Leveraging machine learning technology to efficiently identify and match patients for precision oncology clinical trials.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13588-e13588
Author(s):  
Laura Sachse ◽  
Smriti Dasari ◽  
Marc Ackermann ◽  
Emily Patnaude ◽  
Stephanie OLeary ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Language Processing ◽  
Time Trial ◽  
High Fidelity ◽  
Learning Technology ◽  
Precision Oncology ◽  
Sequencing Data ◽  
Clinical Criteria ◽  
Patient Table

e13588 Background: Pre-screening for clinical trials is becoming more challenging as inclusion/exclusion criteria becomes increasingly complex. Oncology precision medicine provides an exciting opportunity to simplify this process and quickly match patients with trials by leveraging machine learning technology. The Tempus TIME Trial site network matches patients to relevant, open, and recruiting clinical trials, personalized to each patient’s clinical and molecular biology. Methods: Tempus screens patients at sites within the TIME Trial Network to find high-fidelity matches to clinical trials. The patient records include documentation submitted alongside NGS orders as well as electronic medical records (EMR) ingested through EMR Integrations. While Tempus-sequenced patients were automatically matched to trials using a Tempus-built matching application, EMR records were run through a natural language processing (NLP) data abstraction model to identify patients with an actionable gene of interest. Structured data were analyzed to filter to patients that lack a deceased date and have an encounter date within a predefined time period. Tempus abstractors manually validated the resulting unstructured records to ensure each patient was matched to a TIME Trial at a site capable of running the trial. For all high-level patient matches, a Tempus Clinical Navigator manually evaluated other clinical criteria to confirm trial matches and communicated with the site about trial options. Results: Patient matching was accelerated by implementing NLP gene and report detection (which isolated 17% of records) and manual screening. As a result, Tempus facilitated screening of over 190,000 patients efficiently using proprietary NLP technology to match 332 patients to 21 unique interventional clinical trials since program launch. Tempus continues to optimize its NLP models to increase high-fidelity trial matching at scale. Conclusions: The TIME Trial Network is an evolving, dynamic program that efficiently matches patients with clinical trial sites using both EMR and Tempus sequencing data. Here, we show how machine learning technology can be utilized to efficiently identify and recruit patients to clinical trials, thereby personalizing trial enrollment for each patient.[Table: see text]

scholarly journals Ai and Ml Based Google Assistant for an Organization using Google Cloud Platform and Dialogflow

2020 ◽  
Vol 8 (5) ◽  
pp. 2722-2727
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language ◽  
Language Processing ◽  
User Interaction ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Cloud Platform ◽  
Organization Process ◽  
Academic Information

Many people adopting Smart Assistant Devices such as Google Home. Now a days of solely engaging with a service through a keyboard are over. The new modes of user interaction are aided in part by this research will investigate how advancements in Artificial Intelligence and Machine Learning technology are being used to improve many services. In particular, it will look at the development of google assistants as a channel for information distribution. This project is aimed to implement an android-based chatbot to assist with Organization basic processes, using google tools such as Dialogflow that uses Natural language processing NLP, Actions on Google and Google Cloud Platform that expose artificial intelligence and Machine Learning methods such as natural language understanding. Allowing users to interact with the google assistant using natural language as input and to train the chatbot i.e. google assistant using Dialogflow Machine learning tool and some appropriate methods so it will be able to generate a dynamic response. The chatbot will allow users to view all their personal academic information, schedule meetings with higher officials, automating the organization process and organization resources information all from within the chatbot i.e. Google Assistant. This project uses the OAuth authentication for security purpose. The Dialogflow helps to understand the users query by using machine learning algorithms. By using this google assistant we are going to use the Cloud Vision API for advancement. We will use Dialogflow as key part to develop Google assistant.

scholarly journals Natural language processing for patient selection in phase I/II oncology clinical trials

2021 ◽  
Author(s):  
Julie Delorme ◽  
Valentin Charvet ◽  
Muriel Wartelle ◽  
François Lion ◽  
Bruno Thuillier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Phase I ◽  
Language Processing ◽  
Early Phase ◽  
Free Text ◽  
Tumor Type ◽  
Early Discontinuation ◽  
Promising Tool ◽  
Oncology Clinical Trials

AbstractPurposeEarly discontinuation affects over one-third of patients enrolled in early-phase oncology clinical trials. Early discontinuation is deleterious both for the patient and for the study, by inflating its duration and associated costs. We aimed at predicting the successful screening and dose-limiting toxicity period completion (SSD) from automatic analysis of consultation reports.Materials and methodsWe retrieved the consultation reports of patients included in phase I and/or phase II oncology trials for any tumor type at Gustave Roussy, France. We designed a pre-processing pipeline that transformed free-text into numerical vectors and gathered them into semantic clusters. These document-based semantic vectors were then fed into a machine learning model that we trained to output a binary prediction of SSD status.ResultsBetween September, 2012 and July, 2020, 56,924 consultation reports were used to build the dictionary, and 1,858 phase I/II inclusion reports were used to train (75%), validate (15%) and test (15%) a Random Forest model. Pre-processing could efficiently cluster words with semantic proximity. On the unseen test cohort of 264 consultation reports, the performances of the model reached: F1 score 0.80, recall 0.81 and AUC 0.88. Using this model, we could have reduced the screen fail rate (including DLT period) from 39.8% to 12.8% (RR=0.322, 95%CI[0.209-0.498], p<0.0001) within the test cohort. Most important semantic clusters for predictions comprised words related to hematological malignancies, anatomo-pathological features and laboratory and imaging interpretation.ConclusionMachine learning with semantic conservation is a promising tool to assist physicians in selecting patients prone to achieve SSD in early-phase oncology clinical trials.

scholarly journals Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

2021 ◽  
Vol 11 ◽  
Author(s):  
Ken Asada ◽  
Syuzo Kaneko ◽  
Ken Takasawa ◽  
Hidenori Machino ◽  
Satoshi Takahashi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Genome Project ◽  
Integrated Analysis ◽  
Learning Technology ◽  
Precision Oncology ◽  
Whole Genome ◽  
Genomic Information ◽  
Omics Analysis ◽  
Number Of Patients

With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, “precision medicine,” which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.

scholarly journals Machine learning and big scientific data

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190054 ◽  
Author(s):  
Tony Hey ◽  
Keith Butler ◽  
Sam Jackson ◽  
Jeyarajan Thiyagalingam
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Language Processing ◽  
High Performance ◽  
Large Scale ◽  
Materials Science ◽  
Numerical Algorithms ◽  
Scientific Data ◽  
Learning Technology ◽  
Challenges And Opportunities

This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory (RAL) site at Harwell near Oxford. Such ‘Big Scientific Data’ comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility and the UK's Central Laser Facility. Increasingly, scientists are now required to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now used the deep learning technology to develop their AlphaFold tool to make predictions for protein folding. Remarkably, it has been able to achieve some spectacular results for this specific scientific problem. Can deep learning be similarly transformative for other scientific problems? After a brief review of some initial applications of machine learning at the RAL, we focus on challenges and opportunities for AI in advancing materials science. Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from several different scientific domains. We conclude with some initial examples of our ‘scientific machine learning’ benchmark suite and of the research challenges these benchmarks will enable. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

scholarly journals Natural Language Processing for Patient Selection in Phase I or II Oncology Clinical Trials

2021 ◽  
pp. 709-718
Author(s):  
Julie Delorme ◽  
Valentin Charvet ◽  
Muriel Wartelle ◽  
François Lion ◽  
Bruno Thuillier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Phase I ◽  
Language Processing ◽  
Early Phase ◽  
Free Text ◽  
Tumor Type ◽  
Early Discontinuation ◽  
Oncology Clinical Trials ◽  
Dose Limiting Toxicity

PURPOSE Early discontinuation affects more than one third of patients enrolled in early-phase oncology clinical trials. Early discontinuation is deleterious both for the patient and for the study, by inflating its duration and associated costs. We aimed at predicting the successful screening and dose-limiting toxicity period completion (SSD) from automatic analysis of consultation reports. MATERIALS AND METHODS We retrieved the consultation reports of patients included in phase I and/or phase II oncology trials for any tumor type at Gustave Roussy, France. We designed a preprocessing pipeline that transformed free text into numerical vectors and gathered them into semantic clusters. These document-based semantic vectors were then fed into a machine learning model that we trained to output a binary prediction of SSD status. RESULTS Between September 2012 and July 2020, 56,924 consultation reports were used to build the dictionary and 1,858 phase I or II inclusion reports were used to train (72%), validate (14%), and test (14%) a random forest model. Preprocessing could efficiently cluster words with semantic proximity. On the unseen test cohort of 264 consultation reports, the performances of the model reached: F1 score 0.80, recall 0.81, and area under the curve 0.88. Using this model, we could have reduced the screen fail rate (including dose-limiting toxicity period) from 39.8% to 12.8% (relative risk, 0.322; 95% CI, 0.209 to 0.498; P < .0001) within the test cohort. Most important semantic clusters for predictions comprised words related to hematologic malignancies, anatomopathologic features, and laboratory and imaging interpretation. CONCLUSION Machine learning with semantic conservation is a promising tool to assist physicians in selecting patients prone to achieve SSD in early-phase oncology clinical trials.

scholarly journals A Text Mining using Web Scraping for Meaningful Insights

2021 ◽  
Vol 2089 (1) ◽  
pp. 012048
Author(s):  
Kishor Kumar Reddy C ◽  
P R Anisha ◽  
Nhu Gia Nguyen ◽  
G Sreelatha
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Text Summarization ◽  
Learning Technology ◽  
Web Scraping ◽  
Textual Data ◽  
The Web

Abstract This research involves the usage of Machine Learning technology and Natural Language Processing (NLP) along with the Natural Language Tool-Kit (NLTK). This helps develop a logical Text Summarization tool, which uses the Extractive approach to generate an accurate and a fluent summary. The aim of this tool is to efficiently extract a concise and a coherent version, having only the main needed outline points from the long text or the input document avoiding any type of repetitions of the same text or information that has already been mentioned earlier in the text. The text to be summarized can be inherited from the web using the process of web scraping or entering the textual data manually on the platform i.e., the tool. The summarization process can be quite beneficial for the users as these long texts, needs to be shortened to help them to refer to the input quickly and understand points that might be out of their scope to understand.

scholarly journals An End-to-End Rumor Detection Model Based on Feature Aggregation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Aoshuang Ye ◽  
Lina Wang ◽  
Run Wang ◽  
Wenqi Wang ◽  
Jianpeng Ke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Language Processing ◽  
Domain Knowledge ◽  
Learning Ability ◽  
Feature Engineering ◽  
Learning Technology ◽  
Detection Model ◽  
Feature Aggregation ◽  
Rumor Detection

The social network has become the primary medium of rumor propagation. Moreover, manual identification of rumors is extremely time-consuming and laborious. It is crucial to identify rumors automatically. Machine learning technology is widely implemented in the identification and detection of misinformation on social networks. However, the traditional machine learning methods profoundly rely on feature engineering and domain knowledge, and the learning ability of temporal features is insufficient. Furthermore, the features used by the deep learning method based on natural language processing are heavily limited. Therefore, it is of great significance and practical value to study the rumor detection method independent of feature engineering and effectively aggregate heterogeneous features to adapt to the complex and variable social network. In this paper, a deep neural network- (DNN-) based feature aggregation modeling method is proposed, which makes full use of the knowledge of propagation pattern feature and text content feature of social network event without feature engineering and domain knowledge. The experimental results show that the feature aggregation model has achieved 94.4% of accuracy as the best performance in recent works.

Corona virus-related Disease Pandemic: A Review on Machine Learning Approaches and Treatment Trials on Diagnosed Population for Future Clinical Decision Support

2021 ◽  
Vol 17 ◽  
Author(s):  
Reyana A ◽  
Sandeep Kautish
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Clinical Trials ◽  
Clinical Decision ◽  
Learning Technology ◽  
Learning Approaches ◽  
Related Disease ◽  
Treatment Trials ◽  
Corona Virus ◽  
The Government

Objective: Corona virus-related disease, a deadly illness, has raised public health issues worldwide. The majority of individuals infected are multiplying. The government takes aggressive steps to quarantine people, people exposed to infection, and clinical trials for treatment. Subsequently recommends critical care for the aged, children, and health-care personnel. While machine learning methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Methods: This paper reviews the recent study that applies machine-learning technology addressing Corona virus-related disease issues' challenges in different perspectives. The report also discusses various treatment trials and procedures on Corona virus-related disease infected patients providing insights to physicians and the public on the current treatment challenges. Results: The paper provides the individual with insights into certain precautions to prevent and control the spread of this deadly disease. Conclusion: This review highlights the utility of evidence-based machine learning prediction tools in several clinical settings, and how similar models can be deployed during the Corona virus-related disease pandemic to guide hospital frontlines and health-care administrators to make informed decisions about patient care and managing hospital volume. Further, the clinical trials conducted so far for infected patients with Corona virus-related disease addresses their results to improve community alertness from the viewpoint of a well-known saying, “prevention is always better."

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

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