[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125235-en":3,"doc-seo-125235-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125235,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Are Our Predictions Healthy? A Comparative Meta-Analysis of Machine Learning Studies in Predictive Healthcare","Predictive healthcare for pancreatic neuroendocrine tumors is a critical decision point because treatment varies widely with disease heterogeneity and surgical aggressiveness. Machine learning models are used to forecast and classify PNETs to improve outcome estimation. This systematic review performs a comparative meta-analysis and quality assessment using the standardized IJMEDI checklist, revealing limitations in existing ML studies, including insufficient data pre-processing, unclear model architectures, and constrained clinical usability.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| ECIS 2024 Proceedings | European Conference on Information Systems\u003Cbr>(ECIS) |\n| --- | --- |\n| June 2024\u003Cbr>Are Our Predictions Healthy? A Comparative Meta-Analysis of Machine Learning Studies in Predictive Healthcare\u003Cbr>Kai Heinrich\u003Cbr>Otto-von-Guericke-Universität Magdeburg, kai. heinrich@o[vgu.de](vgu.de)\u003Cbr>Armin Keshavarzi\u003Cbr>Otto-von-Guericke-Universität Magdeburg, armin. keshav[arzi@st.ovgu.de](arzi@st.ovgu.de)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/ecis2024](https://aisel.aisnet.org/ecis2024) |  |\n\nRecommended Citation  \nHeinrich, Kai and Keshavarzi, Armin, \"Are Our Predictions Healthy? A Comparative Meta-Analysis of Machine Learning Studies in Predictive Healthcare\" (2024) . ECIS 2024 Proceedings. 2.  \n[https://aisel.aisnet.org/ecis2024/track18_healthit/track18_healthit/2](https://aisel.aisnet.org/ecis2024/track18_healthit/track18_healthit/2)  \nThis material is brought to you by the European Conference on Information Systems (ECIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ECIS 2024 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org).  \nARE OUR PREDICTIONS HEALTHY? A COMPARATIVE META-ANALYSIS OF MACHINE LEARNING STUDIES IN PREDICTIVE HEALTHCARE  \nCompleted Research Paper  \nKai Heinrich, OVGU Magdeburg, Germany, [kai.heinrich@ovgu.de](kai.heinrich@ovgu.de)  \nArmin Keshavarzi, OVGU Magdeburg, Germany, [armin.keshavarzi@st.ovgu.de](armin.keshavarzi@st.ovgu.de)  \nAbstract  \nPredictive healthcare in the case of pancreatic neuroendocrine tumors (PNETs) is a crucial operation as treatment challenges arise due to the heterogeneity of the disease. Surgical approaches vary based on aggressiveness, ranging from resection for milder cases to extensive removal for aggressive PNETs. Thus, machine learning (ML) models are crucial for precise prediction and categorizing PNETs for enhanced outcome forecasting. This systematic review sheds light on the practices of ML approaches within a comparative meta-analysis and a quality assessment employing the standardized IJMEDI checklist. The results show that ML studies within the field of predictive healthcare, despite their potential, face challenges like inadequate data pre-processing, unclear model architecture, and limited clinical applicability.  \nKeywords: Predictive healthcare, systematic review, meta-analysis, quality.  \n1 Introduction  \nPancreatic neuroendocrine tumors (PNETs) are the second most common type of solid tumors in the pancreas and originate from neuroendocrine cells. The primary approach for treating PNETs is surgery, which is the sole method for achieving a cure. Various surgical techniques are available based on the tumor's level of aggressiveness. In cases of less aggressive PNETs, surgical options like exenteration or resection can mitigate surgical risks while not impacting patient prognosis. Conversely, aggressive PNENs necessitate extensive removal along with lymph node dissection, and in cases where required, combination with pharmaceutical intervention is advised to lower the chances of recurrence (X.-T. Huang et al. 2022) .  \nUtilizing machine learning (ML) models to predict PNETs, rare and heterogeneous tumors involving the pancreas, is significant for several reasons. PNETs are uncommon and diverse growths exhibiting varying biological behaviors and prognoses based on their grade, stage, and molecular attributes. ML models can potentially categorize PNETs into subtypes and pinpoint the most pertinent characteristics for forecasting outcomes (Park et al., 2023) . Secondly, they often present with no symptoms or nonspecific symptoms, making them difficult to diagnose early. Hence, prediction models can facilitate the detection of PNETs from diverse data sources, such as imaging, blood ","cbCailr3a3beBOKj","https://ap.wps.com/l/cbCailr3a3beBOKj","pdf",483651,1,17,"English","en",105,"# Abstract\n## Introduction\n## Machine Learning for PNET Prediction\n## Challenges and Motivation\n## Review Aim and Scope","[{\"question\":\"Why is predictive healthcare important for pancreatic neuroendocrine tumors (PNETs)?\",\"answer\":\"Treatment decisions depend on tumor aggressiveness and disease heterogeneity, and PNETs may be difficult to diagnose early. Predictive healthcare supports better outcome forecasting and classification.\"},{\"question\":\"How does the document evaluate machine learning studies in predictive healthcare?\",\"answer\":\"It conducts a systematic review with a comparative meta-analysis and a quality assessment using the standardized IJMEDI checklist.\"},{\"question\":\"What key challenges affect the current machine learning research quality?\",\"answer\":\"The review highlights issues such as inadequate data pre-processing, unclear model architecture, and limited clinical applicability.\"}]","Are Our Predictions Healthy? A Comparative Meta-Analysis of Machine Learning Studies in Predictive Healthcare | PDF",1785897637,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"are-our-predictions-healthy-a-comparative-meta-analysis-of-machine-learning-studies-in-predictive-healthcare","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/are-our-predictions-healthy-a-comparative-meta-analysis-of-machine-learning-studies-in-predictive-healthcare/125235/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predictive healthcare important for pancreatic neuroendocrine tumors (PNETs)?","Question",{"text":75,"@type":76},"Treatment decisions depend on tumor aggressiveness and disease heterogeneity, and PNETs may be difficult to diagnose early. 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