Shyamkumar Sriram, Muayad Albadrani
Do hospitalizations push households into poverty in India: evidence from national data
F1000Research
modeling causality graphs referred causal graph learning offers appropriate description dynamics causality majority current machine learning models clinical decision support systems predict associations variables whereas causal graph learning models causality dynamics graphs however building personalized causal graphs individual challenging due limited amount data available patient study present new algorithmic framework using metalearning learning personalized causal graphs biomedicine framework extracts common patterns multiple patient graphs applies information develop individualized graphs multitask causal graph learning proposed optimized initial guess shared commonality enables rapid adoption knowledge new tasks efficient causal graph learning experiments one realworld biomedical causal graph learning benchmark data four synthetic benchmarks show algorithm outperformed baseline methods algorithm better understand underlying patterns data leading accurate predictions causal graph specifically reduce structural hamming distance 5075 indicating improvement graph prediction accuracy additionally false discovery rate decreased 2030 demonstrating algorithm made fewer incorrect predictions compared baseline algorithms best knowledge first study demonstrate effectiveness metalearning personalized causal graph learning cause inference modeling biomedicine addition proposed algorithm also generalized transnational research areas integrated analysis necessary various distributions datasets including different clinical institutions