# Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review
## 3 Results
### How Does the Data Harmonization Resolve the Issues of Heterogeneity?
[PERSON] [54] mentioned that the disparate data are generated in unstructured formats, such as sensors and text, which describe heterogeneous behavior. For this reason, a data integration model was developed to solve the technical and quality problems of BDA. The model was developed using ML and DL techniques so that BD analysts could visualize, analyze, and make decisions from disparate data. Additionally, [PERSON] et al., generated a tool for clinical data in a heterogeneous form and for integration of data, an ontology-based tool suggested to arrange data in a structured format. Moreover, patient cohort and biomedical data play an important role for previous health treatmentand analysis, and data provided by patients in a heterogeneous structure need to be harmonized, as argued by [PERSON] et al. [11], so that, in an online tool, all patient data are available to medical staff during analysis. In this survey, different cohort harmonization techniques were highlighted, which will help in healthcare applications, such as ML, DL, and Ontology techniques. In addition, in an urban town, so many issues related to basic needs was mentioned by [PERSON] et al. [55]; the objective of that study was to make the urban town into a smart urban town. Data are generated by different departments in JSON, string, and maps. To make smart decisions, all data must be integrated. Furthermore, the patient stays in hospital data with different codes were not publicly available to make an health records into an EHR reported by authors ([PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [56]. By using Naive base and Random Forest on the UZA dataset, the patient classification was performed. Similarly, the researchers [PERSON] et al. [57], in their study, stated that the web-portal-based patient data produced by many healthcare hospitals in different formats were difficult to decide due to decentralization. To solve this issue, an automated and centralized web portal was developed which helps with online decisions. In contrast, the research team of [PERSON], [PERSON], [PERSON], et al. [58] analyzed that the patients with obesity and comorbidities were monitored after discharge from hospitals. The objective of this study is to develop a patient-centric system for FHIR using NLP toolkits and ML algorithms from the Mayo Clinic, MIMIC III, and i2b2 datasets. The overall performance of this system is measured in precision, recall, and F-Score. In addition, the same authors, [PERSON] et al. [59] proposed a model for the quality and performance-based data integration for information extraction, using NLP, ML, and Bag of Words (BoW). Moreover, [PERSON] et al. [60] used a Mayo Clinic dataset with the help of NLP toolkits for making a digital FHIR system. In contrast, [PERSON], [PERSON], [PERSON], and [PERSON] [61] conducted a review and suggested a unified model for SSU data, using MapReduce. Besides that, XML-based OGOLOD datasets were accessed by using ontology tools for a semantic oriented data harmonization model that was presented by [PERSON], [PERSON], [PERSON], and [PERSON] [62]. In Saudi Arabia, patient health data generated in public and private hospitals are not shared and integrated with the health information system due to a lack of heterogeneity. Therefore, the [PERSON], [PERSON], and [PERSON] [63] team proposed a NLP and BDA-based systems. Lastly, online FHIR-based web portals were developed by using NLP techniques and open-source tools on the Mayo Clinic dataset to centralize the data generated in a heterogeneous format that was revealed by the researchers [PERSON], [PERSON], [PERSON], et al. [64]. The contributions of all studies in all domains are discussed in Table 7.