A disease diagnosis and treatment recommendation system based on big data mining and cloud computing

Abstract

It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and cooperative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in this paper. First, to effectively identify disease symptoms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm separately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. Moreover, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experimental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately.

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

“…Subsequently, ensuring the privacy of a patient's information plays a vital role in clinical research. In the proposed approach, the integrity of this information will be maintained while personal identity is effectively shielded [17,[29][30][31][32][33].…”

“…For the success of the recommender system, it is very important to choose what type of criteria are used to evaluate the recommender system. Conventionally, recommender systems were evaluated based on criteria borrowed from information retrieval [9,31,36]. Common metrics used in the evaluation are:…”

A survey on semanticized and personalized health recommender systems

“…Chen et al () proposed a disease diagnosis and treatment recommendation system (DDTRS) based on big data mining and cloud computing. The researchers used massive historical medical inspection datasets to derive disease‐symptom clusters aiming to discover association relationships among diseases, diagnoses, and treatments.…”

Peer Review #1 of "DISNET: a framework for extracting phenotypic disease information from public sources (v0.1)"

“…In this context, it is important to relate the quantity of available medical sources and systems on one hand, and the need of health professionals for quality information on the other, helping them performing their work with higher precision and lower time (Russell-Rose, Chamberlain & Azzopardi, 2018). Therefore, diagnostic systems (Chen et al, 2018) have become more relevant and researchers such as Xia et al attempt to take on the challenge through the mining of information from sources such as DO, Symptom Ontology (SYMP) and MEDLINE/PubMed citation records (Xia et al, 2018). We can also observe in the literature a large volume of studies that use the mining of texts from different unstructured or semi-structured medical information sources (Frunza, Inkpen & Tran, 2011;Mazumder et al, 2016;Singhal, Simmons & Lu, 2016;Xu et al, 2016;Tsumoto et al, 2017;Sudeshna, Bhanumathi & Hamlin, 2017;Aich et al, 2017;Gupta et al, 2018;Rao & Rao, 2018;Zhao et al, 2018;Bou Rjeily et al, 2019).…”