[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124162-en":3,"doc-seo-124162-105":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},124162,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Data-Driven Strategies for Carbimazole Titration - Exploring Machine Learning Solutions in Thyrotoxicity Control","University Hospitals Dorset (UHD) receives over 1,000 thyroid patient contacts annually, largely for autoimmune hyperthyroidism treated with Carbimazole titration. Dose adjustments are currently made by clinicians using thyroid function tests and then communicated to patients by letter, which is time-consuming and can delay treatment. This study develops and compares machine learning classification models to predict Carbimazole dosing from extracted anonymised patient data (n=353), aiming to enable rapid, safe dose determination and reduce workload and healthcare costs while supporting patient-led follow-up.","1 Data-Driven Strategies for Carbimazole Titration:  \n2 Exploring Machine Learning Solutions in  \n3 Thyrotoxicity Control  \n4 Reich, Thilo 1∗, Bakirov, Rashid 1 , Budka, Dominika 1 , Kelly, Derek2 Smith, James 1 Richardson, Tristan 1 ,2 , Budka, Marcin 1  \n1 Bournemouth University  \n2 University Hospitals Dorset  \n∗ Corresponding author & reprint requests: Thilo Reich, [treich-contact@runbox.com](treich-contact@runbox.com)[ ](treich-contact@runbox.com)ORCID: [https://orcid.org/0000-0001-7705-0987](https://orcid.org/0000-0001-7705-0987)  \n5 Funding received from the Higher Education Innovation Fund  \n6 Keywords: Carbimazole , Hyperthyroidism, Dose prediction, Machine  \n7 learning, Digital health, Mobile app  \n8 Abstract  \n9 University Hospitals Dorset (UHD) has over 1,000 thyroid  \n10 patient contacts annually. These are primarily patients with  \n11 autoimmune hyperthyroidism treated with Carbimazole titra- 12 tion. Dose adjustments are made by a healthcare professional  \n13 (HCP) based on the results of thyroid function tests. Based on  \n14 the test results, the HCP prescribes a dose and communicates  \n15 this to the patient via letter. This is time-consuming and in- 16 troduces treatment delays. This study aimed to replace some  \n17 time-intensive manual dose adjustments with a machine learning  \n18 model to determine Carbimazole dosing. This can in the future  \n19 serve patients with rapid and safe dose determination and ease  \n20 the pressures on HCPs.  \n21 Patient data of 421 hyperthyroidism at UHD were extracted  \n22 and anonymised. These data were processed and cleaned. A  \n23 total of 353 patients (83.85%) were included in the study. A  \n24 wide range of machine learning classification algorithms were  \n25 tested under different data processing regimes in an iterative  \n26 approach consisting of an initial model selection followed by a  \n27 feature selection method to improve performance. All models  \n28 were evaluated using weighted F1 scores (1=best) and Brier  \n29 scores to select the best performing model with the highest  \n30 confidence.  \n31 The best performance is achieved using a random forest (RF)  \n32 approach, resulting in good average F1 scores of 0.731 . Based on  \n33 a balanced assessment considering the accuracy of the prediction  \n34 (F1 = 0.755) and the confidence of the model (Brier score = 35 0.366), a model was selected with a view to a patient held app.  \n36 To simulate a use-case, the accumulation of the prediction error  \n37 over time was assessed. It was determined that an improvement  \n38 in accuracy is expected if this model was to be deployed in  \n39 practice.  \n40 1 Introduction  \n41 Thyroid function plays a crucial role in controlling metabolic rate and  \n42 cardiovascular function. This regulatory effect is mediated through  \n43 the thyroid hormones triiodothyronine (T3) and thyroxine (T4) . The  \n44 release of these hormones is, in turn, controlled by the thyroid stim- 45 ulating hormone (TSH) released from the pituitary gland (1) . Any  \n46 disturbance in the homeostasis of this hormonal system can lead to  \n47 disease. Generally, thyroid disease can be distinguished between hy- 48 pothyroidism and hyperthyroidism depending on the level of T3 and  \n49 T4 production. Hyperthyroidism is a very common endocrine disease  \n50 with an incidence of 0.2-1.3 %(2) and the most common referral to 51 Endocrinology. In 1977, a UK study reported a prevalence of 2.7%  \n52 in women and 0.23% in men, highlighting the high disease burden of 53 this disorder nationally (3) . There are three avenues of treatment for 54 hyperthyroidism: the use of antithyroid medication, the treatment 55 with radioactive iodine, or surgical removal of the thyroid gland (? 56 ) . In general, first-time treatment uses antithyroid medications for 57 6-18 months to allow patients the ”opportunity” to naturally gain 58 remission. In the UK, the first line of treatment is Carbimazole (? )  \n59 which involves the titration of the an","cbCaieXismuPSNA2","https://ap.wps.com/l/cbCaieXismuPSNA2","pdf",669714,1,42,"English","en",105,"# Abstract\n## Introduction\n## Background\n## Cardiovascular","[{\"question\":\"Why is Carbimazole dose adjustment time-consuming in current practice?\",\"answer\":\"Dose changes are made by healthcare professionals based on thyroid function tests and then communicated to patients via writing, which takes time and introduces treatment delays.\"},{\"question\":\"What data and patient sample were used to build the machine learning models?\",\"answer\":\"The study extracted and anonymised patient data for 421 hyperthyroidism cases at UHD, and 353 patients (83.85%) were included after cleaning and processing.\"},{\"question\":\"Which machine learning approach performed best for Carbimazole dose prediction?\",\"answer\":\"A random forest model achieved the best performance, with good average weighted F1 scores and assessment using Brier scores to select a model with higher confidence.\"}]","Data-Driven Strategies for Carbimazole Titration - 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