TF-IDF-Based Automated Application for classification Forensic Autopsy Reports to Identification of Cause of Death (CoD)

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1 Chiew T.K., et al. (Eds.): PGRES 2017, Kuala Lumpur: Eastin Hotel, FCSIT, 2017: pp TF-IDF-Based Automated Application for classification Forensic Autopsy Reports to Identification of Cause of Death (CoD) Zahra Shams Khoozani & Ram Gopal Raj Department of AI, Faculty of Computer Science and Information Technology University of Malaya Jalan Universiti, Kuala Lumpur, MALAYSIA ABSTRACT Nowadays, making automated biomedical applications in medical science is a major concern for AI researchers. This is because usually medical documents could be too complicated and automated applications can be helpful to experts to analyse different cases. The aim of this paper is to proposed automated application which is able to identify the Cause of Death (CoD) by using forensic autopsy. A forensic autopsy or a post-mortem is the surgical process that includes external and internal examination of dead body and forensic autopsy is a full written report which is consists all autopsy findings to determine the CoD. In forensic autopsy, different experts collect various information from dead body. Therefore, performing autopsy examination to discover CoD is laborious and time consuming. Hence, automated application which is able to analyse complex forensic autopsy and identify CoD, can be valuable to decrease the time, labor involved and eliminating irregularities in determining CoD that can arise due to human error. Many researchers have proposed automated approaches that require very large datasets and also have complex methodologies. In addition, the lack of readily available forensic data due to privacy issues makes this a significant problem. Therefore, what is required is a system that produces high accuracy with low computational complexity. Moreover, the forensic autopsy are text centric and medical terms tend to be indicative of particular symptoms and causes which magnifies usefulness of TF-IDF as weighted terms carry the greatest impact. TF-IDF is a way to score the importance of term in a document based on how frequently they appear across multiple documents. Keywords: Biomedical Application; Forensic Autopsy Reports; Cause of Death; TF-IDF; Feature Extraction INTRODUCTION In recent years, developing biomedical and healthcare applications have progress rapidly to help medical experts (Peek, Combi, Marin, & Bellazzi, 2015). Because medical experts always have major concern to provide high accurate results as identification with low Page 57

2 Zahra Shams Khoozani & Ram Gopal Raj time consumption. In addition, designing an automated system which work on complex data, could lead to improve quality, fewer medical errors and lower costs. On the other hand, artificial intelligence techniques have propelled advances in medicine and become an essential part of wide range of medical in order to offers experts a chance at better identifying of causes with more efficiency and precision. Therefore, developing automated biomedical applications is valuable to apply AI techniques in the field of medicine for getting a high accurate system. Besides, the task of autopsy is considered effective in the health-related education because recently the purpose of autopsy has change to quality assurance and improves the healthcare industry (Davies et al., 2004; van den Tweel & Wittekind, 2016). This study involves a complete process of designing a fully automated CoD application to provide an efficient fully automated system to solve the problems of determination of CoD which are doing by experts or pathologists and using forensic autopsy. Forensic autopsy or the Medico-legal is carry out the instructions of the legal authority and criminal investigation (Vij, 2014). A forensic autopsy or a post-mortem is the surgical process that include anatomical and external examination of dead body to determine the CoD. The forensic pathologist focuses on discovering the CoD by doing post-mortem examination. During forensic autopsy examination, experts collect various external and internal information. External examination includes collecting some external deceased information such as injury related information, recent medical therapy and any postmortem changes (Saukko & Knight, 2015). Internal examination pathologists collect various findings from internal organs of the body such as heart, brain, abdomen, etc. Moreover, experts collect death scene information from police, witnesses, and relatives. The collected autopsy findings are correlated with any medical history, pre-mortem and post-mortem laboratory studies, microscopic tissues analysing, toxicology and other related medical procedures and documents to ascertain the CoD according to the World Health Organization (WHO) ICD-10 coding examination standard (Abacha et al., 2015). Determining the particular CoD could be a complicated process and takes considerable amount of time because during forensic autopsy examination, different experts collect various information relevant to internal and external organs. For instance, the examination of autopsy might take 2 to 4 hours. The preliminary report is prepared in 2 to 3 days. However, final report is being available from 30 to 45 days and could take up to 90 days depends on complexity of the case (James, Nordby, & Bell, 2002). Therefore, performing autopsy examination and providing forensic autopsy report to discover CoD is laborious, time consuming and is a matter to inconsistencies correlated with any labor-intensive process (Hoelz, Ralha, & Geeverghese, 2009). LITERATURE REVIEW Many researchers have worked on forensic autopsy data and proposed automated classification approaches that required very huge forensic datasets which leads to have complicated methodologies. According to (Mujtaba et al., 2017), selecting the correct features with discriminative ability among different CoD is an intricate and complex task. For instance, in (Yeow, Mahmud, & Raj, 2014), the authors used 3568 autopsy of war victims from various mass graves in Eastern Bosnia. The objective of their study, Page 58

3 TF-IDF-Based Automated Application for classification Forensic Autopsy Reports to Identification of Cause of Death (CoD) regarding to classify forensic autopsy is extract features to analyze the case similarities. They used information extraction (IE) technique from the existing autopsy and in terms of technical implementation, one limitations is the proposed model requires a great deal of user intervention. In (Mujtaba et al., 2017), authors provide Automatic classification model which used multi-class feature selection and include 2200 complete forensic autopsy, which is 8 to 15 pages in length. The limitation of this study is that various classes or causes of death is a complicated task and involves substantial effort. Regarding to requiring huge datasets, in (Danso, Atwell, & Johnson, 2013), the datasets contains a total of over 11,700 verbal autopsy documents for CoD prediction. The authors used computational linguistics and machine learning approaches to identify various features based on TF-IDF methods to be able to classify. The limitation are stated as CoD is determined by using a complex combination of information from the overall document, and this is hard to capture in simplistic models used in Machine Learning classifiers. Based on the limitations of current automated CoD systems as pertains to their computational complexity and need for large training datasets, this study has two main objectives. Firstly, to develop a CoD identification system which has high accuracy (more than 90 percent) with low computational complexity. Secondly, to evaluate the system s ability to classify forensic autopsy and identify CoD with high accuracy. The forensic autopsy are text centric and complements the nature of forensic autopsy are mostly text based. According to (Chen, Fuller, Friedman, & Hersh, 2006), some computational techniques such as text mining are required to analyze biomedical data for extracting useful patterns and information from them. In addition, text mining techniques have applied in many successful biomedical applications in recent years. Moreover, medical terms tend to be indicative of particular symptoms and causes which means applying some methods are required to extract key features. TF-IDF can be successfully used for text classification (Croft, 2000). In this study, both standard and cumulative TF-IDF methods are used as weighted terms to carry the greatest impact on feature selection. Then the selected features will be use to classifying forensic autopsy and finally assign a particular CoD. RESEARCH DESIGN For this research, the forensic autopsy were collected from Pusat Perubatan Universiti Malaya (PPUM) which is the well-known and governmentfunded hospital in the dynamic and bustling capital city of Malaysia. The collected forensic autopsy are related to seven most common CoD which are belonging to three different manners of death (MoD) as per PPUM record office. Natural, accident and suicide are the three MoD and for each CoD we collected 10 complete forensic autopsy. Each autopsy report contains the detailed information about death and dead body such as the personal information of decease, external examination information, sign of postmortem change information, the condition of deceased when he/she was brought to hospital, whether the case is medico legal or not, internal Page 59

4 Zahra Shams Khoozani & Ram Gopal Raj examination information, injury related information, histopathology information and deceased medical history related information. However the compulsory information for identification of CoD is include four different type of information i.e. external examination information, anatomical examination information, injury related information and death scene related information. Hence, in total we collected 70 complete forensic autopsy belonging to seven different CoDs and three different MoDs. The detail distribution of collected complete forensic autopsy based on CoD and MoD with WHO ICD-10 coding standard are shown in TABLE 1. Table 1: 7 different CoD related to 3 different MoD with WHO ICD-10 coding standards MoD CoD WHO ICD-10 Code Natural Ischemic Heart Disease I-24 Natural Chronic Heart Disease I-25 Natural Acute Myocardial Infarction I-23 Accident Head Injury S-06 Accident Multiple Injuries T-07 Suicide Intentional Self harm by Hanging X-70 Suicide Intentional Self harm by Height X-80 cases 10 complete IHD cases 10 complete CHD cases 10 complete AMI cases 10 complete HI cases 10 complete MI cases 10 complete HNG cases 10 complete HET cases This section provides a brief discussion of the methods employed in fully automated CoD determination which is classifying forensic autopsy. The forensic autopsy are text centric and complements the nature of forensic autopsy are mostly text based. For this reason, natural language processing (NLP) techniques such as text mining techniques are the chosen research methodology to design a fully automated CoD application. In the following, more detailed Hierarchy steps are explained. 1) Since forensic autopsy are plain text and unstructured, data pre-processing techniques are required to preparing dataset for analyzing. For data pre-processing we used the natural language processing tool kit (NLTK) (Bird, Klein, & Loper, 2009) which is programming in python programming language. In data pre-processing step, all the content was changed to lower case characters and all special symbols, punctuations and stop words were removed. 2) For the purpose of extracting key features, first it is require to obtain feature value. The TF-IDF methods are used to present a feature value which is a straightforward methods. TF-IDF stands from term frequency-inverse document frequency (Wu, Luk, Wong, & Kwok, 2008). It can be successfully used for text classification (Croft, 2000). If a word appears frequently in a document, it is important and get high score. But if a word appears in many documents, it is not a unique word and get low score (Wu et al., 2008). 3) After that, based on the each feature value, we apply commutative TF-IDF algorithm to extract key features. The autopsy report is a complicated report having lot of information on it. One report Page 60

5 TF-IDF-Based Automated Application for classification Forensic Autopsy Reports to Identification of Cause of Death (CoD) comprised of from 8 to 15 pages depending upon the CoD. Therefore, feature extraction techniques may extract massive features from all 70 autopsy. This can be very high for many decision systems and might leads to provide less accurate system (Sebastiani, 2002; Yang & Pedersen, 1997). Accordingly, cumulative TF-IDF algorithm has applied as summative approach which uses the scores from multiple attributes for weighting predictive measures for classification and identification. 4) In the last step, a class weight engine are created which include two different kind of classifier to provide a high accurate system. The two different kind of classifier are involves same-word classifier and unique-word classifier. RESULTS In this research, from all 70 autopsy, after applying the pre-processing algorithms, a total of features were obtained. From all features, 3000 features are key features and stored to create class weight engine. The remaining features were removed because they are not important features. The same-word classifier included 210 different classes and unique-word classifier included 420 different classes. Therefore in total, class weight engine is contain 630 different class for classification and produce high accurate (more than 90 percent) result. The proposed system is working very fast with low computational time consumption. It takes less than a minute for both classification of autopsy and identification of particular CoD. CONCLUSION The fully automated approach of proposed application is efficient to solve the problems of determination of CoD which are doing by pathologists and using forensic autopsy findings. The proposed system is beneficial to identify the CoD accurately and rapidly and it is valuable to decrease the time, labor involved and eliminating irregularities in determining CoD that can arise due to human error. The task of classification of ICD-10 related CoD are performed automatically to provide a high accurate result. Furthermore, the proposed classification and identification application are generally applicable to other types of ICD-10 related CoD as well as other kinds of plaintext clinical. REFERENCES Abacha, A. B., Chowdhury, M. F. M., Karanasiou, A., Mrabet, Y., Lavelli, A., & Zweigenbaum, P. (2015). Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug drug interaction extraction and classification. Journal of biomedical informatics, 58, Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit: " O'Reilly Media, Inc.". Chen, H., Fuller, S. S., Friedman, C., & Hersh, W. (2006). Medical informatics: knowledge management and data mining in biomedicine (Vol. 8): Springer Science & Business Media. Page 61

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