1
|
Luo J, Qiao Z, Glass L, Xiao C, Ma F. ClinicalRisk: A New Therapy-related Clinical Trial Dataset for Predicting Trial Status and Failure Reasons. PROCEEDINGS OF THE ... ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT. ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT 2023; 2023:5356-5360. [PMID: 38601744 PMCID: PMC11005852 DOI: 10.1145/3583780.3615113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Clinical trials aim to study new tests and evaluate their effects on human health outcomes, which has a huge market size. However, carrying out clinical trials is expensive and time-consuming and often ends in no results. It will revolutionize clinical practice if we can develop an effective model to automatically estimate the status of a clinical trial and find out possible failure reasons. However, it is challenging to develop such a model because of the lack of a benchmark dataset. To address these challenges, in this paper, we first build a new dataset by extracting the publicly available clinical trial reports from ClinicalTrials.gov. The associated status of each report is treated as the status label. To analyze the failure reasons, domain experts help us manually annotate each failed report based on the description associated with it. More importantly, we examine several state-of-the-art text classification baselines on this task and find out that the unique format of the clinical trial protocols plays an essential role in affecting prediction accuracy, demonstrating the need for specially designed clinical trial classification models.
Collapse
Affiliation(s)
- Junyu Luo
- The Pennsylvania State University, University Park, USA
| | - Zhi Qiao
- United Imaging Healthcare, Beijing, China
| | | | | | - Fenglong Ma
- The Pennsylvania State University, University Park, USA
| |
Collapse
|
2
|
Fine-Grained Algorithm for Improving KNN Computational Performance on Clinical Trials Text Classification. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc5040060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Text classification is an important component in many applications. Text classification has attracted the attention of researchers to continue to develop innovations and build new classification models that are sourced from clinical trial texts. In building classification models, many methods are used, including supervised learning. The purpose of this study is to improve the computational performance of one of the supervised learning methods, namely KNN, in building a clinical trial document text classification model by combining KNN and the fine-grained algorithm. This research contributed to increasing the computational performance of KNN from 388,274 s to 260,641 s in clinical trial texts on a clinical trial text dataset with a total of 1,000,000 data.
Collapse
|
3
|
Huang K, Xiao C, Glass LM, Critchlow CW, Gibson G, Sun J. Machine learning applications for therapeutic tasks with genomics data. PATTERNS (NEW YORK, N.Y.) 2021; 2:100328. [PMID: 34693370 PMCID: PMC8515011 DOI: 10.1016/j.patter.2021.100328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.
Collapse
Affiliation(s)
- Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Cao Xiao
- Amplitude, San Francisco, CA 94105, USA
| | - Lucas M. Glass
- Analytics Center of Excellence, IQVIA, Cambridge, MA 02139, USA
| | | | - Greg Gibson
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Jimeng Sun
- Computer Science Department and Carle's Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61820, USA
| |
Collapse
|
4
|
Alkaitis MS, Agrawal MN, Riely GJ, Razavi P, Sontag D. Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer. JCO Clin Cancer Inform 2021; 5:550-560. [PMID: 33989016 DOI: 10.1200/cci.20.00139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Key oncology end points are not routinely encoded into electronic medical records (EMRs). We assessed whether natural language processing (NLP) can abstract treatment discontinuation rationale from unstructured EMR notes to estimate toxicity incidence and progression-free survival (PFS). METHODS We constructed a retrospective cohort of 6,115 patients with early-stage and 701 patients with metastatic breast cancer initiating care at Memorial Sloan Kettering Cancer Center from 2008 to 2019. Each cohort was divided into training (70%), validation (15%), and test (15%) subsets. Human abstractors identified the clinical rationale associated with treatment discontinuation events. Concatenated EMR notes were used to train high-dimensional logistic regression and convolutional neural network models. Kaplan-Meier analyses were used to compare toxicity incidence and PFS estimated by our NLP models to estimates generated by manual labeling and time-to-treatment discontinuation (TTD). RESULTS Our best high-dimensional logistic regression models identified toxicity events in early-stage patients with an area under the curve of the receiver-operator characteristic of 0.857 ± 0.014 (standard deviation) and progression events in metastatic patients with an area under the curve of 0.752 ± 0.027 (standard deviation). NLP-extracted toxicity incidence and PFS curves were not significantly different from manually extracted curves (P = .95 and P = .67, respectively). By contrast, TTD overestimated toxicity in early-stage patients (P < .001) and underestimated PFS in metastatic patients (P < .001). Additionally, we tested an extrapolation approach in which 20% of the metastatic cohort were labeled manually, and NLP algorithms were used to abstract the remaining 80%. This extrapolated outcomes approach resolved PFS differences between receptor subtypes (P < .001 for hormone receptor+/human epidermal growth factor receptor 2- v human epidermal growth factor receptor 2+ v triple-negative) that could not be resolved with TTD. CONCLUSION NLP models are capable of abstracting treatment discontinuation rationale with minimal manual labeling.
Collapse
Affiliation(s)
- Matthew S Alkaitis
- CSAIL & IMES, Massachusetts Institute of Technology, Cambridge, MA.,Harvard Medical School, Boston, MA
| | - Monica N Agrawal
- CSAIL & IMES, Massachusetts Institute of Technology, Cambridge, MA
| | - Gregory J Riely
- Memorial Sloan Kettering Cancer Center, New York, NY.,Weill-Cornell Medical College, New York, NY
| | - Pedram Razavi
- Memorial Sloan Kettering Cancer Center, New York, NY.,Weill-Cornell Medical College, New York, NY
| | - David Sontag
- CSAIL & IMES, Massachusetts Institute of Technology, Cambridge, MA
| |
Collapse
|
5
|
Bustos A, Pertusa A, Salinas JM, de la Iglesia-Vayá M. PadChest: A large chest x-ray image dataset with multi-label annotated reports. Med Image Anal 2020; 66:101797. [PMID: 32877839 DOI: 10.1016/j.media.2020.101797] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 07/24/2020] [Accepted: 07/24/2020] [Indexed: 10/23/2022]
Abstract
We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray databases suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/.
Collapse
Affiliation(s)
- Aurelia Bustos
- Department of Software and Computing Systems, University Institute for Computing Research, University of Alicante, Spain.
| | - Antonio Pertusa
- Department of Software and Computing Systems, University Institute for Computing Research, University of Alicante, Spain.
| | - Jose-Maria Salinas
- Department of Health Informatics, Hospital Universitario San Juan de Alicante, Spain.
| | - Maria de la Iglesia-Vayá
- Foundation for the Promotion of the Research in Healthcare and Biomedicine (FISABIO), Valencia, Spain.
| |
Collapse
|
6
|
Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10062157] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Natural language processing (NLP) is an effective tool for generating structured information from unstructured data, the one that is commonly found in clinical trial texts. Such interdisciplinary research has gradually grown into a flourishing research field with accumulated scientific outputs available. In this study, bibliographical data collected from Web of Science, PubMed, and Scopus databases from 2001 to 2018 had been investigated with the use of three prominent methods, including performance analysis, science mapping, and, particularly, an automatic text analysis approach named structural topic modeling. Topical trend visualization and test analysis were further employed to quantify the effects of the year of publication on topic proportions. Topical diverse distributions across prolific countries/regions and institutions were also visualized and compared. In addition, scientific collaborations between countries/regions, institutions, and authors were also explored using social network analysis. The findings obtained were essential for facilitating the development of the NLP-enhanced clinical trial texts processing, boosting scientific and technological NLP-enhanced clinical trial research, and facilitating inter-country/region and inter-institution collaborations.
Collapse
|
7
|
Savova GK, Danciu I, Alamudun F, Miller T, Lin C, Bitterman DS, Tourassi G, Warner JL. Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records. Cancer Res 2019; 79:5463-5470. [PMID: 31395609 PMCID: PMC7227798 DOI: 10.1158/0008-5472.can-19-0579] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 06/17/2019] [Accepted: 07/29/2019] [Indexed: 12/12/2022]
Abstract
Current models for correlating electronic medical records with -omics data largely ignore clinical text, which is an important source of phenotype information for patients with cancer. This data convergence has the potential to reveal new insights about cancer initiation, progression, metastasis, and response to treatment. Insights from this real-world data will catalyze clinical care, research, and regulatory activities. Natural language processing (NLP) methods are needed to extract these rich cancer phenotypes from clinical text. Here, we review the advances of NLP and information extraction methods relevant to oncology based on publications from PubMed as well as NLP and machine learning conference proceedings in the last 3 years. Given the interdisciplinary nature of the fields of oncology and information extraction, this analysis serves as a critical trail marker on the path to higher fidelity oncology phenotypes from real-world data.
Collapse
Affiliation(s)
- Guergana K Savova
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts.
- Harvard Medical School, Boston, Massachusetts
| | | | | | - Timothy Miller
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Chen Lin
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
| | - Danielle S Bitterman
- Harvard Medical School, Boston, Massachusetts
- Dana Farber Cancer Institute, Boston, Massachusetts
| | | | | |
Collapse
|