[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125885-en":3,"doc-seo-125885-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125885,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Machine Learning based Assessment of Preclinical Health Questionnaires","Modern health systems can access large volumes of diverse patient-related data, including information from mobile or wearable devices and from specialized medical systems. This study investigates how modern machine learning techniques can support preclinical health assessment using patient questionnaire data. A questionnaire was distributed in three maternity hospitals in Mureș County, Romania, and an ML pattern-detection model was developed for common risk assessment. Results on 1,278 respondents show feasibility with accuracy reaching 98% in the case study, enabling efficient digitization and analysis with reduced computational cost.","Machine Learning based Assessment of Preclinical Health Questionnaires  \nCalin Avram 1*+, Adrian Gligor 1*+, Dumitru Roman2,3, Ahmet Soylu3, Victoria Nyulas 1, Laura Avram4  \n1. George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, Romania; [calin.avram@umfst.ro](calin.avram@umfst.ro) ; [adrian.gligor@umfst.ro](adrian.gligor@umfst.ro), [victoria.rus@umfst.ro](victoria.rus@umfst.ro)  \n2. SINTEF AS, Norway; [dumitru.roman@sintef.no](dumitru.roman@sintef.no)  \n3. OsloMet – Oslo Metropolitan University, Norway; [ahmet.soylu@oslomet.no](ahmet.soylu@oslomet.no) ; [dumitrur@oslomet.no](dumitrur@oslomet.no)  \n4. “Dimitrie Cantemir” University ofTârgu-Mureș, Romania; [laura.avram@cantemir.ro](laura.avram@cantemir.ro)  \n* Correspondence: [calin.avram@umfst.ro](calin.avram@umfst.ro) ; [adrian.gligor@umfst.ro](adrian.gligor@umfst.ro)[ ](adrian.gligor@umfst.ro)+authors with equal rights  \nAbstract  \nBackground: Within modern health systems, the possibility of accessing a large amount and a variety of data related to patients' health has increased significantly over the years. The source of this data could be mobile and wearable electronic systems used in everyday life, and specialized medical devices. In this study we aim to investigate the use of modern Machine Learning (ML) techniques for preclinical health assessment based on data collected from questionnaires filled out by patients.  \nMethod: To identify the health conditions of pregnant women, we developed a questionnaire that was distributed in three maternity hospitals in the Mureș County, Romania. In this work we proposed and developed an ML model for pattern detection in common risk assessment based on data extracted from questionnaires.  \nResults: Out of the 1278 women who answered the questionnaire, 381 smoked before pregnancy and only 216 quit smoking during the period in which they became pregnant. The performance of the model indicates the feasibility of the solution, with an accuracy of 98% confirmed for the considered case study.  \nConclusion: The proposed solution offers a simple and efficient way to digitize questionnaire data and to analyze the data through a reduced computational effort, both in terms of memory and computing power used.  \nKeywords: public health; big data; feature extraction; machine learning; Hopfield neural network  \n1. Introduction  \nThe use of modern health systems has brought up the possibility of accessing a significant amount and a variety of data related to patients’ health. The source of this data could be mobile and wearable electronic systems used in everyday life, and specialized medical devices. These devices with access to communication networks and implicitly to the Internet enable storing collected data in traditional databases or more complex storage solutions offered by the cloud. This paves the way for long-term and real-time patient monitoring. Additionally, the  \nAuthor accepted manuscript version of the publication by  \nC. Avram, A. Gligor, D. Roman, A. Soylu, V. Nyulas & L. Avram.  \nIn International Journal of Medical Informatics, 180, 105248, December 2023.  \nPublished version: [https://doi.org/10.1016/j.ijmedinf.2023.105248](https://doi.org/10.1016/j.ijmedinf.2023.105248)  \navailability of medical data that could be collected through current solutions allows faster tracking of changes and evolutions in many fields of medicine [1,2] . The realization of international standards by which a set of data related to a medical condition or intervention are identically collected, even if they are taken from different places, would lead to a better understanding of the diseases and even to faster treatments [3] .  \nA very large part of medical data is in unstructured or even undefined formats. With the help of natural language processing (NLP), this problem could be alleviated by using methods such as semantic analysis and text mining [4-6]. In a broader sense, the processing and interpretation of med","cbCairkCp3StHfc4","https://ap.wps.com/l/cbCairkCp3StHfc4","pdf",412128,5,1,13,"English","en",105,"# Abstract\n# Introduction\n# Method\n# Results\n# Conclusion","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To evaluate the use of modern machine learning techniques for preclinical health assessment based on data extracted from patient-filled questionnaires.\"},{\"question\":\"How was the questionnaire data collected?\",\"answer\":\"A questionnaire was developed to identify the health conditions of pregnant women and distributed across three maternity hospitals in Mureș County, Romania.\"},{\"question\":\"What performance did the proposed ML model achieve?\",\"answer\":\"The model’s feasibility is supported by an accuracy of 98% for the considered case study.\"}]","Machine Learning based Assessment of Preclinical Health Questionnaires | PDF",1785901831,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-based-assessment-of-preclinical-health-questionnaires","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-based-assessment-of-preclinical-health-questionnaires/125885/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main objective of the study?","Question",{"text":77,"@type":78},"To evaluate the use of modern machine learning techniques for preclinical health assessment based on data extracted from patient-filled questionnaires.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How was the questionnaire data collected?",{"text":82,"@type":78},"A questionnaire was developed to identify the health conditions of pregnant women and distributed across three maternity hospitals in Mureș County, Romania.",{"name":84,"@type":75,"acceptedAnswer":85},"What performance did the proposed ML model achieve?",{"text":86,"@type":78},"The model’s feasibility is supported by an accuracy of 98% for the considered case study.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,119,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]