Goran Nenadic
Biographic Data
| ID | 7522543 |
|---|---|
| NAME | Goran Nenadic |
| GIVEN NAMES | Goran |
| FAMILY NAME | Nenadic |
| SIGNATURE | NENADIC G |
| AFFILIATIONS | University of Manchester |
| ORCID | 0000-0003-0795-5363 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2004 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Surveillance of Domestic Violence Using Text Mining Outputs From Australian Police Records
In Australia, domestic violence reports are mostly based on data from the police, courts, hospitals, and ad hoc surveys. However, gaps exist in reporting information such as victim injuries, mental health status and abuse types. The police record details of domestic violence events as structured information (e.g., gender, postcode, ethnicity), but also in text narratives describing other details such as injuries, substance use, and mental health …
Modelling trend life cycles in scientific research using the Logistic and Gompertz equations
Scientific topics vary in popularity over time. In this paper, we model the life cycles of 200 trending topics by fitting the Logistic and Gompertz models to their frequency over time in published abstracts. Unlike other work, the topics we use are algorithmically extracted from large datasets of abstracts covering computer science, particle physics, cancer research, and mental health. We find that the Gompertz model produces lower median error, …
Should free-text data in electronic medical records be shared for research? A citizens’ jury study in the UK
BACKGROUND: Use of routinely collected patient data for research and service planning is an explicit policy of the UK National Health Service and UK government. Much clinical information is recorded in free-text letters, reports and notes. These text data are generally lost to research, due to the increased privacy risk compared with structured data. We conducted a citizens' jury which asked members of the public whether their medical free-text d…
Mining term similarities from corpora
In this article, we present an approach to the automatic discovery of term similarities, which may serve as a basis for a number of term-oriented knowledge mining tasks. The method for term comparison combines internal (lexical similarity) and two types of external criteria (syntactic and contextual similarities). Lexical similarity is based on sharing lexical constituents (i.e. term heads and modifiers). Syntactic similarity relies on a set of s…
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Mining term similarities from corpora
In this article, we present an approach to the automatic discovery of term similarities, which may serve as a basis for a number of term-oriented knowledge mining tasks. The method for term comparison combines internal (lexical similarity) and two types of external criteria (syntactic and contextual similarities). Lexical similarity is based on sharing lexical constituents (i.e. term heads and modifiers). Syntactic similarity relies on a set of s…
Should free-text data in electronic medical records be shared for research? A citizens’ jury study in the UK
BACKGROUND: Use of routinely collected patient data for research and service planning is an explicit policy of the UK National Health Service and UK government. Much clinical information is recorded in free-text letters, reports and notes. These text data are generally lost to research, due to the increased privacy risk compared with structured data. We conducted a citizens' jury which asked members of the public whether their medical free-text d…
Modelling trend life cycles in scientific research using the Logistic and Gompertz equations
Scientific topics vary in popularity over time. In this paper, we model the life cycles of 200 trending topics by fitting the Logistic and Gompertz models to their frequency over time in published abstracts. Unlike other work, the topics we use are algorithmically extracted from large datasets of abstracts covering computer science, particle physics, cancer research, and mental health. We find that the Gompertz model produces lower median error, …
Surveillance of Domestic Violence Using Text Mining Outputs From Australian Police Records
In Australia, domestic violence reports are mostly based on data from the police, courts, hospitals, and ad hoc surveys. However, gaps exist in reporting information such as victim injuries, mental health status and abuse types. The police record details of domestic violence events as structured information (e.g., gender, postcode, ethnicity), but also in text narratives describing other details such as injuries, substance use, and mental health …
Computer Science (3 works) · Psychology (3 works) · Mathematics (2 works) · Medicine (2 works) · Mental health (2 works) · Political science (2 works) · Adolescent Sexual and Reproductive Health (1 works) · Alternative medicine (1 works) · Artificial Intelligence (1 works) · Biomedical Text Mining and Ontologies (1 works)