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研究生:姜朝祐
研究生(外文):CHIANG, CHAO-YU
論文名稱:利用人工智慧之逆運算疊代法評量癲癇患者之藥物治療劑量
論文名稱(外文):Applying the Inverse Problem Algorithm in Artificial Intelligence to Evaluate the Effective Concentration of Serum Valproic Acid in Epilepsy Patients
指導教授:潘榕光
指導教授(外文):PANG, LUNG-KUANG
口試委員:潘榕光潘龍發楊登和彭炳儒陳永福
口試委員(外文):PANG, LUNG-KUANGPANG, LUNG-FAYANG, DENG-HUOPENG, BIN-RUCHEN, YONG-FU
口試日期:2023-06-20
學位類別:博士
校院名稱:中臺科技大學
系所名稱:醫學影像暨放射科學系暨研究所
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:123
中文關鍵詞:逆運算法丙戊酸有效血中濃度群體藥物動力學
外文關鍵詞:Inverse problem algorithmValproic acidEffective blood concentrationPopularion pharmacokinetic
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本研究是運用逆運算疊代法,可透過已知輸出和輸入之間的關係式的情況下,通過逆推算法推導出輸入量的值;而運用在藥物治療領域中,逆運算疊代法可以用於預測藥物在人體內的濃度,從而幫助醫師確定治療方案和藥物劑量,希望建立一套科學數據運算模型,透過非侵入性預測服用丙戊酸(Valproic acid, VPA) 前就能預測病患的丙戊酸藥物治療的有效血中濃度,使臨床醫師做為開立處方之依據。本研究先收集453例癲癇病患者臨床上之可能影響藥物血中濃度的因子,包含年齡(Age)、體表面積(BSA)、血清尿素氮(BUN)、平均動脈壓(MAP)、肌酸酐(Creatinine)、天門冬胺酸轉胺酶(GOT)及丙胺酸轉胺酶(GPT)等可能影響丙戊酸血中有效濃度相關因子。並將上述整合成一個二十九項的一階非線性方程式,再經由STATISTICA 7.0軟體進行逆運算疊代法演算,後續再透過另一組124位相似條件患者進行驗證。經由分析後得出其最終損失函數值Loss function(Φ),回歸方差Variance of regression, (S2),及變異數分別為2.249,0.9382,0.9686,而兩組數據中實際值和預測值間決定係數(R2)分別為0.9381及0.871。以上內容表示這七個影響因子對評估癲癇病患的臨床數據預測服用丙戊酸後之有效血中濃度程度都會造成影響。且得知在個別因子中,以丙胺酸轉胺酶(GPT)、平均動脈壓(MAP)和肌酸酐(Creatinine)的影響較大,但血清尿素氮(BUN)與丙胺酸轉胺酶(GPT)及平均動脈壓(MAP)與血清尿素氮(BUN)的交互作用影響同樣不可小覷。同時本研究建立體內動力學模組,模擬丙戊酸進入人體後的吸收、分布、代謝及排除狀況,結合群體藥物動力學概念來描述藥品於各器官的代謝情形,以驗證採用逆運算疊代法來預測藥物血中濃度的可行性,研究結果均符合臨床醫學實證及理論模擬結果,顯示運用科學數據運算模型,應用於非侵入性預測服用藥物前就能預測病患有效血中濃度是準確可行的,能快速準確診斷疾病的嚴重程度及準確掌握藥物治療的有效血中濃度以達防止中毒的副作用產生,嘉惠癲癇患者。
This study applies the inverse problem algorithm to derive the expected value through the iteration algorithm. In the field of drug therapy, the inverse problem algorithm can predict the concentration of drugs in the human body to determine treatment plans and drug doses for physicans. The goal of this study is to establish a scientific data calculation model that can predict the effective blood concentration of valproic acid medication for patients before taking it, so that clinical physicians can prescribe on the basis of prediction.We first collected 453 clinical factors that may affect drug concentration in the blood of epilepsy patients, including age, body surface area, blood urea nitrogen, mean arterial pressure, creatinine, aspartate aminotransferase (GOT), and alanine aminotransferase (GPT). Seven factors were compiled into a 29-item first-order nonlinear equation, and the inverse problem algorithm was performed using STATISTICA 7.0 software. The theoretical results were further validated with another group of 124 patients with similar syndromes. Accordingly, the loss function value (Φ), variance of regression (S2), and variability were found to be 2.249, 0.9382, and 0.9686. The coefficient of determination (R2) between actual and predicted values were 0.9381 and 0.871 for original and follow-up group of data, respectively. Furthermore, alanine aminotransferase (GPT), mean arterial pressure (MAP), and creatinine have a dominant impact, and same for the interaction between blood urea nitrogen (BUN) and alanine aminotransferase (GPT) or mean arterial pressure (MAP). This study constructed an in vivo pharmacokinetic model to simulate the absorption, distribution, metabolism, and elimination of valproic acid in the human body. The pharmacokinetic model described the drug metabolism in various organs, and the feasibility of predicting drug concentrations in the blood using the inverse problem algorithm was verified as well. The derived results were consistent with clinical medical evidence and theoretical simulation results, demonstrating that the application of scientific data computing models for prediction of effective blood concentrations before taking medication is accurate and feasible. This method can quickly and accurately diagnose the severity of the disease and grasp the effective blood concentration of drug therapy to prevent the occurrence of toxic side effects, benefiting patients with epilepsy.
誌謝 I
摘要 II
ABSTRACT IV
目錄 VI
圖目錄 X
表目錄 XII
第一章、前言 1
1.1 研究動機 1
1.2 研究目的 2
1.3 文章架構 3
第二章、背景說明與文獻分析 4
2.1 研究背景 4
2.1.1癲癇 4
2.1.1.1癲癇流行病學 4
2.1.1.2癲癇病理學 5
2.1.1.3癲癇診斷及評估 7
2.1.1.4癲癇臨床藥物治療 11
2.2 丙戊酸(VALPROIC ACID) 12
2.2.1 丙戊酸藥物動力學 13
2.2.2 丙戊酸的臨床用途 14
2.2.3丙戊酸治療癲癇的評估 17
2.3 相關文獻分析 18
2.3.1癲癇治療 18
2.3.2丙戊酸 (VALPROIC ACID) 19
2.3.3群體藥物動力學 20
2.3.4醫療大數據分析 21
2.3.5醫療大數據運用於預防醫學 23
第三章、實驗方法 25
3.1 逆運算分析法(INVERSE PROBLEM ALGORITHM) 25
3.2 修正逆運算分析法(REVISED INVERSE PROBLEM ALGORITHM) 25
3.3 理論預測方程式 26
3.4 正規化(NORMALIZATION) 26
3.5 統計工具與方法 27
3.6 實驗規劃及流程 27
3.7 STATISTICA操作方式 29
第四章、研究原始資料 32
4.1 影響藥物濃度因子的選擇 32
4.1.1年齡 32
4.1.2 體表面積(BODY SURFACE AREA, BSA) 32
4.1. 3平均動脈壓(MEAN ARTERIAL PRESSURE, MAP) 33
4.1.4血中尿氮素(BLOOD UREA NITROGEN, BUN) 34
4.1.5肌酸酐(CREATININE) 34
4.1.6 天門冬胺酸轉胺酶(GOT)及丙胺酸轉胺酶(GPT) 35
4.2丙戊酸(VALPROIC ACID)血中濃度之評估 36
4.3原始數據 36
4.3.1 初始數據(INITIAL DATA) 36
4. 3.2驗證數據(VERIFY THE DATA) 36
第五章、結果 37
5.1 研究結果---原始數據正規化 37
5.2 研究結果- --STATISTICA 7分析預測值 37
5.3 研究結果---貢獻值及正規化後的排序 38
5.4 研究結果--- VPA血中有效濃度資料庫實際與預測 39
5.5 研究結果---VPA驗證數據正規化與預測值 39
5.6 研究結果--- VPA血中有效濃度驗證實際與預測 39
第六章、討論 40
6.1 實做範例 40
6.1.1臨床影響丙戊酸(VALPROIC ACID)血中治療濃度之因子的篩選 40
6.1.2影響丙戊酸(VALPROIC ACID)有效血中濃度之因子的交互作用 40
6.1.3影響丙戊酸(VALPROIC ACID)有效血中濃度之臨床驗證 41
6.1.4影響丙戊酸(VALPROIC ACID)血中有效濃度之控制因子個數 43
6.1.4.1 減因子設計 43
6.2 主要三個關鍵因子影響癲癇之預測 46
6.3 跳脫邊界之分析 47
6.4丙戊酸-藥物動力學預測模型 50
6.4.1前言 50
6.4.2實驗理論設計 50
6.4.2.1多腔室體內動力學模組 50
6.4.2.2模組設定基本參數 51
6.4.2.3預期各器官濃度變化 53
6.4.3器官功能異常代謝模擬 54
6.4.3.1肝功能異常 54
6.4.3.2腎功能異常 56
第七章、結論 59
第八章、未來展望 61
參考文獻 62
附表 71
附表一: 癲癇患者的初始生理數據(INITIAL DATA) 71
附表二:癲癇病患的驗證數據(VERIFY THE DATA) 85
附表三:癲癇病患的原始生理數據正規化與預測值 89
附表四:癲癇病患的驗證數據(VERIFY THE DATA) 103
相似度分析 108
圖目錄
圖 一:癲癇患病率隨年齡成長趨勢圖 5
圖 二:癲癇患者的腦部CT影像 8
圖 三:腦部顳葉硬化的癲癇患者 9
圖 四:具腦部腫瘤的癲癇患者 9
圖 五:癲癇患者的腦部PET影像 10
圖 六:癲癇發作期的SPECT影像 11
圖 七:Valproic acid化學結構式 13
圖 八:2010~2018年與醫療大數據有關的出版物數量 23
圖 九:實驗規劃及流程 28
圖 十:因子之原始數據正規化後數據套入表格中 29
圖 十一:正規化數據做統計上非線性分析 30
圖 十二:STATISTICA 7 程式軟體中選擇定義回歸、自訂損失函數 30
圖 十三:制定的二十九項的一階乘非線性方程式套入程式中 30
圖 十四:STATISTICA 7 程式軟體中選擇香蕉函式及類牛頓法進行分析 31
圖 十五:STATISTICA 7 程式軟體分析結果 31
圖 十六:VPA藥物有效血中濃度 STATISTICA 回歸分析值 37
圖 十七:Digoxin血中有效濃度實際與預測分布 39
圖 十八:VPA血中有效濃度驗證實際與預測 39
圖 十九:根據STATISTICA衍生的線性回歸 42
圖 二十:改變因子數量的預測VPA血中濃度圖 45
圖 二十一:以三個主要因子預測癲癇患者之藥物有效血中濃度 47
圖 二十二:丙戊酸於人體代謝路徑及分歧圖 52
圖 二十三:VPA於體內各器官代謝模擬趨勢圖 53
圖 二十四:肝功能正常成VPA代謝模擬趨勢圖 55
圖 二十五:肝功能剩餘50% VPA代謝模擬趨勢圖 55
圖 二十六、肝功能剩餘30% VPA代謝模擬趨勢圖 55
圖 二十七、腎排除功能正常成人VPA代謝模擬趨勢圖 57
圖 二十八、75%腎排除功能VPA代謝模擬趨勢圖 57
圖 二十九、50%腎排除功能VPA代謝模擬趨勢圖 57
圖 三十、25%腎排除功能VPA代謝模擬趨勢圖 58
圖 三十一、15%腎排除功能VPA代謝模擬趨勢圖 58
表目錄
表 1:國際抗癲癇聯盟癲癇發作分類圖 6
表 2:影響藥物血中有效濃度因子貢獻值及正規化後的排序 38
表 3:服用丙戊酸的癲癇患者的臨床數據和有效血中藥濃度讀數 42
表 4:因子刪減次序 44
表 5:根據STATISTICA程序中因子數計算得的線性回歸線之參數 45
表 6:跳脫邊界之分析-以原始數據超出原始數據集範圍 49
表 7:白蛋白指標分類及實驗定義 54
表 8:肝功能不全的VPA使用患者體內濃度推測圖 56
表 9:腎絲球過濾率指標分類及實驗定義 56


1.Haag, A., Strzelczyk, A., Bauer, S., et al. (2010). Quality of life and employment status are correlated with antiepileptic monotherapy versus polytherapy and not with use of "newer" versus "classic" drugs: results of the "Compliant 2006" survey in 907 patients. Epilepsy & Behavior, 19, 618.
2.Tchalla, A. E., Marin, B., Mignard, C., et al. (2011). Newly diagnosed epileptic seizures: focus on an elderly population on the French island of Réunion in the Southern Indian Ocean. Epilepsia, 52, 2203.
3.衛生福利部統計處 (2019). 2.3.2 身心障礙者福利. Retrieved from https://dep.mohw.gov.tw/DOS/cp-2976-13821-113.html.
4.Loiseau, J., Crespel, A., Picot, M. C., et al. (1998). Idiopathic generalized epilepsy of late onset. Seizure, 7, 485.
5.Marini, C., King, M. A., Archer, J. S., et al. (2003). Idiopathic generalised epilepsy of adult onset: clinical syndromes and genetics. Journal of Neurology, Neurosurgery, and Psychiatry, 74, 192.
6.Cutting, S., Lauchheimer, A., Barr, W., & Devinsky, O. (2001). Adult-onset idiopathic generalized epilepsy: clinical and behavioral features. Epilepsia, 42, 1395.
7.Gaillard, W. D., Chiron, C., Cross, J. H., et al. (2009). Guidelines for imaging infants and children with recent-onset epilepsy. Epilepsia, 50, 2147.
8.Krumholz, A., Wiebe, S., Gronseth, G., et al. (2007). Practice Parameter: evaluating an apparent unprovoked first seizure in adults (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology and the American Epilepsy Society. Neurology, 69, 1996.
9.Terbach, N., & Williams, R. S. (2009). Structure Function studies for the panacea, valproic acid. Biochemical Society Transactions, 37, 1126-132.
10.Gerady, J. (1984). The effects of sodium valproate on gamma-aminobutyrate metabolism and behaviour in naive and ethanolamine-O-sulphate pretreated rats and mice. Pharmaceutica Belgica, 39(4), 205-208.
11.McNamara, J.O. (1996). Drugs effective in the therapy of the epilepsies. In J.G. Hardman, L.E. Limbird, P.B. Molinoff, et al. (Eds.), Goodman & Gilman's The Pharmacological basis of therapeutics (9th ed., pp. 476). McGraw-Hill.
12.Yukawa, E. (1999). Population-based investigations of drug relative clearance using nonlinear mixed-effect modelling from information generated during the routine clinical care of patients. Journal of Clinical Pharmacy and Therapeutics, 24(2), 103-113.
13.Annegers, J.F., Hauser, W.A., & Elveback, L.R. (1979). Remission of seizures and relapse in patients with epilepsy. Epilepsia, 20, 729-737.
14.Scheuer, M.L., & Cohen, J. (1993). Seizures and epilepsy in the elderly. Neurologic Clinics, 11, 787-804.
15.Chen, R.C., Chang, Y.C., Chen, T.H., Wu, H.M., & Liou, H.H. (2005). Mortality in adult patients with epilepsy in Taiwan. Epileptic Disorders, 7, 213-219.
16.Baker, G.A., Nashef, L., & van Hout, B.A. (1997). Current issues in the management of epilepsy: the impact of frequent seizures on cost of illness, quality of life, and mortality. Epilepsia, 38(Suppl 1), S1-S8.
17.Khoo, A., de Tisi, J., Mannan, S., et al. (2021). Reasons for not having epilepsy surgery. Epilepsia, 62, 2909.
18.Chang, B.S., & Lowenstein, D.H. (2003). Epilepsy. The New England Journal of Medicine, 349(13), 1257–1266.
19.Sander, J.W., Hart, Y.M., Johnson, A.L., & Shorvon, S.D. (1990). National General Practice Study of Epilepsy: newly diagnosed epileptic seizures in a general population. The Lancet, 336, 1267.
20.Fisher, R.S., Cross, J.H., French, J.A., et al. (2017). Operational classification of seizure types by the International League Against Epilepsy: Position paper of the ILAE Commission for Classification and Terminology. Epilepsia, 58(4), 522-530.
21.Beghi, E., Carpio, A., Forsgren, L., et al. (2010). Recommendation for a definition of acute symptomatic seizure. Epilepsia, 51, 671.
22.Fisher, R. S., Cross, J. H., French, J. A., et al. (2017). Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia, 58, 522.
23.Fisher, R. S., Cross, J. H., D'Souza, C., et al. (2017). Instruction manual for the ILAE 2017 operational classification of seizure types. Epilepsia, 58, 531.
24.McBride, A. E., Shih, T. T., & Hirsch, L. J. (2002). Video-EEG monitoring in the elderly: A review of 94 patients. Epilepsia, 43, 165.
25.Beghi, E., Carpio, A., Forsgren, L., et al. (2010). Recommendation for a definition of acute symptomatic seizure. Epilepsia, 51(4), 671-675.
26.Tranvinh, E., Lanzman, B., Provenzale, J., & Wintermark, M. (2019). Imaging evaluation of the adult presenting with new-onset seizure. AJR American Journal of Roentgenology, 212, 15.
27.Spencer, S. S., McCarthy, G., & Spencer, D. D. (1993). Diagnosis of medial temporal lobe seizure onset: Relative specificity and sensitivity of quantitative MRI. Neurology, 43, 2117.
28.Farid, N., Girard, H. M., Kemmotsu, N., et al. (2012). Temporal lobe epilepsy: Quantitative MR volumetry in detection of hippocampal atrophy. Radiology, 264, 542-543.
29.Lapalme-Remis, S., & Nguyen, D. K. (2022). Neuroimaging of epilepsy. Continuum (Minneapolis, Minn.), 28, 306.
30.Van Paesschen, W., Dupont, P., Sunaert, S., et al. (2007). The use of SPECT and PET in routine clinical practice in epilepsy. Current Opinion in Neurology, 20, 194.
31.Lee, S. K., Lee, S. Y., Yun, C. H., et al. (2006). Ictal SPECT in neocortical epilepsies: Clinical usefulness and factors affecting the pattern of hyperperfusion. Neuroradiology, 48, 678.
32.Fisher, R. S., Acevedo, C., Arzimanoglou, A., et al. (2014). ILAE official report: A practical clinical definition of epilepsy. Epilepsia, 55, 475.
33.Shih, J. J., Whitlock, J. B., Chimato, N., et al. (2017). Epilepsy treatment in adults and adolescents: Expert opinion, 2016. Epilepsy & Behavior, 69, 186.
34.Kwan, P., & Brodie, M. J. (2001). Effectiveness of first antiepileptic drug. Epilepsia, 42, 1255.
35.McLean, M. J., & Macdonald, R. L. (1986). Sodium valproate, but not ethosuximide, produces use- and voltage-dependent limitation of high-frequency repetitive firing of action potentials of mouse central neurons in cell culture. Journal of Pharmacology and Experimental Therapeutics, 237, 1001.
36.Gram, L. (1988). Experimental studies and controlled clinical testing of valproate and vigabatrin. Acta Neurologica Scandinavica, 78, 241.
37.Löscher, W. (1981). Valproate induced changes in GABA metabolism at the subcellular level. Biochemical Pharmacology, 30, 1364..
38.Ramsay, R. E., Cantrell, D., Collins, S. D., et al. (2003). Safety and tolerance of rapidly infused Depacon. A randomized trial in subjects with epilepsy. Epilepsy Research, 52, 189.
39.Gerstner, T., Teich, M., Bell, N., et al. (2006). Valproate-associated coagulopathies are frequent and variable in children. Epilepsia, 47, 1136.
40.Dreifuss, F. E., Santilli, N., Langer, D. H., et al. (1987). Valproic acid hepatic fatalities: A retrospective review. Neurology, 37, 379.
41.Patsalos, P. N., Zugman, M., Lake, C., et al. (2017). Serum protein binding of 25 antiepileptic drugs in a routine clinical setting: A comparison of free non-protein-bound concentrations. Epilepsia, 58(7), 1234-1243.
42.Noven Therapeutics, LLC. (2008). STAVZOR(R) delayed release oral capsules, valproic acid delayed release oral capsules [Product Information]. Miami, FL.
43.UPSHER-SMITH LABORATORIES. (2003). Valproic acid oral capsule, valproic acid oral capsule [Product Information]. St Petersburg, FL.
44.AbbVie Inc. (per Manufacturer). (2013). DEPAKENE oral capsules, oral solution, valproic acid oral capsules, oral solution [Product Information]. North Chicago, IL.
45.Bialer, M. (2007). Extended-release formulations for the treatment of epilepsy. CNS Drugs, 21(9), 765-774.
46.Ghaleiha, A., Haghighi, M., Sharifmehr, M., et al. (2014). Oral loading of sodium valproate compared to intravenous loading and oral maintenance in acutely manic bipolar patients. Neuropsychobiology, 70(1), 29-35.
47.Linde, M., Mulleners, W. M., Chronicle, E. P., et al. (2013). Valproate (valproic acid or sodium valproate or a combination of the two) for the prophylaxis of episodic migraine in adults. Cochrane Database of Systematic Reviews
48.Hiemke, C., Bergemann, N., Clement, H. W., et al. (2018). Consensus guidelines for therapeutic drug monitoring in neuropsychopharmacology: Update 2017. Pharmacopsychiatry, 51(1-02), 9-62.
49.Osborn, H. H. (1996). Phenytoin toxicity. In J. E. Tintinalli, E. Ruiz, & R. L. Krome (Eds.), Emergency medicine: A comprehensive study guide (4th ed., pp. 807-811).
50.Gareri, P., Lacava, R., Cotroneo, A., et al. (2009). Valproate-induced delirium in a demented patient. Archives of Gerontology and Geriatrics, 49(Suppl 1), 113-118.
51.Canevini, M. P., De Sarro, G., Galimberti, C. A., et al. (2010). Relationship between adverse effects of antiepileptic drugs, number of coprescribed drugs, and drug load in a large cohort of consecutive patients with drug-refractory epilepsy. Epilepsia, 51, 797.
52.Beghi, E., Gatti, G., Tonini, C., et al. (2003). Adjunctive therapy versus alternative monotherapy in patients with partial epilepsy failing on a single drug: a multicentre, randomised, pragmatic controlled trial. Epilepsy Research, 57, 1.
53.Sake, J. K., Hebert, D., Isojärvi, J., et al. (2010). A pooled analysis of lacosamide clinical trial data grouped by mechanism of action of concomitant antiepileptic drugs. CNS Drugs, 24, 1055.
54.Sukumaran, S. C., Sarma, P. S., & Thomas, S. V. (2010). Polytherapy increases the risk of infertility in women with epilepsy. Neurology, 75, 1351.
55.Lamberink, H. J., Otte, W. M., Geleijns, K., & Braun, K. P. (2015). Antiepileptic drug withdrawal in medically and surgically treated patients: a meta-analysis of seizure recurrence and systematic review of its predictors. Epileptic Disorders, 17, 211.
56.Wiebe, S., Blume, W. T., Girvin, J. P., et al. (2001). A randomized, controlled trial of surgery for temporal-lobe epilepsy. New England Journal of Medicine, 345, 311.
57.Jehi, L., Jette, N., Kwon, C. S., et al. (2022). Timing of referral to evaluate for epilepsy surgery: Expert Consensus Recommendations from the Surgical Therapies Commission of the International League Against Epilepsy. Epilepsia, 63, 2491.
58.McLean, M. J., & Macdonald, R. L. (1986). Sodium valproate, but not ethosuximide, produces use- and voltage-dependent limitation of high frequency repetitive firing of action potentials of mouse central neurons in cell culture. Journal of Pharmacology and Experimental Therapeutics, 237, 1001.
59.Nevitt, S. J., Sudell, M., Weston, J., et al. (2017). Antiepileptic drug monotherapy for epilepsy: a network meta-analysis of individual participant data. Cochrane Database of Systematic Reviews, 12, CD011412.
60.Doré, M., San Juan, A. E., Frenette, A. J., & Williamson, D. (2017). Clinical Importance of Monitoring Unbound Valproic Acid Concentration in Patients with Hypoalbuminemia. Pharmacotherapy, 37, 900.
61.Yukawa, E. (1999). Population-based investigations of drug relative clearance using nonlinear mixed-effect modelling from information generated during the routine clinical care of patients. Journal of Clinical Pharmacy and Therapeutics, 24(2), 103-113.
62.U.S. Food and Drug Administration. (1999). Guidance for industry - population pharmacokinetics. Retrieved from https://www.fda.gov/regulatory-information/search-fda-guidance-documents/population-pharmacokinetics.
63.Whiting, B., Kelman, A. W., & Grevel, J. (1986). Population pharmacokinetics. Theory and clinical application. Clinical Pharmacokinetics, 11(5), 387-401.
64.Beal, S. L., & Sheiner, L. B. (1989-98). NONMEM User Guides. GloboMax LLC, Hanover, MD
65.Trifiro, G., Sultana, J., & Bate, A. (2018). From big data to smart data for pharmacovigilance: The role of healthcare databases and other emerging sources. Drug Safety, 41, 143-149.
66.Liu, Q., Yang, J., Zhang, J., et al. (2019). Description of clinical characteristics of VAP patients in MIMIC database. Frontiers in Pharmacology, 10, 62.
67.Ollier, W. E., Sprosen, T., & Peakman, T. C. (2005). UK Biobank: From concept to reality. Pharmacogenomics, 6, 639-646.
68.Resteghini, C., Trama, A., Borgonovi, et al. (2018). Big data in head and neck cancer. Current Treatment Options in Oncology, 19(12), 1-15.
69.Sahu, H., Shrma, S., & Gondhalakar, S. (2011). A brief overview on data mining survey. International Journal of Computer Technology and Electronics Engineering, 1(3), 114-121.
70.Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future - big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219
71.Richards, G. (2001). Data mining for indicators of early mortality in a database of clinical records. Artif Intell Med, 22(3), 215-231.
72.Li, X., Cui, L., Zhang, G. Q., & Lhatoo, S. D. (2021). Can Big Data guide prognosis and clinical decisions in epilepsy? Epilepsia, 62(Suppl. 2), S106-S115.
73.Duncan, D., Vespa, P., Pitkänen, A., et al. (2019). Big data sharing and analysis to advance research in post-traumatic epilepsy. Neurobiology of Disease, 123, 127-136.
74.Anttila, V., Bulik-Sullivan, B., Finucane, H. K., Walters, R. K., et al. (2018). Analysis of shared heritability in common disorders of the brain. Science, 360(6395), eaap8757.
75.Baldassano, S. N., Hill, C. E., Shankar, A., Bernabei, et al. (2019). Big data in status epilepticus. Epilepsy & Behavior, 101, 106457.
76.Nolte, D., & Bertoglio, C. (2022). Inverse problems in blood flow modeling: A review. International Journal for Numerical Methods in Biomedical Engineering, 38(8), e3613.
77.Galka, A., Yamashita, O., Ozaki, T., Biscay, R., & Valdés-Sosa, P. (2004). A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering. NeuroImage, 23(2), 435-453.
78.Pan, L. F., Chiu, S. W., Xiao, M. F., Chen, C. H., & Pan, L. K. (2017). Revised inverse problem algorithm-based prediction of coronary artery stenosis readings from the clinical data of patients with coronary heart diseases. Computer Assisted Surgery, 22(sup1), 70-78.
79.Kaipio, J., & Somersalo, E. (2006). Statistical and computational inverse problems (Vol. 160). Springer Science & Business Media.
80.Shouman, M., Turner, T., Stocker, R., et al. (2012). Using data mining techniques om heart disease diagnosis and treatment. In Japan-Egypt Conference on Electronics, Communications and Computers (pp. 189-193). IEEE.
81.Pan, L. F., Davva, O., Chen, C. Y., et al. (2015). Quantitative evaluation of contrast-induced-nephropathy in vascular post-angiography patients: feasibility study of a semiempirical model. BME, 26, s851–s860.
82.Inverse problem algorithm. (2015). Available from: https://en.wikipedia.org/wiki/Inverse_problem.
83.Tereshin, E. B., Trofimov, V. A., & Fedotov, M. V. (2006). Conservative finite-difference scheme for the problem of propagation of a femtosecond pulse in a nonlinear photonic crystal with nonreflecting boundary conditions. Computational Mathematics and Mathematical Physics, 46(1), 154-164.
84.Scott, J. C., & Stanski, D. R. (1987). Decreased fentanyl and alfentanil dose requirements with age: a simultaneous pharmacokinetic and pharmacodynamic evaluation. Journal of Pharmacology and Experimental Therapeutics, 240(1), 159-166.
85.Tan, J. L., Eastment, J. G., Poudel, A., et al. (2015). Age-Related Changes in Hepatic Function: An Update on Implications for Drug Therapy. Drugs & Aging, 32(12), 999-1008.
86.Body Surface Area. (n.d.). In Wikipedia. Retrieved October 15, 2021, from https://en.wikipedia.org/wiki/Body_surface_area.
87.Koyfman, A., Ng, C., Foran, M. P., & Pediatric Dehydration. (2021). In Medscape. Retrieved October 15, 2021, from http://emedicine.medscape.com/article/801012-overview.
88.Vaccarino, V., Berger, A. K., Abramson, J., et al. (2001). Pulse pressure and risk of cardiovascular events in the systolic hypertension in the elderly program. American Journal of Cardiology, 88(9), 980-986.
89.Peng, R., Liu, K., Li, W., et al. (2021). Blood urea nitrogen, blood urea nitrogen to creatinine ratio and incident stroke: The Dongfeng-Tongji cohort. Atherosclerosis, 333, 1-8.
90.Kamimura, D., Ohtani, T., Sakata, Y., et al. (2012). Ca2+ entry mode of Na+/Ca2+ exchanger as a new therapeutic target for heart failure with preserved ejection fraction. European Heart Journal, 33(11), 1408-1416.
91.Nalpas, B., Vassault, A., Charpin, S., et al. (1986). Serum mitochondrial aspartate aminotransferase as a marker of chronic alcoholism: diagnostic value and interpretation in a liver unit. Hepatology, 6(2), 608-615.
92.Rej, R. (1984). Measurement of aminotransferases: Part 1. Aspartate aminotransferase. Critical Reviews in Clinical Laboratory Sciences, 21(2), 99-128.
93.Mortensen, P. B., Hansen, H. E., Pedersen, B., et al. (1983). Acute valproate intoxication: biochemical investigations and hemodialysis treatment. International Journal of Clinical Pharmacology, Therapy and Toxicology, 21(2), 64-68.
94.Farrar, H. C., Herold, D. A., & Reed, M. D. (1993). Acute valproic acid intoxication: enhanced drug clearance with oral-activated charcoal. Critical Care Medicine, 21(2), 299-303.
95.McNamara, J. O. V. (1996). Drugs effective in the therapy of the epilepsies. In J. G. Hardman, L. E. Limbird, P. B. Molinoff et al. (Eds.), Goodman & Gilman's The Pharmacological basis of therapeutics (9th ed., pp. 476). McGraw-Hill.
96.Chadwick, D. W. (1985). Concentration-effect relationships of valproic acid. Clinical Pharmacokinetics, 10, 155-163.
97.Gugler, R., & von Unruh, G. E. (1980). Clinical pharmacokinetics of valproic acid. Clinical Pharmacokinetics, 5, 67-83.
98.AbbVie Inc. (2017). Product Information: Depakote ER oral extended-release tablets, divalproex sodium oral extended-release tablets. U.S. Food and Drug Administration. North Chicago, IL.
99.AbbVie Inc. (2017). Product Information: Depakote oral tablets, divalproex sodium oral tablets. U.S. Food and Drug Administration. North Chicago, IL.
100.AbbVie Inc. (2016). Product Information: Depakote Sprinkle Capsules oral capsules, divalproex sodium oral delayed release capsules. U.S. Food and Drug Administration. North Chicago, IL.
101.Pollack, G. M., & Brouwer, K. L. (1991). Physiologic and metabolic influences on enterohepatic recirculation: simulations based upon the disposition of valproic acid in the rat. Journal of Pharmacokinetics and Biopharmaceutics, 19, 189-225.
102.Chen, C. Y., Chang, P. J., Changlai, S. P. et al. (2007). Effective Half Life of Iodine for Five Thyroidectomy Patients Using an in vivo Gamma Camera Approach. Journal of Radiological Research, 48(6), 485-493.
103.Liu, B., Peng, W., Huang, R. et al. (2014). Thyroid Cancer: Radiation Safety Precautions in 131I Therapy Based on Actual Biokinetic Measurements. Radiology, 273(1), 211-219.
104.Hong, C. M., Kim, C. Y., Son, S. H. et al. (2017). I-131 biokinetics of remnant normal thyroid tissue and residual thyroid cancer in patients with differentiated thyroid cancer: comparison between recombinant human TSH administration and thyroid hormone withdrawal. Annals of Nuclear Medicine, 31(8), 582-589.
105.Huang, C. C., Lin, Y. H., Pan, L. K. et al. (2020). Biokinetic model of radioiodine I-131 in nine thyroid cancer patients subjected to in-vivo gamma camera scanning: A simplified five compartmental model. PLoS One, 15(5), e0233031.
106.Ghersi-Egea, J. F., & Strazielle, N. (2001). Brain drug delivery, drug metabolism, and multidrug resistance at the choroid plexus. Microscopy Research and Technique, 52(1), 83-88.
107.Pugh, R. N., Murray-Lyon, I. M., Dawson, J. L. et al. (1973). Transection of the oesophagus for bleeding oesophageal varices. British Journal of Surgery, 60, 646-649

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