Entity resolution for symptom vs disease for top-K treatments

The sufficient information on the web of data calls for an efficient entity resolution techniques in biomedical records, symptom vs. disease where a particular symptom, subjected to ambiguity. There may be several terms that refer to the same symptom. Thus, Entity Resolution becomes an essential task to identify a particular disease, for a given symptom. This work aims at suggesting the best alternate treatments to the health care professionals based on the patient's disease. A hybrid recommender system that recommends alternate treatments to the healthcare professionals based on their patient's disease, symptoms, age, and gender is designed and developed. Nowadays, there are new on-going treatments which are much successful know from the clinical trials for a particular disease. The content-based filtering, find the different treatments that are available for user's disease based on their outcome obtained by sentiment analysis. The collaborative filtering uses the similarity measure to find the similarity between the user and the patients by considering their age, gender, location, symptoms, and diseases. The treatments obtained from both these modules are then ranked by assigning a score based on their effectiveness and side effects. Finally, the top-k treatments for the disease are recommended to the health care professionals.

[1]  Andreas Thor,et al.  Multi-pass sorted neighborhood blocking with MapReduce , 2012, Computer Science - Research and Development.

[2]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[3]  Riccardo Miotto,et al.  A human-computer collaborative approach to identifying common data elements in clinical trial eligibility criteria , 2013, J. Biomed. Informatics.

[4]  Perry L. Miller,et al.  Application of Information Technology: Organization of Heterogeneous Scientific Data Using the EAV/CR Representation , 1999, J. Am. Medical Informatics Assoc..

[5]  G. Aghila,et al.  Text Mining Process, Techniques and Tools : an Overview , 2010 .

[6]  Gediminas Adomavicius,et al.  New Recommendation Techniques for Multicriteria Rating Systems , 2007, IEEE Intelligent Systems.

[7]  Tim Berners-Lee,et al.  Linked Data - The Story So Far , 2009, Int. J. Semantic Web Inf. Syst..

[8]  David W. Embley,et al.  Assessing clinical trial eligibility with logic expression queries , 2008, Data Knowl. Eng..

[9]  Soon Ae Chun,et al.  Patient-oriented clinical trials search through semantic integration of Linked Open Data , 2013, 2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing.

[10]  Chunhua Weng,et al.  Extracting temporal constraints from clinical research eligibility criteria using conditional random fields. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[11]  Edward Curry,et al.  Querying linked data graphs using semantic relatedness: A vocabulary independent approach , 2013, Data Knowl. Eng..

[12]  Jianzhong Li,et al.  Rule-Based Method for Entity Resolution , 2015, IEEE Transactions on Knowledge and Data Engineering.

[13]  K. A. Vidhya,et al.  Hybrid text mining model for document classification , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[14]  Huizhi Liang,et al.  Semantic-Aware Blocking for Entity Resolution , 2016, IEEE Trans. Knowl. Data Eng..

[15]  Zuhair Bandar,et al.  Sentence similarity based on semantic nets and corpus statistics , 2006, IEEE Transactions on Knowledge and Data Engineering.