臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：利用機器學習及統計方法分析醫療相關資料集以躁鬱症及登革熱為例
作者：林芳羽
作者(外文)：LIN, FANG-YU
校院名稱：輔仁大學
系所名稱：商學研究所博士班
指導教授：盧浩鈞
葉承達
學位類別：博士
出版日期：2023
主題關鍵詞：躁鬱症；登革熱；機器學習；支援向量機；支援向量回歸；決策樹；羅吉斯迴歸；Bipolar Disorder；Dengue Fever；Machine Learning；Support Vector Machine；Support Vector Regression；Decision Tree；Logistic Regression
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:2
根據聯合國發布之永續發展目標-健康促進與福祉，本研究希望藉由機器學習及統計方法，分別從不同領域挑選出兩種不一樣類別的資料集進行分析，一是與心理健康有關的躁鬱症，本研究欲建立一預測模型，希望能讓躁鬱症患者可以提前預防躁鬱症的發作，若能依躁鬱症患者就診歷史紀錄來有效預測躁鬱症的發作日期範圍，對永續發展目標的降低死亡率及加強疾病預防能力來說，將會是非常大的貢獻。
首先先將躁鬱症資料進行預處理，將資料型態轉換成時間序列集，再使用特徵選取，找出躁鬱症患者發病的預測因子，利用找出來的預測因子搭配支持向量機進行預測躁鬱症患者之發作時間範圍，最後再建立決策樹模型來判斷未來其他患者是否適用於本研究所建構出之預測模型。
在本研究最後可分析的87位樣本中，男性樣本有25位，女性有62位，而本研究所提出的SVM預測模型可準確預測日期誤差在7天內的男性樣本有16位(64%)，女性樣本則有39位(62.9%)，從上述研究結果來看，根據躁鬱症患者住院資料來預測其發病日期是可行的。
二是與傳染疾病相關的登革熱，本研究以羅吉斯迴歸作為機器學習之演算法，從空氣汙染、紫外線及氣象等變項找出登革熱發病率之影響因子，本研究目的欲探討在全球暖化、空氣汙染與生態環境三者之間密不可分的關係下，登革熱之發病率除了會受氣候因素影響之外，空氣汙染物是否亦會對發病率造成影響。
本研究結果發現，影響登革熱發病率之影響因子包含月份、SO2平均值、O3最小值、NO2最大值、氣溫最小值、最大陣風風向平均值、降水時數平均值，這些因子與發病率皆有顯著正相關關係，而PM10最大值、SO2最小值、紫外線最大值、相對溼度平均值與發病率亦有顯著負相關關係。
According to the United Nations' Sustainable Development Goals – Ensure healthy lives and promote well-being for all at all ages, this study aims to utilize machine learning and statistical methods to analyze two different datasets from various fields. The first dataset is related to mental health - Bipolar Disorder. The research intends to establish a predictive model to enable early prevention of bipolar disorder episodes. If the model can effectively predict the range of dates for bipolar disorder onset based on the patient's medical history, it would make a significant contribution to achieving the Sustainable Development Goals by reducing mortality rates and enhancing disease prevention capabilities.
The first step is preprocess the bipolar disorder data by converting the data type into a time series dataset. Then, utilize feature selection to identify predictive factors for the onset of bipolar disorder in subjects. Use the identified predictive factors in conjunction with support vector machines to predict the time range of bipolar disorder episodes in subjects. Finally, establish a decision tree model to determine whether the constructed predictive model is applicable to future patients.
Among the 87 subjects analyzed in this study, there were 25 male subjects and 62 female subjects. The SVM predictive model proposed in this research accurately predicted the date within a 7-day error for 16 male subjects (64%) and 39 female subjects (62.9%). Based on the above research findings, it is feasible to predict the onset date of bipolar disorder subjects using their hospitalization data.
The second dataset is related to the infectious disease - Dengue Fever. In this study, logistic regression was used as the machine learning algorithm to identify the influencing factors of dengue fever incidence from variables such as air pollution, ultraviolet radiation, and meteorological factors. The research aims to investigate the interconnections among global warming, air pollution, and ecological environment, and to explore whether air pollutants can also affect the incidence of dengue fever in addition to climate factors.
The results of this study found that the influencing factors of dengue fever incidence include the month, average SO2, minimum O3, maximum NO2, minimum temperature, average wind direction of maximum gust, and average duration of precipitation. These factors showed significant positive correlations with the incidence of dengue fever. On the other hand, maximum PM10, minimum SO2, maximum ultraviolet radiation level, and average relative humidity showed significant negative correlations with the incidence of dengue fever.
1.Adnan, S., Hanif, M., Khan, A. H., Latif, M., Ullah, K., Bashir, F., . . . Haider, S. (2021). Impact of heat index and ultraviolet index on COVID-19 in major cities of Pakistan. Journal of occupational and environmental medicine, 63(2), 98.
2.Aldinger, F., & Schulze, T. G. (2017). Environmental factors, life events, and trauma in the course of bipolar disorder. Psychiatry and Clinical Neurosciences, 71(1), 6-17. https://doi.org/10.1111/pcn.12433
3.Altman, S., Haeri, S., Cohen, L. J., Ten, A., Barron, E., Galynker, I. I., & Duhamel, K. N. (2006). Predictors of relapse in bipolar disorder: a review. Journal of Psychiatric Practice®, 12(5), 269-282.
4.Arnold, L. M. (2003). Gender differences in bipolar disorder. Psychiatric Clinics of North America, 26(3), 595-620.
5.Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345-370.
6.Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. Lancet, 360(9341), 1233-1242.
7.Calabrese, J. R., Keck, P. E., Macfadden, W., Minkwitz, M., Ketter, T. A., Weisler, R. H., . . . Grp, B. S. (2005). A randomized, double-blind, placebo-controlled trial of quetiapine in the treatment of bipolar I or II depression. American Journal of Psychiatry, 162(7), 1351-1360.
8.Caraka, R. E., Chen, R. C., Lee, Y., Gio, P. U., Budiarto, A., & Pardamean, B. (2021). Latent Regression and Ordination Risk of Infectious Disease and Climate. Procedia Computer Science, 179, 25-32. https://doi.org/https://doi.org/10.1016/j.procs.2020.12.004
9.Cardno, A. G., & Owen, M. J. (2014). Genetic relationships between schizophrenia, bipolar disorder, and schizoaffective disorder. Schizophrenia Bulletin, 40(3), 504-515.
10.Choi, Y., Tang, C. S., McIver, L., Hashizume, M., Chan, V., Abeyasinghe, R. R., . . . Huy, R. (2016). Effects of weather factors on dengue fever incidence and implications for interventions in Cambodia. BMC Public Health, 16(1).
11.Claude, L. A., Houenou, J., Duchesnay, E., & Favre, P. (2020). Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions. Bipolar Disorders, 22(4), 334-355.
12.Coumou, D., & Rahmstorf, S. (2012). A decade of weather extremes. Nature climate change, 2(7), 491-496.
13.Couret, J., & Benedict, M. Q. (2014). A meta-analysis of the factors influencing development rate variation in Aedes aegypti (Diptera: Culicidae). BMC Ecology, 14(1), 3. https://doi.org/10.1186/1472-6785-14-3
14.Craddock, N., & Sklar, P. (2013). Genetics of bipolar disorder. Lancet, 381(9878), 1654-1662.
15.De Peri, L., Crescini, A., Deste, G., Fusar-Poli, P., Sacchetti, E., & Vita, A. (2012). Brain structural abnormalities at the onset of schizophrenia and bipolar disorder: a meta-analysis of controlled magnetic resonance imaging studies. Current pharmaceutical design, 18(4), 486-494.
16.Fung, G., Deng, Y., Zhao, Q., Li, Z., Qu, M., Li, K., . . . Chan, R. C. K. (2015). Distinguishing bipolar and major depressive disorders by brain structural morphometry: a pilot study. BMC Psychiatry, 15(1).
17.Geddes, J. R., Burgess, S., Hawton, K., Jamison, K., & Goodwin, G. M. (2004). Long-term lithium therapy for bipolar disorder: Systematic review and meta-analysis of randomized controlled trialls. American Journal of Psychiatry, 161(2), 217-222.
18.Geddes, J. R., & Miklowitz, D. J. (2013). Treatment of bipolar disorder. Lancet, 381(9878), 1672-1682.
19.Gideon, J., Provost, E. M., & McInnis, M. (2016, 2016). Mood state prediction from speech of varying acoustic quality for individuals with bipolar disorder.
20.Goldstein, T. R., Merranko, J., Hafeman, D., Gill, M. K., Liao, F. Z., Sewall, C., . . . Birmaher, B. (2022). A risk calculator to predict suicide attempts among individuals with early-onset bipolar disorder. Bipolar Disorders, 24(7), 749-757.
21.Gordovez, F. J. A., & McMahon, F. J. (2020). The genetics of bipolar disorder. Molecular Psychiatry, 25(3), 544-559.
22.Hii, Y. L., Zhu, H. P., Ng, N., Ng, L. C., & Rocklov, J. (2012). Forecast of dengue incidence using temperature and rainfall. Plos Neglected Tropical Diseases, 6(11).
23.Izonin, I., Tkachenko, R., Gregus, M., Zub, K., & Lotoshynska, N. (2021). Input Doubling Method based on SVR with RBF kernel in Clinical Practice: Focus on Small Data. Procedia Computer Science, 184, 606-613. https://doi.org/https://doi.org/10.1016/j.procs.2021.03.075
24.Izonin, I., Tkachenko, R., Horbal, N., Greguš, M., Verhun, V., & Tolstyak, Y. (2022, 2022//). An Approach Toward Numerical Data Augmentation and Regression Modeling Using Polynomial-Kernel-Based SVR. Proceedings of International Conference on Data Science and Applications, Singapore.
25.Izonin, I., Tkachenko, R., Shakhovska, N., & Lotoshynska, N. (2021). The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach. Symmetry, 13(4), 612. https://doi.org/10.3390/sym13040612
26.J. Ring, M., Lindner, D., F. Cross, E., & E. Schlesinger, M. (2012). Causes of the global warming observed since the 19th century. Atmospheric and Climate Sciences, 02(04), 401-415.
27.Jan, Z., Ai-Ansari, N., Mousa, O., Abd-Alrazaq, A., Ahmed, A., Alam, T., & Househ, M. (2021). The role of machine learning in diagnosing bipolar disorder: scoping review. Journal of Medical Internet Research, 23(11), e29749.
28.Jie, N.-F., Zhu, M.-H., Ma, X.-Y., Osuch, E. A., Wammes, M., Theberge, J., . . . Calhoun, V. D. (2015). Discriminating bipolar disorder from major depression based on SVM-FoBa: efficient feature selection with multimodal brain imaging data. IEEE Transactions on Autonomous Mental Development, 7(4), 320-331.
29.Kampa, M., & Castanas, E. (2008). Human health effects of air pollution. Environmental Pollution, 151(2), 362-367.
30.Kessler, R. (1993). Sex and depression in the national comorbidity survey I: lifetime prevalence, chronicity and recurrence. Journal of Affective Disorders, 29(2-3), 85-96.
31.Kleindienst, N., Engel, R. R., & Greil, W. (2005). Which clinical factors predict response to prophylactic lithium? A systematic review for bipolar disorders. Bipolar Disorders, 7(5), 404-417.
32.Kularatne, S. A. M. (2015). Dengue fever. Bmj-British Medical Journal, 351.
33.Kupfer, D. J. (2005). The increasing medical burden in bipolar disorder. Jama-Journal of the American Medical Association, 293(20), 2528-2530.
34.Kurane, I. (2010). The effect of global warming on infectious diseases. Osong public health and research perspectives, 1(1), 4-9.
35.Kurt, O. K., Zhang, J. J., & Pinkerton, K. E. (2016). Pulmonary health effects of air pollution. Current Opinion in Pulmonary Medicine, 22(2), 138-143.
36.Lai, Y. H. (2018). The climatic factors affecting dengue fever outbreaks in southern Taiwan: an application of symbolic data analysis. Biomedical Engineering Online, 17.
37.Landrigan, P. J. (2017). Air pollution and health. Lancet Public Health, 2(1), E4-E5.
38.Lega, J., Brown, H. E., & Barrera, R. (2017). Aedes aegypti (Diptera: Culicidae) abundance model improved with relative humidity and precipitation-driven egg hatching. Journal of medical entomology, 54(5), 1375-1384.
39.Li, H., Cui, L., Cao, L., Zhang, Y., Liu, Y., Deng, W., & Zhou, W. (2020). Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques. BMC Psychiatry, 20(1).
40.Li, Y., Dou, Q., Lu, Y., Xiang, H., Yu, X., & Liu, S. (2020). Effects of ambient temperature and precipitation on the risk of dengue fever: A systematic review and updated meta-analysis. Environmental Research, 191, 110043.
41.Lin, C.-C., Lin, Y.-S., Huang, Y.-H., Liu, H.-S., Wu, H.-S., Yeh, T.-M., . . . Shu, P.-Y. (2010). Characteristic of dengue disease in Taiwan: 2002–2007. The American Journal of Tropical Medicine and Hygiene, 82(4), 731-739.
42.Lin, C. S., Liou, F. M., & Huang, C. P. (2011). Grey forecasting model for CO2 emissions: A Taiwan study. Applied Energy, 88(11), 3816-3820.
43.Lo, W. C., Shie, R. H., Chan, C. C., & Lin, H. H. (2017). Burden of disease attributable to ambient fine particulate matter exposure in Taiwan. Journal of the Formosan Medical Association, 116(1), 32-40.
44.Morin, C. W., Comrie, A. C., & Ernst, K. (2013). Climate and dengue transmission: evidence and implications. Environmental Health Perspectives, 121(11-12), 1264-1272.
45.Mullins, N., Forstner, A. J., O'Connell, K. S., Coombes, B., Coleman, J. R. I., Qiao, Z., . . . Psychiat, H. A.-I. (2021). Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nature Genetics, 53(6), 817-829.
46.Nakhapakorn, K., & Tripathi, N. K. (2005). An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. International journal of health geographics, 4(1), 1-13.
47.Patz, J. A., Epstein, P. R., Burke, T. A., & Balbus, J. M. (1996). Global climate change and emerging infectious diseases. Jama, 275(3), 217-223.
48.Plans, L., Barrot, C., Nieto, E., Rios, J., Schulze, T. G., Papiol, S., . . . Benabarre, A. (2019). Association between completed suicide and bipolar disorder: A systematic review of the literature. Journal of Affective Disorders, 242, 111-122.
49.Quilty, L. C., Sellbom, M., Tackett, J. L., & Bagby, R. M. (2009). Personality trait predictors of bipolar disorder symptoms. Psychiatry Research, 169(2), 159-163.
50.Rossati, A. (2017). Global warming and its health impact. The international journal of occupational and environmental medicine, 8(1), 7.
51.Sklar, P., Ripke, S., Scott, L. J., Andreassen, O. A., Cichon, S., Craddock, N., . . . D, P. G. C. B. (2011). Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nature Genetics, 43(10), 977-U162.
52.Strakowski, S. M., DelBello, M. P., Fleck, D. E., & Arndt, S. (2000). The impact of substance abuse on the course of bipolar disorder. Biological Psychiatry, 48(6), 477-485. https://doi.org/https://doi.org/10.1016/S0006-3223(00)00900-8
53.Thase, M. E., Macfadden, W., Weisler, R. H., Chang, W., Paulsson, B., Khan, A., & Calabrese, J. R. (2006). Efficacy of quetiapine monotherapy in bipolar I and II depression - A double-blind, placebo-controlled study (The BOLDER II study). Journal of Clinical Psychopharmacology, 26(6), 600-609.
54.Troyo, A., Fuller, D. O., Calderón-Arguedas, O., Solano, M. E., & Beier, J. C. (2009). Urban structure and dengue incidence in Puntarenas, Costa Rica. Singapore Journal of Tropical Geography, 30(2), 265-282.
55.Tsai, P.-J., & Teng, H.-J. (2016). Role of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in local dengue epidemics in Taiwan. BMC Infectious Diseases, 16(1).
56.Valdez, L. D., Sibona, G. J., & Condat, C. A. (2018). Impact of rainfall on Aedes aegypti populations. Ecological Modelling, 385, 96-105. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2018.07.003
57.Wiwanitkit, V. (2010). Dengue fever: diagnosis and treatment. Expert Review of Anti-Infective Therapy, 8(7), 841-845.
58.Wu, P. C., Guo, H. R., Lung, S. C., Lin, C. Y., & Su, H. J. (2007). Weather as an effective predictor for occurrence of dengue fever in Taiwan. Acta Tropica, 103(1), 50-57.
59.Wu, X. C., Lang, L. L., Ma, W. J., Song, T., Kang, M., He, J. F., . . . Ling, L. (2018). Non-linear effects of mean temperature and relative humidity on dengue incidence in Guangzhou, China. Science of the Total Environment, 628-629, 766-771.
60.Yeh, C.-Y., Chen, P.-L., Chuang, K.-T., Shu, Y.-C., Chien, Y.-W., Perng, G. C., . . . Ko, N.-Y. (2017). Symptoms associated with adverse dengue fever prognoses at the time of reporting in the 2015 dengue outbreak in Taiwan. Plos Neglected Tropical Diseases, 11(12), e0006091.
61.鄒秉諺, 林藍萍, 徐尚為, & 林金定. (2016). 台灣慢性精神障礙盛行率長期變化趨勢分析：2000-2014年 [Long-term Prevalence Trend Analysis of Taiwanese Chronic Mental Disability based on Governmental Disability Register]. 身心障礙研究季刊, 14(1), 75-85.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：778（臺灣：778，外國：0） ／ 本年度總使用人次：1885225 ／ 訪客人次（自102年09月）：67684024
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上