Journal of the Thai Medical Informatics Association

Training and evaluation of predictive models for selecting intravenous antibiotics in sepsis patients: A retrospective data case study at Khu Muang Hospital

Sukumal Wannawijit

Sepsis is a critical condition that requires timely and effective antibiotic therapy to improve patient outcomes. With the complexity of patient presentations and the variety of available intravenous antibiotics, selecting the most appropriate treatment can be challenging. This study focuses on training and evaluating predictive models utilizing machine learning techniques to assist healthcare providers in selecting appropriate antibiotics for hospitalized sepsis patients. The objective of this research is to create a robust predictive model that leverages machine learning algorithms to identify the most suitable intravenous antibiotics based on individual patient characteristics and clinical presentations. This study was conducted at Khu Muang Hospital in Buriram, Thailand, involving a retrospective analysis of data from 190 adult patients who met the inclusion criteria and were diagnosed with sepsis between October 2022 and September 2024. The data were collected from medical records, including demographic and clinical characteristics such as age, sex, comorbidities, vital signs, laboratory test results and treatment outcomes. Several machine learning models, including Random Forest, XGBoost, Logistic Regression and Decision Tree were trained and evaluated for their predictive performance regarding antibiotic selection. The predictive models demonstrated promising performance metrics, with Random Forest, XGBoost, and Logistic Regression achieving identical accuracy and F1-scores of 0.95 and 0.92, respectively. Decision Tree showed the lowest performance accuracy and F1-scores of 0.89 and 0.86 respectively. After hyperparameter tuning, the Decision Tree model showed significant improvement by increasing its accuracy and F1-scores from 0.84 and 0.86 to 0.89. Overall, the models effectively identified key features influencing antibiotic efficacy, with age, White Blood Count and Neutrophil count being prioritized in the decision-making process. The study underscores the potential of machine learning models to enhance clinical decision-making in antibiotic selection for sepsis patients. While the models exhibited high predictive accuracy, limitations related to the dataset's size and diversity were identified. Future research should focus on obtaining larger, more representative datasets to further improve model robustness and applicability. The findings highlight the need for continued developments in predictive modeling to optimize treatment strategies for sepsis management.

References

C. Kim, Y. H. Choi, J. Y. Choi, H. J. Choi, R. W.Park, and S. J. Rhie, “Translation of machine learning-based prediction algorithms to personalised empiric antibiotic selection: A population-based cohort study, ”Int. J. Antimicrob. Agents, vol. 62, no. 5, p. 106966, 2023.

I. Poran et al., “Predicting in-hospital antibiotic use in the medical department: Derivation and validation study,” Antibiotics (Basel), vol. 11, no. 6, 2022.

C. Mistry et al., “Development and validation of a multivariable prediction model for infection-related complications in patients with common infections in UK primary care and the extent of risk-based prescribing of antibiotics,” BMC Med., vol. 18, no. 1, p. 118, 2020.

J. G. Wong, A.-H. Aung, W. Lian, D. C. Lye, C.-K. Ooi, and A. Chow, “Risk prediction models to guide antibiotic prescribing: a study on adult patients with

uncomplicated upper respiratory tract infections in an emergency department,” Antimicrob. Resist. Infect. Control, vol. 9, no. 1, 2020.

J. Qin et al., “Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation,” Int. J. Antimicrob. Agents, vol. 63, no. 5, p. 107122, 2024.

P. Theocharopoulos, S. Bersimis, S. V. Georgakopoulos, A. Karaminas, S. K. Tasoulis, and V. P. Plagianakos, “Developing predictive precision medicine models by exploiting real-world data using machine learning methods,” Journal of Applied Statistics, 2024. doi: 10.1080/02664763.2024.2315451.

B. Göksu, B. Berikol, and G. Berikol, “Predictive models in precision medicine,” pp. 177–188, 2020. doi: 10.1016/B978-0-12-817133-2.00007-0.

C.-H. Fang, V. Ravindra, S. Akhter, M. Adibuzzaman,

P. Griffin, S. Subramaniam, and A. Grama, “Identifying and analyzing sepsis states: A retrospective study on patients with sepsis in ICUs,” PLOS Digital Health, vol. 1, no. 11, p. e0000130, 2022. doi: 10.1371/ journal.pdig.0000130.

M. Lugon, “Prognostic and treatment effect modeling in medical research,” 2023. doi: 10.33540/1789.

D. Lamba, W. H. Hsu, and M. Alsadhan, “Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques,” pp. 1–35, 2021. doi: 10.1016/B978-0-12-821777-1.00023-9.

Development of an outpatient data warehouse system for Uthai Hospital

Somchok Klaithong — 2025-11-25

The objective of this study is to develop a health data warehouse and outpatient service database for Uthai Hospital in Phra Nakhon Si Ayutthaya Province. The hospital had been facing issues with delayed data analysis, inaccurate data that did not reflect the real situation, and data that did not meet user requirements, all of which hindered health planning and problem-solving efforts. The study will collect data from Uthai Hospital's outpatient service database for the fiscal years 2020-2023. The development process is divided into three phases: the first phase involves collecting data and questions from administrators and users; the second phase involves designing the structure and necessary tables for the data warehouse; and the third phase focuses on developing data analysis displays, such as charts and tables, to verify the completeness and accuracy of the data. QlikView Desktop, a powerful tool for quickly and easily visualizing various data dimensions, was used in this process.

The results of the development showed that the data warehouse system can fully display essential data with detailed insights, including trends in patient visits, medical expenses categorized by illness type, treatment rights, treatment departments, and ranked reports by diseases, departments, and service units. It also presents data dimensions such as time, gender, and disease groups. Moreover, the system can be further developed to present data in various formats based on user requirements, allowing analyzed data to be used in planning and solving public health issues in the local area.

References

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chilchil learning blogspot.(2559), “การพัฒนาคลัง ข้อมูล”,[ออนไลน์].ได้จาก:http://chilchil-learning.blog spot.com/ [สืบค้น 2 กรกฎาคม 2567]

RSU-MOOC.(2560), “หลักการออกแบบคลังข้อมูลแบบ Star Schema และ Snowflake Schema”, [ออนไลน์]. ได้จาก: http://tiprayong.blogspot.com/2017/09/ blog-post_28.html [สืบค้น 2 กรกฎาคม 2567]

กลยรตน ศรภทรพพธ. “คลังข้อมูลและระบบสนับสนุน การตัดสินใจส􀂷ำหรับการพัฒนาระบบสุขภาพใประเทศไทย”, จุฬาลงกรณ์มหาวิทยาลัย; 2562.

NIDA Wisdom.(2560), “ระบบธุรกิจอัจฉริยะ (Business Intelligence) กับการจัดการ Big data”, [ออนไลน์]. ได้จาก: https://www.mbamagazine.net/index.php/

school/b-school/item/626-business intelligence-big-data [สืบค้น 2 กรกฎาคม 2567]

Kvakusha.(2555), “ระบบธุรกิจเชิงวิเคราะห์ ระบบธุรกิจ อัจฉริยะคืออะไร ข้อดีข้อเสีย ของเทคโนโลยี”, [ออนไลน์]. ได้จาก:https://kvakusha.ru/th/analiticheskie sistemy-biznesa-chto-takoe-business-intelligence-plyusy-i.html [สืบค้น 2 กรกฎาคม 2567]

สำนักงานปลัดกระทรวงสาธารณสุข.(2558), “บทที่ 6 ระบบสุขภาพของประเทศไทย”, [ออนไลน์]. ได้จาก:http:// wops.moph.go.th/ops/thp/thp/userfiles/7_%20lesson6. pdf [สืบค้น 2 กรกฎาคม 2567]

Development of an information system for personnel management in nursing operations Ubon Ratchathani Cancer Hospital

Santi Mantong — 2025-11-25

This study aims to: 1. Examine the current situation of personnel management in the nursing department at Ubon Ratchathani Cancer Hospital. 2. Investigate the information system requirements for personnel management in the nursing department at Ubon Ratchathani Cancer Hospital. 3. Develop an information system for personnel management in the nursing department at Ubon Ratchathani Cancer Hospital. 4. Evaluate the effectiveness of the information system for personnel management in the nursing department at Ubon Ratchathani Cancer Hospital using the System Development Life Cycle (SDLC) principles. The target group consists of nursing personnel working at Ubon Ratchathani Cancer Hospital. The sample was selected using purposive sampling, including registered nurses, staff, and stakeholders involved in using the information system. The effectiveness of the system was evaluated by computer experts who are personnel from Ubon Ratchathani Cancer Hospital, holding a bachelor's degree and having at least three years of work experience, totaling five individuals. The research tool was the information system for personnel management in the nursing department at Ubon Ratchathani Cancer Hospital, along with an assessment form for the system's effectiveness. The statistical methods used for data analysis were mean and standard deviation.

The study found that the information system for personnel management in the nursing department at Ubon Ratchathani Cancer Hospital is functional. Nursing personnel at the hospital can access personnel data according to their needs. The system consists of ten components: 1) New personnel registration system, 2) System displaying all personnel information, 3) System displaying individual personnel information, 4) Login system, 5) System for adding general/personal information, 6) System for adding educational background information, 7) System for adding meeting/training information, 8) System for adding specialized training information, 9) System for adding license renewal information, and 10) Dashboard system summarizing the number/percentage. The overall evaluation of the system's effectiveness by experts was rated at a high level ( 4.23, SD = 0.63).

References

โรงพยาบาลมะเร็งอุบลราชธานี, “ประวัติโรงพยาบาลมะเร็ง อุบลราชธานี,” 2567. [ออนไลน์]. เข้าถึงได้จาก:http:// uboncancer.go.th/ubcc2016v2/abouts.php

ละอองดาว ทองดี, “ระบบสารสนเทศงานนวัตกรรมสิ่ง ประดิษฐ์งานสร้างสรรค์และงานวิจัยเพื่อการประกันคุณภาพ ภายในสถานศึกษา กรณีศึกษา วิทยาลัยการอาชีพ เชียงราย,” ปริญญานิพนธ์, มหาวิทยาลัยพะเยา, พะเยา, 2557.

วสันต์ เทวัญ, “การพัฒนาระบบสารสนเทศเพื่อการรับส่ง เอกสารภายในมหาวิทยาลัยเทคโนโลยีและการจัดการ กฟผ. แม่เมาะ,” ปริญญานิพนธ์, มหาวิทยาลัยพะเยา, พะเยา, 2557.

เกียรติพงษ์ อุดมธนะธีระ, “วงจรพัฒนาระบบ (System Development Life Cycle: SDLC),” 2562. [ออนไลน์]. เข้าถึงได้จาก: สำนักงานโลจิสติกส์

จารุกิตติ์ สายสิงห์, “การพัฒนาระบบจัดเก็บฐานข้อมูล บุคลากรทางการศึกษาแบบ 360 องศา,” คณะศึกษาศาสตร์, มหาวิทยาลัยภาคตะวันออกเฉียงเหนือ, 2563.

กัญญาภัทร จิตมาตย์, ศศิธร แสงจำรัสชัยกุล, และภิญญา สุขวิพัฒน์, “การพัฒนาระบบฐานข้อมูลศิษย์เก่าวิทยาลัย ธาตุพนม มหาวิทยาลัยนครพนม,” 2567.

มนต์ชัย เทียนทอง, *สถิติและวิธีการวิจัยทางเทคโนโลยี สารสนเทศ*. กรุงเทพฯ: สถาบันเทคโนโลยีพระจอมเกล้า พระนครเหนือ, 2548.

รณชัย ทิพย์มณฑา, “การพัฒนาระบบสารสนเทศเพื่อการ จัดการ สำหรับการวางแผนพัฒนาบุคลากร คณะเทคโนโลยีสารสนเทศและการสื่อสารมหาวิทยาลัยพะเยา,” 2565.

The analysis and evaluation of the patient transportation program

Chaita Sujinpram — 2025-11-25

This study aims to evaluate and analyze the performance of the Patient Transportation Program using retrospective data from May 2023 to September 2024. The findings revealed a total of 205,371 transportation requests, but after data cleansing to remove duplicates and system test entries, 194,071 valid requests remained, accounting for 94.54% of the total requests. The analysis identified several issues in recording timestamp data at various moments, including 82,730 instances (40.8%) of initiating transport before patient pickup and 7,708 instances (3.8%) of completing service before the start. Additionally, 3,859 instances (1.89%) of scheduling after order acceptance were noted, primarily due to delayed data entry and system inefficiencies.

The workload analysis showed that afternoon shifts had the highest workload, averaging 19 tasks per staff member per shift, followed by night shifts at 17 tasks and morning shifts at 16 tasks per staff member per shift. Regarding patient satisfaction, 97.1% of the feedback gave a score of 0, possibly due to the inconvenience of the feedback process. Simplifying the evaluation process through QR codes or mobile applications could improve response rates and service quality.

The use of real-time data entry technology and in-depth data analysis (Big Data Analytics) can significantly improve resource allocation and decision-making accuracy, enhancing the overall efficiency of the Patient Transportation Program.

References

S. W. Huang, S. Y. Chiou, and R. C. Chen, "Enhancing hospital efficiency and patient care: Real-time tracking and data-driven dispatch in patient transport," Sensors, vol. 24, no. 12, pp. 4020-4035, Jun. 2024.

Jelvix, "Best solutions for efficient medical data entry in real time," Jelvix, 2024.

Ambula Healthcare, "What is patient tracking software?," Ambula.io, 2024.

"Importance & Benefits of Real-Time Data Entry to the EHR System," PrognoCIS EHR, 2024.

M. Kadaei, "Optimizing patient transportation by applying cloud computing and big data analysis," The Journal of Supercomputing, vol. 63, no. 1, pp. 33-49, Mar. 2024.

D. Sobhy, Y. El-Sonbaty, and M. Abou Elnasr, "Med Cloud: healthcare cloud computing system," in Proc. 2012 Int. Conf. Internet Technol. Secured Trans., pp. 161-166, Dec. 2012.

F. Shahzad, "Modern and responsive mobile health care systems: Implementation and management," IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 4, pp. 572-583, Jul. 2023.

Nursing Times, "How real-time data improves patient care," Nursing Times, 2024.

Ambula Healthcare, "How patient satisfaction is improved with digital tools," Ambula.io, 2024.

Forecasting medicine sales using an ARIMA model

Chanon Jarupaktranonth — 2025-11-25

This study aims to apply the ARIMA (AutoRegressive Integrated Moving Average) model to predict the drug sales at Chulabhorn Hospital using monthly sales data of 200 consistently selling drugs from August 1, 2019, to August 31, 2024. The goal is to create a model that can accurately forecast future sales. The model's performance is evaluated using the MAPE (Mean Absolute Percentage Error) metric to measure prediction accuracy.

The research process begins by loading the monthly sales data, followed by testing for stationarity using the Augmented Dickey-Fuller (ADF) test. If the data is non-stationary, differencing is applied to correct it. The data is then split into a training set (80%) and a test set (20%). The auto ARIMA function is used to automatically build the model and select the most appropriate parameters. The model is evaluated using MAE, RMSE, and MAPE metrics.

The research results show that the average MAPE for the ARIMA model is 19.6%, with a maximum error of 33.80% and a minimum error of 6.13%. These results indicate that the ARIMA model provides an acceptable level of accuracy in predicting drug sales. However, some periods exhibit higher-than-normal error rates.

The recommendation from this study is to develop a hybrid model combining ARIMA and Neural Networks to improve accuracy by capturing both linear and non-linear trends simultaneously.

References

Box, G. E., Jenkins, G. M., & Reinsel, G. C. (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.

Rathipriya, R., Rahman, A. A. A., Dhamodharavadhani, S., Meero, A., & Yoganandan, G. (2022). "Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model," Neural Computing and Applications, Springer-Verlag London Ltd. DOI: 10.1007/s00521-022-07889- 9(521_2022_Article_7889).

Vongspanich, W. (2021). "Forecasting Medical Time Series Data Using the ARIMA Model," Siriraj Medical Bulletin, vol. 14, no. 4, pp. 38-49.

Rodríguez González, R., Abdul Rahman, A. A., & Aziz, A. (2021). Forecast of the demand for med ications by a pharmaceutical company. Procedia Computer Science, 179, 480-487. https://doi. org/10.1016/j.procs.2021.01.036

Ankita M., Srinivas, A., Soni, A., Prajapati, G., & Manjunath, P. S. (2024). "Pharmaceutical Sales Data Prediction Using Time Series Forecasting," Interna tional Journal of Intelligent Systems and Applications in Engineering, 12(13s), 681-696.

Dave, E., Leonardo, A., Jeanice, M., & Hanafiah, N. (2021). "Forecasting Indonesia Exports using a Hybrid Model ARIMA-LSTM," Procedia Computer Science, 179, 480-487

Predicting estimated glomerular filtration rate (eGFR) for chronic kidney disease risk assessment using machine learning techniques: A case study of Khu Mueang Hospital

Wannaporn Jaimeetham — 2025-11-25

Chronic Kidney Disease (CKD) is a significant health problem affecting populations worldwide. Analyzing and predicting the estimated glomerular filtration rate (eGFR) is essential for planning patient care at various stages, including primary prevention, secondary prevention, and tertiary prevention. In this study, we experimented with various machine learning models, including Linear Regression, Random Forest, Decision Tree, Gradient Boosting Machine (GBM), XGBoost, and LightGBM, to identify the factors influencing eGFR. We selected the most effective model based on performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R².The results demonstrated that the most effective model was Random Forest, with an MAE of 0.146688, an MSE of 0.260079, and an R2 of 0.999688, indicating the highest accuracy across all metrics. The R² value, being close to 1, reflects the model's ability to explain the variability in the data effectively. Furthermore, the Random Forest model was refined using Grid Search, resulting in optimal parameters. This model proved to be highly effective in predicting eGFR for patisnts with chronic kidney disease (CKD) with a low MSE and a high R² score, showcasing its capability for accurate outcome predictions. It can be utilized to assess the risk of CKD effectively, aiding in the planning and prevention of chronic kidney disease more efficiently.

References

B. P. Ghosh, T. Imam, N. Anjum, M. T. Mia, C. U. Siddiqua, K. Shaharia, Sharif, M. A. I. Mamun, M. Z. Hossain, Md Atikul, and I. Mamun, "Advancing Chronic Kidney Disease Prediction: Comparative Analysis of Machine Learning Algorithms and a Hybrid Model," Journal of Computer Science and Technology, pp. 989-993, Jul. 2024

C. Mbah, A. Johnson, L. Williams, and D. Smith, "Association between Chronic Kidney Disease and Cardiovascular Risk Factors in Diverse Populations," Journal of Nephrology Research, vol. 15, no. 2, pp. 123-134, 2023

D. Vanathi, S. M. Ramesh, K. Tamizharasu, S. N, and K. P, "A Machine Learning Perspective for Predicting Chronic Kidney Disease," Proceedings, pp. 989-993, Jul. 2024.

D. Xie et al., “Global burden and influencing factors of chronic kidney disease due to type 2 diabetes in adults aged 20-59 years, 1990-2019,” Dental Science Reports, vol. 13, no. 1, pp. 20234-20, Nov. 2023

E. Mahbub, R. K. Sarker, M. Hasan, and F. Ahmed, "Application of Machine Learning in Predicting Chronic Kidney Disease: A Comprehensive Study," Healthcare Informatics Research, vol. 28, no. 4, pp. 315-325, Dec. 2022

M. Tonelli, V. Nkunu, C. Varghese, A. K. Abu-Alfa, M. N. Alrukhaimi, L. Fox, J. Gill, D. C. H. Harris, F. F. Hou, P. J. O’Connell, H. U. Rashid, A. Niang, S. Ossareh, V. Tesar, E. Zakharova, and C.-W. Yang, “Framework for establishing integrated kidney care programs in low- and middle-income countries,” Kidney International Supplements, vol. 10, no. 1, pp. e19–e23, 2020.

M. Rane, M. Derkar, D. Kabra, and T. J. Desai, "Chronic Kidney Disease Prediction Using Machine Learning," International Journal for Research in Applied Science and Engineering Technology (IJRASET), vol. 12, no. 6, pp. 65-70, Jun. 2024

M. Forné, S. L. Pérez-García, J. M. Betriu, and L. Fernandez, "Cardiovascular Risk Assessment in Patients with Chronic Kidney Disease: A Compre hensive Review," Nephrology Dialysis Transplantation, vol. 34, no. 12, pp. 1915-1923, Dec. 2019.

N. A. Kikvadze and G. Gorgadze, “The role of environmental factors in the development of chronic kidney disease (CKD) – Literature Review,” Junior Researchers, vol. 7, no. 1, pp. 1-8, Jun. 2023.

T. Bai, Y. Zhang, L. Wang, and J. Liu, "Machine Learning Models for Predicting the Progression of Chronic Kidney Disease," Journal of Medical Systems, vol. 46, no. 11, pp. 1-10, Nov. 2022.

T. R. Noviandy, G. M. Idroes, M. Syukri, and R. Idroes, "Interpretable Machine Learning for Chronic Kidney Disease Diagnosis: A Gaussian Processes Approach," Indonesian Journal of Case Reports, vol. 2, no. 1, pp. 24-32, Jun. 2024.

U. O. Pontes, T. L. M. Amaral, C. A. Amaral, and G. T. R. Monteiro, "Prevalence and factors associated with chronic kidney disease in diabetic and hyper tensive patients," Environmental Health Perspectives, 2023

กมลทิพย์ วิจิตรสุนทรกุล, “แนวทางการดูแลและจัดการ ผู้ป่วยโรคไตเรื้อรัง,” กรมควบคุมโรค, กระทรวงสาธารณสุข, สืบค้นเมื่อ 30 กันยายน 2566. [ออนไลน์]. เข้าถึงได้จาก: https://ddc.moph.go.th/uploads/pulish/13088202209050 25852.pdf

Development clinical decision support system to enhance accuracy in diagnosis and treatment of stroke in emergency patients

Apisak Sutanon — 2025-11-25

Objective: To develop and evaluate the effectiveness of a Clinical Decision Support System (CDSS) for diagnosing and treating stroke patients in the emergency department.

Methods: A quasi-experimental one-group pretest-posttest study was conducted. The CDSS was developed using Decision Trees algorithm, trained on retrospective patient data from 2021-2023 (n=300). The dataset was split into 70% training, 20% validation, and 10% testing sets. A pilot study was conducted with 30 patients.

Results: The developed model achieved 92.5% diagnostic accuracy, 91.8% precision, 93.2% recall, and 92.5% F1-score. After implementation, the mean door-to-needle time significantly decreased from 49.46 to 37 minutes (25.19% reduction, p<0.05). Physician satisfaction with the system was 95%, and no incidents of misdiagnosis or medication complications were reported.

Conclusion: The developed CDSS significantly improved the efficiency of stroke diagnosis and treatment in the emergency department by reducing treatment time and enhancing physician confidence in decision-making.

References

J. Warner, R. A. Harrington, R. L. Sacco, and M. S. V. Elkind, “Guidelines for the Early Management of Patients With Acute Ischemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke.,” vol. 50, no. 12, 2019, doi: 10.1161/STROKEAHA.119.027708.

E. H. Shortliffe and M. J. Sepúlveda, “Clinical decision support in the era of artificial intelligence,” Jama, vol. 320, no. 21, pp. 2199–2200, 2018.K. Elissa, “Title of paper if known,” unpublished.

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Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017.

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E. S. Berner, “Clinical decision support systems,” Springer Science+ Business Media, LLC, 2007.

J. A. Osheroff, J. M. Teich, B. Middleton, E. B. Steen, A. Wright, and D. E. Detmer, “A roadmap for national action on clinical decision support,” Journal of the American medical informatics association, vol. 14, no. 2, pp. 141–145, 2007.

Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005). Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. Bmj, 330(7494), 765.

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Bernhardt, J., Hayward, K. S., Kwakkel, G., Ward, N. S., Wolf, S. L., Borschmann, K., ... & Cramer, S. C. (2017). Agreed definitions and a shared vision for new standards in stroke recovery research the stroke recovery and rehabilitation roundtable taskforce. International Journal of Stroke, 12(5), 444-450

Automated health check-up interpretation and advice system via web application using rule-based method

Kittiphop Jamsophon — 2025-11-25

This study aims to develop an automated health check-up result interpretation and recommendation system via a web application using a Rule-based method. The system is designed to provide accurate interpretations of health check-up results and recommendations. The system was tested using health data from 395 hospital staffs. The results showed that the system performed with the highest accuracy in urine (UA) and fat interpretation, achieving 100% accuracy. The system also performed well in blood sugar interpretation with 99% accuracy, while liver interpretation reached 93%. However, the system failed to detect abnormalities in kidney interpretation, with a sensitivity of 0%, indicating the need for improvement. This study demonstrates the potential of Rule-Based Engines in reducing the workload of healthcare professionals and enhancing convenience in health monitoring, but further refinements are needed to improve detection in certain areas.

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Evaluating a machine learning models for predicting full recovery in stroke: A case study at Hatyai Hospital

Nisjara Kunaton — 2025-11-25

Stroke is a leading cause of death and disability worldwide, making the prediction of patient recovery crucial for treatment planning and rehabilitation. This study investigates the application of Machine Learning (ML) techniques to predict stroke patient recovery, using data from 6,081 cases at Hatyai Hospital. In the design and execution of the experiment, four ML models were utilized: Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting. The data preparation process involved label encoding, handling missing values, and balancing the dataset. Model performance was evaluated using K-Fold Cross-Validation and Hyperparameter Tuning. Results show that Random Forest performed the best, achieving an accuracy of 88.61%, with both Precision and Recall at 0.91, and an F1-Score of 0.91. Gradient Boosting followed closely with an accuracy of 88.50%. Logistic Regression and Decision Tree showed lower performance, with accuracies of 87.08% and 83.83%, respectively. The study demonstrates that ML techniques, particularly Random Forest and Gradient Boosting, offer high accuracy in predicting stroke recovery, providing valuable insights for efficient treatment planning.

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