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Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study

Mujtaba G, Shuib L, Raj RG, Rajandram R, Shaikh K

J Forensic Leg Med, 2018 Jul;57:41-50.
PMID: 29801951 DOI: 10.1016/j.jflm.2017.07.001

OBJECTIVES: Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction.
METHODS: For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall.
RESULTS: From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier.
CONCLUSION: Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques.
Fulltext Classification of forensic autopsy reports through conceptual graph-based document representation model

Mujtaba G, Shuib L, Raj RG, Rajandram R, Shaikh K, Al-Garadi MA

J Biomed Inform, 2018 06;82:88-105.
PMID: 29738820 DOI: 10.1016/j.jbi.2018.04.013

Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD.
Fulltext Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

Mujtaba G, Shuib L, Raj RG, Rajandram R, Shaikh K, Al-Garadi MA

PLoS One, 2017;12(2):e0170242.
PMID: 28166263 DOI: 10.1371/journal.pone.0170242

OBJECTIVES: Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models.
METHODS: Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system.
RESULTS: Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines.
CONCLUSION: The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.
Fulltext Expert Opinion: Patient Selection for Premixed Insulin Formulations in Diabetes Care

Kalra S, Czupryniak L, Kilov G, Lamptey R, Kumar A, Unnikrishnan AG, et al.

Diabetes Ther, 2018 Dec;9(6):2185-2199.
PMID: 30390228 DOI: 10.1007/s13300-018-0521-2

Premixed insulins are an important tool for glycemic control in persons with diabetes. Equally important in diabetes care is the selection of the most appropriate insulin regimen for a particular individual at a specific time. Currently, the choice of insulin regimens for initiation or intensification of therapy is a subjective decision. In this article, we share insights, which will help in rational and objective selection of premixed formulations for initiation and intensification of insulin therapy. The glycemic status and its variations in a person help to identify the most appropriate insulin regimen and formulation for him or her. The evolution of objective glucometric indices has enabled better glycemic monitoring of individuals with diabetes. Management of diabetes has evolved from a 'glucocentric' approach to a 'patient-centered' approach; patient characteristics, needs, and preferences should be evaluated when considering premixed insulin for treatment of diabetes.Funding: Novo Nordisk, India.