{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T18:01:17Z","timestamp":1785866477593,"version":"3.56.0"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T00:00:00Z","timestamp":1697068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009945","name":"Merck KGaA","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009945","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004755","name":"EMD Serono","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004755","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Patients' increasing digital participation provides an opportunity to pursue patient-centric research and drug development by understanding their needs. Social media has proven to be one of the most useful data sources when it comes to understanding a company's potential audience to drive more targeted impact. Navigating through an ocean of information is a tedious task where techniques such as artificial intelligence and text analytics have proven effective in identifying relevant posts for healthcare business questions. Here, we present an enterprise-ready, scalable solution demonstrating the feasibility and utility of social media-based patient experience data for use in research and development through capturing and assessing patient experiences and expectations on disease, treatment options, and unmet needs while creating a playbook for roll-out to other indications and therapeutic areas.<\/jats:p>","DOI":"10.3389\/frai.2023.1237124","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T10:34:33Z","timestamp":1697106873000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Artificial intelligence-driven approach for patient-focused drug development"],"prefix":"10.3389","volume":"6","author":[{"given":"Prathamesh","family":"Karmalkar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harsha","family":"Gurulingappa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erica","family":"Spies","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer A.","family":"Flynn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2020.findings-emnlp.148","article-title":"\u201cTweetEval: unified benchmark and comparative evaluation for tweet classification,\u201d","volume-title":"Findings of the Association for Computational","author":"Barbieri","year":"2020"},{"key":"B2","first-page":"319","article-title":"Towards identifying drug side effects from social media using active learning and crowd sourcing","volume":"25","author":"Burkhardt","year":"2020","journal-title":"Pac. Symp. Biocomput."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1016\/j.jval.2022.06.004","article-title":"Natural language processing for automated classification of qualitative data from interviews of patients with cancer","volume":"25","author":"Fang","year":"2022","journal-title":"Value. Health."},{"key":"B4","doi-asserted-by":"crossref","DOI":"10.4324\/9781003116202-16","article-title":"\u201cNursing care in psychosocial health,\u201d","volume-title":"Introducing Psychosocial health Nursing","author":"Happell","year":"2020"},{"key":"B5","doi-asserted-by":"publisher","first-page":"618","DOI":"10.3390\/healthcare10040618","article-title":"Predicting adverse drug reactions from social media posts: data balance, feature selection and deep learning","volume":"10","author":"Huang","year":"2022","journal-title":"Healthcare."},{"key":"B6","unstructured":"Detect adverse drug events (biobert)2021"},{"key":"B7","doi-asserted-by":"publisher","first-page":"e100262","DOI":"10.1136\/bmjhci-2020-100262","article-title":"Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review","volume":"28","author":"Khanbhai","year":"2021","journal-title":"BMJ. Health. Care. Inform."},{"key":"B8","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/ICCV.2019.00019","article-title":"\u201cNLNL: negative learning for noisy labels,\u201d","author":"Kim","year":"2019","journal-title":"IEEE\/CVF International Conference on Computer Vision (ICCV)"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00932","article-title":"\u201cJoint negative and positive learning for noisy labels,\u201d","author":"Kim","year":"2021","journal-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"B10","doi-asserted-by":"publisher","first-page":"2871","DOI":"10.1016\/j.drudis.2021.08.012","article-title":"Social media mining in drug development-fundamentals and use cases","volume":"26","author":"Koss","year":"2021","journal-title":"Drug. Discov. Today."},{"key":"B11","doi-asserted-by":"publisher","first-page":"e15708","DOI":"10.2196\/15708","article-title":"Machine learning and natural language processing in psychosocial health: systematic review","volume":"23","author":"Le Glaz","year":"2021","journal-title":"J. Med. Internet. Res."},{"key":"B12","doi-asserted-by":"publisher","first-page":"7871","DOI":"10.18653\/v1\/2020.acl-main.703","article-title":"\u201cBART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,\u201d","author":"Lewis","year":"2020","journal-title":"58th Annual Meeting of the Association for Computational Linguistics"},{"key":"B13","volume-title":"Manual for the Depression Anxiety Stress Scales.","author":"Lovibond","year":"1995"},{"key":"B14","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/W18-5511","article-title":"\u201cZero-shot relation classification as textual entailment,\u201d","volume-title":"Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)","author":"Obamuyide","year":"2018"},{"key":"B15","first-page":"365","article-title":"Symptom severity, anxiety, depression, self-efficacy and quality of life in patients with cancer","volume":"19","author":"Omran","year":"2018","journal-title":"Asian. Pac. J. Cancer. Prev."},{"key":"B16","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1136\/bmjqs-2012-001744","article-title":"Patient-centred healthcare, social media and the internet: the perfect storm?","volume":"22","author":"Rozenblum","year":"2013","journal-title":"BMJ. Qual. Saf."},{"key":"B17","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1136\/bmjqs-2017-006457","article-title":"The role of social media around patient experience and engagement","volume":"26","author":"Rozenblum","year":"2017","journal-title":"BMJ. Qual. Saf."},{"key":"B18","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1016\/j.drudis.2022.01.015","article-title":"Applications of quantitative social media listening to patient-centric drug development","volume":"27","author":"Schmidt","year":"2022","journal-title":"Drug. Discov. Today."},{"key":"B19","first-page":"1","article-title":"Searching social networks to detect adverse reactions","volume":"294","author":"Sukkar","year":"2015","journal-title":"Pharmaceut. J."},{"key":"B20","unstructured":"CDER patient-focused drug development2022"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2023.1237124\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T10:34:42Z","timestamp":1697106882000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2023.1237124\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,12]]},"references-count":20,"alternative-id":["10.3389\/frai.2023.1237124"],"URL":"https:\/\/doi.org\/10.3389\/frai.2023.1237124","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,12]]},"article-number":"1237124"}}