A Semi-Automated Solution Approach Recommender for a Given Use Case: a Case Study for AI/ML in Oncology via Scopus and OpenAI

• URL: http://arxiv.org/abs/2307.04573v2
• Date: Wed, 15 May 2024 07:46:58 GMT
• Title: A Semi-Automated Solution Approach Recommender for a Given Use Case: a Case Study for AI/ML in Oncology via Scopus and OpenAI
• Authors: Deniz Kenan Kılıç, Alex Elkjær Vasegaard, Aurélien Desoeuvres, Peter Nielsen,
• Abstract summary: Our proposed tool, SARBOLD-LLM, allows discovering and choosing among methods related to a given problem. It provides additional information about their uses in the literature to derive decision-making insights. It is a useful tool to select which methods to investigate first and comes as a complement to surveys.
• Score: 0.6749750044497732
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nowadays, literature review is a necessary task when trying to solve a given problem. However, an exhaustive literature review is very time-consuming in today's vast literature landscape. It can take weeks, even if looking only for abstracts or surveys. Moreover, choosing a method among others, and targeting searches within relevant problem and solution domains, are not easy tasks. These are especially true for young researchers or engineers starting to work in their field. Even if surveys that provide methods used to solve a specific problem already exist, an automatic way to do it for any use case is missing, especially for those who don't know the existing literature. Our proposed tool, SARBOLD-LLM, allows discovering and choosing among methods related to a given problem, providing additional information about their uses in the literature to derive decision-making insights, in only a few hours. The SARBOLD-LLM comprises three modules: (1: Scopus search) paper selection using a keyword selection scheme to query Scopus API; (2: Scoring and method extraction) relevancy and popularity scores calculation and solution method extraction in papers utilizing OpenAI API (GPT 3.5); (3: Analyzes) sensitivity analysis and post-analyzes which reveals trends, relevant papers and methods. Comparing the SARBOLD-LLM to manual ground truth using precision, recall, and F1-score metrics, the performance results of AI in the oncology case study are 0.68, 0.9, and 0.77, respectively. SARBOLD-LLM demonstrates successful outcomes across various domains, showcasing its robustness and effectiveness. The SARBOLD-LLM addresses engineers more than researchers, as it proposes methods and trends without adding pros and cons. It is a useful tool to select which methods to investigate first and comes as a complement to surveys. This can limit the global search and accumulation of knowledge for the end user. However...

Related papers

• AutoSurvey: Large Language Models Can Automatically Write Surveys [77.0458309675818]
  This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys. Traditional survey paper creation faces challenges due to the vast volume and complexity of information. Our contributions include a comprehensive solution to the survey problem, a reliable evaluation method, and experimental validation demonstrating AutoSurvey's effectiveness.
  arXiv  Detail & Related papers  (2024-06-10T12:56:06Z)
• A Survey on Deep Active Learning: Recent Advances and New Frontiers [27.07154361976248]
  This work aims to serve as a useful and quick guide for researchers in overcoming difficulties in deep learning-based active learning (DAL) This technique has gained increasing popularity due to its broad applicability, yet its survey papers, especially for deep learning-based active learning (DAL), remain scarce.
  arXiv  Detail & Related papers  (2024-05-01T05:54:33Z)
• Artificial intelligence to automate the systematic review of scientific literature [0.0]
  We present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature. We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies.
  arXiv  Detail & Related papers  (2024-01-13T19:12:49Z)
• A Survey on Query-based API Recommendation [12.999521865816185]
  Application Programming Interfaces (APIs) are designed to help developers build software more effectively. To understand this research domain, we have surveyed to analyze API recommendation studies published in the last 10 years.
  arXiv  Detail & Related papers  (2023-12-17T06:39:10Z)
• CRUISE-Screening: Living Literature Reviews Toolbox [8.292338880619061]
  CRUISE-Screening is a web-based application for conducting living literature reviews. It is connected to several search engines via an API, which allows for updating the search results periodically.
  arXiv  Detail & Related papers  (2023-09-04T15:58:43Z)
• A Gold Standard Dataset for the Reviewer Assignment Problem [117.59690218507565]
  "Similarity score" is a numerical estimate of the expertise of a reviewer in reviewing a paper. Our dataset consists of 477 self-reported expertise scores provided by 58 researchers. For the task of ordering two papers in terms of their relevance for a reviewer, the error rates range from 12%-30% in easy cases to 36%-43% in hard cases.
  arXiv  Detail & Related papers  (2023-03-23T16:15:03Z)
• Learning Open Domain Multi-hop Search Using Reinforcement Learning [20.078330789576256]
  We teach an automated agent to learn how to search for multi-hop paths of relations between entities in an open domain. We implement the method in an actor-critic reinforcement learning algorithm and evaluate it on a dataset of search problems derived from a subset of English Wikipedia.
  arXiv  Detail & Related papers  (2022-05-30T17:44:19Z)
• Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning [113.05118113697111]
  Few-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with only a limited amount of labeled data. Research literature on few-shot learning exhibits great diversity, while different algorithms often excel at different few-shot learning scenarios. We present Meta Navigator, a framework that attempts to solve the limitation in few-shot learning by seeking a higher-level strategy.
  arXiv  Detail & Related papers  (2021-09-13T07:20:01Z)
• Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning [56.17603785248675]
  Model-agnostic meta-learning (MAML) has become a popular research area. Existing MAML algorithms rely on the episode' idea by sampling a few tasks and data points to update the meta-model at each iteration. This paper proposes memory-based algorithms for MAML that converge with vanishing error.
  arXiv  Detail & Related papers  (2021-06-09T08:47:58Z)
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges [76.20963684020145]
  Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. Bizarre approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning.
  arXiv  Detail & Related papers  (2020-11-12T06:41:05Z)
• Active Learning from Crowd in Document Screening [76.9545252341746]
  We focus on building a set of machine learning classifiers that evaluate documents, and then screen them efficiently. We propose a multi-label active learning screening specific sampling technique -- objective-aware sampling. We demonstrate that objective-aware sampling significantly outperforms the state of the art active learning sampling strategies.
  arXiv  Detail & Related papers  (2020-11-11T16:17:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.