United States
| | - Divneet Mandair
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, United States
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, United States
| | - Zhiwei Zheng
- University of California, Berkeley, Berkeley, CA 94720, United States
| | - Ahmed Wali
- University of California, Berkeley, Berkeley, CA 94720, United States
| | - Yan-Ning Yu
- University of California, Berkeley, Berkeley, CA 94720, United States
| | - Yuwei Quan
- University of California, Berkeley, Berkeley, CA 94720, United States
| | - Dmytro Lituiev
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, United States
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, United States
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, United States
- Center for Data-driven Insights and Innovation, University of California, Office of the President, Oakland, CA 94607, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94158, United States
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Sim JA, Huang X, Horan MR, Baker JN, Huang IC. Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:467-475. [PMID: 38383308 PMCID: PMC11001514 DOI: 10.1080/14737167.2024.2322664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 02/20/2024] [Indexed: 02/23/2024]
Abstract
INTRODUCTION Patient-reported outcomes (PROs; symptoms, functional status, quality-of-life) expressed in the 'free-text' or 'unstructured' format within clinical notes from electronic health records (EHRs) offer valuable insights beyond biological and clinical data for medical decision-making. However, a comprehensive assessment of utilizing natural language processing (NLP) coupled with machine learning (ML) methods to analyze unstructured PROs and their clinical implementation for individuals affected by cancer remains lacking. AREAS COVERED This study aimed to systematically review published studies that used NLP techniques to extract and analyze PROs in clinical narratives from EHRs for cancer populations. We examined the types of NLP (with and without ML) techniques and platforms for data processing, analysis, and clinical applications. EXPERT OPINION Utilizing NLP methods offers a valuable approach for processing and analyzing unstructured PROs among cancer patients and survivors. These techniques encompass a broad range of applications, such as extracting or recognizing PROs, categorizing, characterizing, or grouping PROs, predicting or stratifying risk for unfavorable clinical results, and evaluating connections between PROs and adverse clinical outcomes. The employment of NLP techniques is advantageous in converting substantial volumes of unstructured PRO data within EHRs into practical clinical utilities for individuals with cancer.
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Affiliation(s)
- Jin-ah Sim
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, USA
- Department of AI Convergence, Hallym University, Chuncheon, Republic of Korea
| | - Xiaolei Huang
- Department of Computer Science, University of Memphis, Memphis, Tennessee, United States
| | - Madeline R. Horan
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Justin N. Baker
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - I-Chan Huang
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, USA
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5
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Desale RP, Patil PS. An efficient multi-class classification of skin cancer using optimized vision transformer. Med Biol Eng Comput 2024; 62:773-789. [PMID: 37996627 DOI: 10.1007/s11517-023-02969-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 11/07/2023] [Indexed: 11/25/2023]
Abstract
Skin cancer is a pervasive and deadly disease, prompting a surge in research efforts towards utilizing computer-based techniques to analyze skin lesion images to identify malignancies. This paper introduces an optimized vision transformer approach for effectively classifying skin tumors. The methodology begins with a pre-processing step aimed at preserving color constancy, eliminating hair artifacts, and reducing image noise. Here, a combination of techniques such as piecewise linear bottom hat filtering, adaptive median filtering, Gaussian filtering, and an enhanced gradient intensity method is used for pre-processing. Afterwards, the segmentation phase is initiated using the self-sparse watershed algorithm on the pre-processed image. Subsequently, the segmented image is passed through a feature extraction stage where the hybrid Walsh-Hadamard Karhunen-Loeve expansion technique is employed. The final step involves the application of an improved vision transformer for skin cancer classification. The entire methodology is implemented using the Python programming language, and the International Skin Imaging Collaboration (ISIC) 2019 database is utilized for experimentation. The experimental results demonstrate remarkable performance with the different performance metrics is accuracy 99.81%, precision 96.65%, sensitivity 98.21%, F-measure 97.42%, specificity 99.88%, recall 98.21%, Jaccard coefficient 98.54%, and Mathew's correlation coefficient (MCC) 98.89%. The proposed methodology outperforms the existing methodology.
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Affiliation(s)
- R P Desale
- E&TC Engineering Department, SSVPS's Bapusaheb Shivajirao Deore College of Engineering, Dhule, Maharashtra, 424005, India.
| | - P S Patil
- E&TC Engineering Department, SSVPS's Bapusaheb Shivajirao Deore College of Engineering, Dhule, Maharashtra, 424005, India
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6
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Sushil M, Zack T, Mandair D, Zheng Z, Wali A, Yu YN, Quan Y, Butte AJ. A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification. RESEARCH SQUARE 2024:rs.3.rs-3914899. [PMID: 38405831 PMCID: PMC10889046 DOI: 10.21203/rs.3.rs-3914899/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Although supervised machine learning is popular for information extraction from clinical notes, creating large, annotated datasets requires extensive domain expertise and is time-consuming. Meanwhile, large language models (LLMs) have demonstrated promising transfer learning capability. In this study, we explored whether recent LLMs can reduce the need for large-scale data annotations. We curated a manually labeled dataset of 769 breast cancer pathology reports, labeled with 13 categories, to compare zero-shot classification capability of the GPT-4 model and the GPT-3.5 model with supervised classification performance of three model architectures: random forests classifier, long short-term memory networks with attention (LSTM-Att), and the UCSF-BERT model. Across all 13 tasks, the GPT-4 model performed either significantly better than or as well as the best supervised model, the LSTM-Att model (average macro F1 score of 0.83 vs. 0.75). On tasks with a high imbalance between labels, the differences were more prominent. Frequent sources of GPT-4 errors included inferences from multiple samples and complex task design. On complex tasks where large annotated datasets cannot be easily collected, LLMs can reduce the burden of large-scale data labeling. However, if the use of LLMs is prohibitive, the use of simpler supervised models with large annotated datasets can provide comparable results. LLMs demonstrated the potential to speed up the execution of clinical NLP studies by reducing the need for curating large annotated datasets. This may increase the utilization of NLP-based variables and outcomes in observational clinical studies.
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Affiliation(s)
- Madhumita Sushil
- Bakar Computational Health Sciences Institute, University of California, San Francisco, USA
| | - Travis Zack
- Bakar Computational Health Sciences Institute, University of California, San Francisco, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, USA
| | - Divneet Mandair
- Bakar Computational Health Sciences Institute, University of California, San Francisco, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, USA
| | | | | | | | | | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, USA
- Center for Data-driven Insights and Innovation, University of California, Office of the President, Oakland, CA, USA
- Department of Pediatrics, University of California, San Francisco, CA, USA
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Liu S, Wen A, Wang L, He H, Fu S, Miller R, Williams A, Harris D, Kavuluru R, Liu M, Abu-el-Rub N, Schutte D, Zhang R, Rouhizadeh M, Osborne JD, He Y, Topaloglu U, Hong SS, Saltz JH, Schaffter T, Pfaff E, Chute CG, Duong T, Haendel MA, Fuentes R, Szolovits P, Xu H, Liu H. An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C). J Am Med Inform Assoc 2023; 30:2036-2040. [PMID: 37555837 PMCID: PMC10654844 DOI: 10.1093/jamia/ocad134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 06/28/2023] [Accepted: 08/08/2023] [Indexed: 08/10/2023] Open
Abstract
Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.
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Affiliation(s)
- Sijia Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew Wen
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Liwei Wang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Huan He
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Robert Miller
- Tufts Clinical and Translational Science Institute, Tufts Medical Center, Boston, Massachusetts, USA
| | - Andrew Williams
- Tufts Clinical and Translational Science Institute, Tufts Medical Center, Boston, Massachusetts, USA
| | - Daniel Harris
- Department of Internal Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Ramakanth Kavuluru
- Department of Internal Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Mei Liu
- Department of Internal Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Noor Abu-el-Rub
- Department of Internal Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Dalton Schutte
- Department of Pharmaceutical Care & Health Systems, University of Minnesota at Twin Cities, Minneapolis, Minnesota, USA
| | - Rui Zhang
- Department of Pharmaceutical Care & Health Systems, University of Minnesota at Twin Cities, Minneapolis, Minnesota, USA
| | - Masoud Rouhizadeh
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, Florida, USA
| | - John D Osborne
- Department of Computer Science, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Umit Topaloglu
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Stephanie S Hong
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Joel H Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | | | - Emily Pfaff
- Department of Medicine, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Tim Duong
- Department of Radiology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Denver, Colorado, USA
| | | | - Peter Szolovits
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Hua Xu
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
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Leviton A, Loddenkemper T. Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review. BMC Med Res Methodol 2023; 23:271. [PMID: 37974111 PMCID: PMC10652539 DOI: 10.1186/s12874-023-02102-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 11/08/2023] [Indexed: 11/19/2023] Open
Abstract
Real world evidence is now accepted by authorities charged with assessing the benefits and harms of new therapies. Clinical trials based on real world evidence are much less expensive than randomized clinical trials that do not rely on "real world evidence" such as contained in electronic health records (EHR). Consequently, we can expect an increase in the number of reports of these types of trials, which we identify here as 'EHR-sourced trials.' 'In this selected literature review, we discuss the various designs and the ethical issues they raise. EHR-sourced trials have the potential to improve/increase common data elements and other aspects of the EHR and related systems. Caution is advised, however, in drawing causal inferences about the relationships among EHR variables. Nevertheless, we anticipate that EHR-CTs will play a central role in answering research and regulatory questions.
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Affiliation(s)
- Alan Leviton
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Tobias Loddenkemper
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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9
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Li B, Wang B, Zhuang P, Cao H, Wu S, Tan Z, Gao S, Li P, Jing W, Shao Z, Zheng K, Wu L, Gao B, Wang Y, Jiang H, Guo S, He L, Yang Y, Jin G. A novel staging system derived from natural language processing of pathology reports to predict prognostic outcomes of pancreatic cancer: a retrospective cohort study. Int J Surg 2023; 109:3476-3489. [PMID: 37578452 PMCID: PMC10651292 DOI: 10.1097/js9.0000000000000648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/16/2023] [Indexed: 08/15/2023]
Abstract
OBJECTIVE To construct a novel tumor-node-morphology (TNMor) staging system derived from natural language processing (NLP) of pathology reports to predict outcomes of pancreatic ductal adenocarcinoma. METHOD This retrospective study with 1657 participants was based on a large referral center and The Cancer Genome Atlas Program (TCGA) dataset. In the training cohort, NLP was used to extract and screen prognostic predictors from pathology reports to develop the TNMor system, which was further evaluated with the tumor-node-metastasis (TNM) system in the internal and external validation cohort, respectively. Main outcomes were evaluated by the log-rank test of Kaplan-Meier curves, the concordance index (C-index), and the area under the receiver operating curve (AUC). RESULTS The precision, recall, and F1 scores of the NLP model were 88.83, 89.89, and 89.21%, respectively. In Kaplan-Meier analysis, survival differences between stages in the TNMor system were more significant than that in the TNM system. In addition, our system provided an improved C-index (internal validation, 0.58 vs. 0.54, P <0.001; external validation, 0.64 vs. 0.63, P <0.001), and higher AUCs for 1, 2, and 3-year survival (internal validation: 0.62 vs. 0.54, P <0.001; 0.64 vs. 0.60, P= 0.017; 0.69 vs. 0.62, P= 0.001; external validation: 0.69 vs. 0.65, P= 0.098; 0.68 vs. 0.64, P= 0.154; 0.64 vs. 0.55, P= 0.032, respectively). Finally, our system was particularly beneficial for precise stratification of patients receiving adjuvant therapy, with an improved C-index (0.61 vs. 0.57, P <0.001), and higher AUCs for 1-year, 2-year, and 3-year survival (0.64 vs. 0.57, P <0.001; 0.64 vs. 0.58, P <0.001; 0.67 vs. 0.61, P <0.001; respectively) compared with the TNM system. CONCLUSION These findings suggest that the TNMor system performed better than the TNM system in predicting pancreatic ductal adenocarcinoma prognosis. It is a promising system to screen risk-adjusted strategies for precision medicine.
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Affiliation(s)
- Bo Li
- Department of Hepatobiliary Pancreatic Surgery
- Department of Hepatobiliary Pancreatic Surgery
| | - Beilei Wang
- Department of Hepatobiliary Pancreatic Surgery
- Department of Marine Biomedicine and Polar Medicine, Naval Medical Center, Navy Military Medical University
| | - Pengjie Zhuang
- Department of School of Computer Science and Technology, East China Normal University
| | - Hongwei Cao
- Department of Information, Changhai Hospital
| | | | - Zhendong Tan
- Department of School of Computer Science and Technology, East China Normal University
| | - Suizhi Gao
- Department of Hepatobiliary Pancreatic Surgery
| | - Penghao Li
- Department of Hepatobiliary Pancreatic Surgery
| | - Wei Jing
- Department of Hepatobiliary Pancreatic Surgery
| | - Zhuo Shao
- Department of Hepatobiliary Pancreatic Surgery
| | | | - Lele Wu
- Department of Information, Changhai Hospital
| | - Bai Gao
- Department of Information, Changhai Hospital
| | - Yang Wang
- Department of Pathology, Shanghai Fourth People’s Hospital, Tongji University School of Medicine
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, Naval Military Medical University
| | - Shiwei Guo
- Department of Hepatobiliary Pancreatic Surgery
| | - Liang He
- Department of School of Computer Science and Technology, East China Normal University
- Shanghai Key Laboratory of Multidimensional Information Processing, Shanghai, People’s Republic of China
| | - Yan Yang
- Department of School of Computer Science and Technology, East China Normal University
- Shanghai Key Laboratory of Multidimensional Information Processing, Shanghai, People’s Republic of China
| | - Gang Jin
- Department of Hepatobiliary Pancreatic Surgery
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10
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Hochheiser H, Finan S, Yuan Z, Durbin EB, Jeong JC, Hands I, Rust D, Kavuluru R, Wu XC, Warner JL, Savova G. DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.05.23289524. [PMID: 37205575 PMCID: PMC10187451 DOI: 10.1101/2023.05.05.23289524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Objective The manual extraction of case details from patient records for cancer surveillance efforts is a resource-intensive task. Natural Language Processing (NLP) techniques have been proposed for automating the identification of key details in clinical notes. Our goal was to develop NLP application programming interfaces (APIs) for integration into cancer registry data abstraction tools in a computer-assisted abstraction setting. Methods We used cancer registry manual abstraction processes to guide the design of DeepPhe-CR, a web-based NLP service API. The coding of key variables was done through NLP methods validated using established workflows. A container-based implementation including the NLP wasdeveloped. Existing registry data abstraction software was modified to include results from DeepPhe-CR. An initial usability study with data registrars provided early validation of the feasibility of the DeepPhe-CR tools. Results API calls support submission of single documents and summarization of cases across multiple documents. The container-based implementation uses a REST router to handle requests and support a graph database for storing results. NLP modules extract topography, histology, behavior, laterality, and grade at 0.79-1.00 F1 across common and rare cancer types (breast, prostate, lung, colorectal, ovary and pediatric brain) on data from two cancer registries. Usability study participants were able to use the tool effectively and expressed interest in adopting the tool. Discussion Our DeepPhe-CR system provides a flexible architecture for building cancer-specific NLP tools directly into registrar workflows in a computer-assisted abstraction setting. Improving user interactions in client tools, may be needed to realize the potential of these approaches. DeepPhe-CR: https://deepphe.github.io/.
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Affiliation(s)
- Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sean Finan
- Boston Childrens' Hospital, Boston, MA, USA and Harvard Medical School, Boston, MA, USA
| | - Zhou Yuan
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Eric B Durbin
- Kentucky Cancer Registry, Markey Cancer Center, Lexington, KY, USA
- Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Jong Cheol Jeong
- Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Isaac Hands
- Kentucky Cancer Registry, Markey Cancer Center, Lexington, KY, USA
- Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - David Rust
- Kentucky Cancer Registry, Markey Cancer Center, Lexington, KY, USA
| | - Ramakanth Kavuluru
- Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, KY, USA
| | | | - Jeremy L Warner
- Lifespan Health System, Providence, RI, USA
- Legorreta Cancer Center at Brown University, Providence, RI, USA
| | - Guergana Savova
- Boston Childrens' Hospital, Boston, MA, USA and Harvard Medical School, Boston, MA, USA
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11
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Kuroiwa T, Sarcon A, Ibara T, Yamada E, Yamamoto A, Tsukamoto K, Fujita K. The Potential of ChatGPT as a Self-Diagnostic Tool in Common Orthopedic Diseases: Exploratory Study. J Med Internet Res 2023; 25:e47621. [PMID: 37713254 PMCID: PMC10541638 DOI: 10.2196/47621] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/17/2023] [Accepted: 08/17/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has gained tremendous popularity recently, especially the use of natural language processing (NLP). ChatGPT is a state-of-the-art chatbot capable of creating natural conversations using NLP. The use of AI in medicine can have a tremendous impact on health care delivery. Although some studies have evaluated ChatGPT's accuracy in self-diagnosis, there is no research regarding its precision and the degree to which it recommends medical consultations. OBJECTIVE The aim of this study was to evaluate ChatGPT's ability to accurately and precisely self-diagnose common orthopedic diseases, as well as the degree of recommendation it provides for medical consultations. METHODS Over a 5-day course, each of the study authors submitted the same questions to ChatGPT. The conditions evaluated were carpal tunnel syndrome (CTS), cervical myelopathy (CM), lumbar spinal stenosis (LSS), knee osteoarthritis (KOA), and hip osteoarthritis (HOA). Answers were categorized as either correct, partially correct, incorrect, or a differential diagnosis. The percentage of correct answers and reproducibility were calculated. The reproducibility between days and raters were calculated using the Fleiss κ coefficient. Answers that recommended that the patient seek medical attention were recategorized according to the strength of the recommendation as defined by the study. RESULTS The ratios of correct answers were 25/25, 1/25, 24/25, 16/25, and 17/25 for CTS, CM, LSS, KOA, and HOA, respectively. The ratios of incorrect answers were 23/25 for CM and 0/25 for all other conditions. The reproducibility between days was 1.0, 0.15, 0.7, 0.6, and 0.6 for CTS, CM, LSS, KOA, and HOA, respectively. The reproducibility between raters was 1.0, 0.1, 0.64, -0.12, and 0.04 for CTS, CM, LSS, KOA, and HOA, respectively. Among the answers recommending medical attention, the phrases "essential," "recommended," "best," and "important" were used. Specifically, "essential" occurred in 4 out of 125, "recommended" in 12 out of 125, "best" in 6 out of 125, and "important" in 94 out of 125 answers. Additionally, 7 out of the 125 answers did not include a recommendation to seek medical attention. CONCLUSIONS The accuracy and reproducibility of ChatGPT to self-diagnose five common orthopedic conditions were inconsistent. The accuracy could potentially be improved by adding symptoms that could easily identify a specific location. Only a few answers were accompanied by a strong recommendation to seek medical attention according to our study standards. Although ChatGPT could serve as a potential first step in accessing care, we found variability in accurate self-diagnosis. Given the risk of harm with self-diagnosis without medical follow-up, it would be prudent for an NLP to include clear language alerting patients to seek expert medical opinions. We hope to shed further light on the use of AI in a future clinical study.
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Affiliation(s)
- Tomoyuki Kuroiwa
- Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Division of Orthopedic Surgery Research, Mayo Clinic, Rochester, MN, United States
| | - Aida Sarcon
- Department of Surgery, Mayo Clinic, Rochester, MN, United States
| | - Takuya Ibara
- Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Eriku Yamada
- Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Akiko Yamamoto
- Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kazuya Tsukamoto
- Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Koji Fujita
- Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Division of Medical Design Innovations, Open Innovation Center, Institute of Research Innovation, Tokyo Medical and Dental University, Tokyo, Japan
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12
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Wang L, He H, Wen A, Moon S, Fu S, Peterson KJ, Ai X, Liu S, Kavuluru R, Liu H. Acquisition of a Lexicon for Family History Information: Bidirectional Encoder Representations From Transformers-Assisted Sublanguage Analysis. JMIR Med Inform 2023; 11:e48072. [PMID: 37368483 PMCID: PMC10337517 DOI: 10.2196/48072] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/25/2023] [Accepted: 06/01/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND A patient's family history (FH) information significantly influences downstream clinical care. Despite this importance, there is no standardized method to capture FH information in electronic health records and a substantial portion of FH information is frequently embedded in clinical notes. This renders FH information difficult to use in downstream data analytics or clinical decision support applications. To address this issue, a natural language processing system capable of extracting and normalizing FH information can be used. OBJECTIVE In this study, we aimed to construct an FH lexical resource for information extraction and normalization. METHODS We exploited a transformer-based method to construct an FH lexical resource leveraging a corpus consisting of clinical notes generated as part of primary care. The usability of the lexicon was demonstrated through the development of a rule-based FH system that extracts FH entities and relations as specified in previous FH challenges. We also experimented with a deep learning-based FH system for FH information extraction. Previous FH challenge data sets were used for evaluation. RESULTS The resulting lexicon contains 33,603 lexicon entries normalized to 6408 concept unique identifiers of the Unified Medical Language System and 15,126 codes of the Systematized Nomenclature of Medicine Clinical Terms, with an average number of 5.4 variants per concept. The performance evaluation demonstrated that the rule-based FH system achieved reasonable performance. The combination of the rule-based FH system with a state-of-the-art deep learning-based FH system can improve the recall of FH information evaluated using the BioCreative/N2C2 FH challenge data set, with the F1 score varied but comparable. CONCLUSIONS The resulting lexicon and rule-based FH system are freely available through the Open Health Natural Language Processing GitHub.
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Affiliation(s)
- Liwei Wang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Huan He
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Andrew Wen
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Sungrim Moon
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Kevin J Peterson
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
| | - Xuguang Ai
- Department of Computer Science, University of Kentucky, Lexington, KY, United States
| | - Sijia Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Ramakanth Kavuluru
- Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, United States
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
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13
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Fu S, Wang L, Moon S, Zong N, He H, Pejaver V, Relevo R, Walden A, Haendel M, Chute CG, Liu H. Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review. Clin Transl Sci 2023; 16:398-411. [PMID: 36478394 PMCID: PMC10014687 DOI: 10.1111/cts.13463] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/03/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP-derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP-assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health.
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Affiliation(s)
- Sunyang Fu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Liwei Wang
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Sungrim Moon
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Nansu Zong
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Huan He
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Rose Relevo
- The National Center for Data to Health, Bethesda, Maryland, USA
| | - Anita Walden
- The National Center for Data to Health, Bethesda, Maryland, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | - Hongfang Liu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
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14
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Saha A, Burns L, Kulkarni AM. A scoping review of natural language processing of radiology reports in breast cancer. Front Oncol 2023; 13:1160167. [PMID: 37124523 PMCID: PMC10130381 DOI: 10.3389/fonc.2023.1160167] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 03/28/2023] [Indexed: 05/02/2023] Open
Abstract
Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data registries, and quality improvement in radiology workflows including mammography screening. This scoping review is the first to examine such applications in the specific context of breast cancer. Out of 210 identified articles initially, 44 met our inclusion criteria for this review. Extracted data elements included both clinical and technical details of studies that developed or evaluated NLP algorithms applied to free-text radiology reports of breast cancer. Our review illustrates an emphasis on applications in diagnostic and screening processes over treatment or therapeutic applications and describes growth in deep learning and transfer learning approaches in recent years, although rule-based approaches continue to be useful. Furthermore, we observe increased efforts in code and software sharing but not with data sharing.
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Affiliation(s)
- Ashirbani Saha
- Department of Oncology, McMaster University, Hamilton, ON, Canada
- Hamilton Health Sciences and McMaster University, Escarpment Cancer Research Institute, Hamilton, ON, Canada
- *Correspondence: Ashirbani Saha,
| | - Levi Burns
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
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15
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Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions. LIFE (BASEL, SWITZERLAND) 2022; 12:life12121991. [PMID: 36556356 PMCID: PMC9786074 DOI: 10.3390/life12121991] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 11/30/2022]
Abstract
The World Health Organization (WHO), in their 2022 report, identified cancer as one of the leading causes of death, accounting for about 16% of deaths worldwide. The Cancer-Moonshot community aims to reduce the cancer death rate by half in the next 25 years and wants to improve the lives of cancer-affected people. Cancer mortality can be reduced if detected early and treated appropriately. Cancers like breast cancer and cervical cancer have high cure probabilities when treated early in accordance with best practices. Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies. However, many of these AI tools will remain ineffective in fighting against cancer and saving the lives of millions of patients unless they are accessible and understandable to biologists, oncologists, and other medical cancer researchers. This paper presents the trends, challenges, and future directions of AI in cancer research. We hope that this paper will be of help to both medical experts and technical experts in getting a better understanding of the challenges and research opportunities in cancer diagnosis and treatment.
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