Non-communicable diseases (NCDs) are among the leading causes of deaths worldwide. Early detection and management could mitigate NCD-related complications. The Philippine Package for Essential NCD Interventions (PhilPEN) is a set of protocols to identify the risk level of developing NCD among at-risk patients and recommend an action plan based on a clinical service pathway. This paper proposes a framework in building a rule-based decision support system for risk assessment and management of NCDs following the PhilPEN and a decision tree derived from the risk prediction chart. It is demonstrated through a prototype application called "BlockNCD App". BlockNCD App maintains a registry of enrolled clients for risk assessment and screening. Variables such as age, gender, lifestyle and laboratory results are processed by the application and calculates the NCD risk level of the client. Depending on the NCD risk level, BlockNCD App recommends appropriate medical intervention. The use of an automated decision support systems can help in managing NCD cases better through early intervention and treatment and by minimizing errors that arise from manual interpretation of risk prediction charts and clinical pathways.
{"title":"BlockNCD App: A Rule-Based Decision Support System for Non-Communicable Disease Risk Assessment","authors":"A. O. Perez, T. Palaoag","doi":"10.1145/3456172.3456213","DOIUrl":"https://doi.org/10.1145/3456172.3456213","url":null,"abstract":"Non-communicable diseases (NCDs) are among the leading causes of deaths worldwide. Early detection and management could mitigate NCD-related complications. The Philippine Package for Essential NCD Interventions (PhilPEN) is a set of protocols to identify the risk level of developing NCD among at-risk patients and recommend an action plan based on a clinical service pathway. This paper proposes a framework in building a rule-based decision support system for risk assessment and management of NCDs following the PhilPEN and a decision tree derived from the risk prediction chart. It is demonstrated through a prototype application called \"BlockNCD App\". BlockNCD App maintains a registry of enrolled clients for risk assessment and screening. Variables such as age, gender, lifestyle and laboratory results are processed by the application and calculates the NCD risk level of the client. Depending on the NCD risk level, BlockNCD App recommends appropriate medical intervention. The use of an automated decision support systems can help in managing NCD cases better through early intervention and treatment and by minimizing errors that arise from manual interpretation of risk prediction charts and clinical pathways.","PeriodicalId":149574,"journal":{"name":"International Conferences on Computing and Data Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123218612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When using prediction models created from data, it is in certain cases not sufficient for the users to only get a prediction, sometimes accompanied with a probability of the predictive outcome. Instead, a more elaborate answer is required, like given the predictive outcome, how can this outcome be changed to a wished outcome, i.e., feature tweaking. In this paper we introduce a novel hybrid method for performing feature tweaking that builds upon Random Forest Similarity Tweaking and utilizing a Constraint Logic Programming solver for the Finite Domain (CLPFD). This hybrid method is compared to only using a CLPFD solver and to using a previously known feature tweaking algorithm, Actionable Feature Tweaking. The results show that the hybrid method provides a good balance between the distances, comparing the original example and the tweaked example, and completeness, the number of successfully tweaked examples, compared to the other methods. Another benefit with the novel method, is that the user can specify a prediction threshold for feature tweaking and adjust weights of features to mimic the real-world cost of changing feature values.
{"title":"Hybrid feature tweaking: Combining random forest similarity tweaking with CLPFD","authors":"Tony Lindgren","doi":"10.1145/3456172.3456193","DOIUrl":"https://doi.org/10.1145/3456172.3456193","url":null,"abstract":"When using prediction models created from data, it is in certain cases not sufficient for the users to only get a prediction, sometimes accompanied with a probability of the predictive outcome. Instead, a more elaborate answer is required, like given the predictive outcome, how can this outcome be changed to a wished outcome, i.e., feature tweaking. In this paper we introduce a novel hybrid method for performing feature tweaking that builds upon Random Forest Similarity Tweaking and utilizing a Constraint Logic Programming solver for the Finite Domain (CLPFD). This hybrid method is compared to only using a CLPFD solver and to using a previously known feature tweaking algorithm, Actionable Feature Tweaking. The results show that the hybrid method provides a good balance between the distances, comparing the original example and the tweaked example, and completeness, the number of successfully tweaked examples, compared to the other methods. Another benefit with the novel method, is that the user can specify a prediction threshold for feature tweaking and adjust weights of features to mimic the real-world cost of changing feature values.","PeriodicalId":149574,"journal":{"name":"International Conferences on Computing and Data Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125433335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract: Python Programming Learning Assistant System (PYPLAS) has been developed to support python programming educations. Currently, PYPLAS provided the element fill-in-blank problem to improve the student programming skill. Students studied python error types and debugging technique in python code. In this paper, error debugging problem in PYPLAS is proposed so that students can study the types of python errors. Error debugging problem is generated by using error debugging method to replace incorrect element into correct ones. For evaluations, the 100 error debugging problems are generated to analysis the correctness of error debugging method according to python grammar. We generated 10 problems and asked 5 students in two universities to solve them. Eventually, the educational effects in python programming learning are verified by generating error debugging problems to assign the students in python programming course.
{"title":"Error Debugging Problem in Python Programming Learning Assistant System using Error Debugging Method extension Blank Element Selection Algorithms","authors":"Hsu Wai Hnin, Khin Khin Zaw","doi":"10.1145/3512850.3512858","DOIUrl":"https://doi.org/10.1145/3512850.3512858","url":null,"abstract":"Abstract: Python Programming Learning Assistant System (PYPLAS) has been developed to support python programming educations. Currently, PYPLAS provided the element fill-in-blank problem to improve the student programming skill. Students studied python error types and debugging technique in python code. In this paper, error debugging problem in PYPLAS is proposed so that students can study the types of python errors. Error debugging problem is generated by using error debugging method to replace incorrect element into correct ones. For evaluations, the 100 error debugging problems are generated to analysis the correctness of error debugging method according to python grammar. We generated 10 problems and asked 5 students in two universities to solve them. Eventually, the educational effects in python programming learning are verified by generating error debugging problems to assign the students in python programming course.","PeriodicalId":149574,"journal":{"name":"International Conferences on Computing and Data Engineering","volume":"516 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116227733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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