Derleme
Adli bilim ve adli tıpta makine öğrenmesi: Literatür üzerine araştırma
Yıl 2022, Cilt: 36 Sayı: 1, 1 - 7, 01.04.2022Öz
Makine öğrenmesi yöntemleri, günümüzde dijitalleşme ile ortaya çıkan büyük veri setlerini anlamada ve yorumlamada oldukça başarılı sonuçlar elde etmektedir. Bu çalışmadaki amacımız, adli bilim ve adli tıp alanlarında makine öğrenmesi yöntemlerini araştırmak ve bu alandaki eğilimleri analiz etmektir.
Çalışmada PubMed veri tabanında 1988-2021 yılları arasında “Forensic Machine Learning” arama terimi kullanılarak 404 makale ve 1999-2021 yılları arasında “Forensic Medicine Machine Learning” arama terimi kullanılarak ortak olan 220 makaleye ulaşılmıştır.
Adli bilim ve adli tıp alanlarında makine öğrenme yöntemlerinin en sık cinsiyet ve yaş tahmininde kullanıldığı belirlenmiştir. Ayrıca en çok kullanılan makine öğrenmesi yönteminin “yapay sinir ağları” olduğu tespit edilirken en fazla kullanılan yöntem değerlendirme kriteri “doğruluk” olarak bulun-muştur.
Sonuç olarak; adli bilim ve adli tıpta yeni bir yaklaşım olan makine öğrenimine adli bilim ve adli tıp uzmanlarını makine öğrenimi çalışmalarına teşvik etmeyi amaçlıyoruz.
Anahtar Kelimeler
Kaynakça
- Mitchell TM, Machine Learning. 1st. New York: McGraw-Hill, 1997:p.414.
- Michie D, Spiegelhalter D, Taylor C. Machine Learning, Neural and Statistical Classification. Technometrics 1999:37.
- Miller DD. Machine Intelligence in Cardiovascular Medicine. CardiolRev 2020 ;28(2):53-64.
- Cabitza F, Locoro A, Banfi G. Machine Learning in Orthopedics: A Literature Review. Front Bioeng Biotechnol. 2018;27(6):75.
- Erbay LG, Celbiş O, Oruç M, Karlıdağ R.Investigation using psychological autopsy method of completed suicide cases coming to the Council of Forensic Medicine, Malatya Regional Office. Journal Of Forensıc Medıcıne. 2020;34(1):1-6.
- Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. NatRevGenet. 2015;16(6):321-32.
- Guan X, Zhang B, Fu M, Li M, Yuan X, Zhu Y, Peng J, Guo H, Lu Y. Clinical and inflammatory features based machine learning model forfatal risk prediction of hospitalized COVID-19 patients: resultsfrom a retrospective cohort study. AnnMed. 2021;53(1):257-66.
- Liu R, Gu Y, Shen M, Li H, Zhang K, Wang Q, Wei X, Zhang H, Wu D, Yu K, Cai W, Wang G, Zhang S, Sun Q, Huang P, Wang Z. Predicting postmortem interval based on microbial community sequences and machine learning algorithms. Environ Microbiol. 2020;22(6):2273-91.
- Gallidabino MD, Barron LP, Weyermann C, Romolo FS. Quantitative profile-profile relationship (QPPR) modelling: a novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR). Analyst. 2019;11;144(4):1128-39.
- Vidaki A, Montiel González D, Planterose Jiménez B, Kayser M. Male-specific age estimation based on Y-chromosomal DNA methylation. Aging (Albany NY). 2021;11;13(5):6442-58.
- Ortega RF, Irurita J, Campo EJE, Mesejo P. Analysis of the performance of machine learning and deep learning methods for sex estimation of infant individuals from the analysis of 2D images of the ilium. Int J Legal Med. 2021;135(6):2659-2666.
- Navega D, Vicente R, Vieira DN, Ross AH, Cunha E. Sex estimation from the tarsal bones in a Portuguese sample: a machine learning approach. Int J Legal Med. 2015;129(3):651-9.
- He H, ve Garcia EA, Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering 2009;21:1263–84.
- Ge Z, Song Z, Ding SX, Huang B. Data Mining and Analytics in the Process Industry: The Role of Machine Learning. IEEE Access 2017;5:20590–616.
- Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine Learning and Data Mining Methods in Diabetes Research. Computational and Structural Biotechnology Journal 2017;15:104–16.
- Kern C, Klausch T, Kreuter F. Tree-based machine learning methods for survey research. In Survey research methods 2019;13(1):73.
- Cutler A, Cutler DR, Stevens JR. Tree-based methods. In High-Dimensional Data Analysis in Cancer Research. Springer, New York. 2008:p.1-19.
- Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 2015: 521(7553);452-9.
- Cortes C, Vapnik V, Support Vector Networks. Mach Learn 1995;20, 273–97.
- Fung G, Sandilya S, Rao RB. Rule extraction from linear support vector machines. Conference: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2005;21-4. Chicago, Illinois, USA.
- Kustrin SA, Beresford R, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis 2020; 22(5)717-27.
- Zou J, Han Y, So SS. Overview of Artificial Neural Networks. In: Livingstone D.J. (eds) Artificial Neural Networks. Methods in Molecular Biology™. 2008;458:15-23. Humana Press.
- Bilgin M. Performance Analysis of Classical Machine Learning Methods on Real Datasets [in Turkish]. Breast, 2017:2(9);683.
- Nizam H, Akın SS. Comparison of the Performance of Balanced and Unbalanced Datasets in Sentiment Analysis with Machine Learning in Social Media [in Turkish]. XIX. Internet Conference in Turkey, 2014:1-6.
- Karslı ÖB. Diagnosis of Liver Disease with Machine Learning Methods [in Turkish]. Ağrı İbrahim Çeçen University. Master Thesis. Ağrı. 2019.
- Mikkonen HG, Clarke BO, Dasika R, Wallis CJ, Reichman SM, Evaluation of methods for managing censored results when calculating the geometric mean, Chemosphere. 2018;191:412-16.
- McIntyre S H, Montgomery DB, Srinivasan V, Weitz BA, Evaluating the statistical significance of models developed by stepwise regression. Journal of Marketing Research. 1983;20(1):1–11.
- Hocking RR, A Biometrics invited paper: The analysis and selection of variables in linear regression. Biometrics 1976;32(1):1–49.
- Kruse C, Eiken P, Vestergaard P. Machine Learning Principles Can Improve Hip Fracture Prediction. Calcif. Tissue Int. 2017;100(4):348–360.
- Liu Z, Tang D, Cai Y, Wang R, Chen F, A hybrid method based on ensemble WELM for handling multi class imbalance in cancer microarray data. Neurocomputing 2017;266:641-50.
- Turhan S, Özkan Y, Suner A, Doğu E. Comparison of Ensemble Learning Methods for Disease Diagnosis in Presence of Class Unbalanced: Case of Diabetes. Turkiye Klinikleri J Biostat. 2020;12(1):16-26
- Gu H, Song T. Balanced Sampling Method for Imbalanced Big Data Using AdaBoost. In Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies. 2015;)189–94.
Machine learning in forensic science and forensic medicine: Research on the literature
Yıl 2022, Cilt: 36 Sayı: 1, 1 - 7, 01.04.2022Öz
Machine learning methods achieve very successful results in understanding and interpreting the large data sets that have emerged with digitalization today. Our aim in this study is to investigate machine learning methods in the fields of forensic science and forensic medicine and to analyze the trends in this field.
In the study, 404 articles were reached by using the search term “Forensic Machine Learning” between 1988-2021 in PubMed database and 220 articles were shared by using the search term “Forensic Medicine Machine Learning” between 1999-2021.
It was determined that machine learning methods were used most frequently in the estimation of gender and age in the fields of forensic science and forensic medicine. In addition, while it was determined that the most used machine learning method was “artificial neural networks”, the most used method evaluation criterion was “accuracy”.
As a result, we aim to encourage forensic science and forensic medicine experts to work on machine learning, which is a new approach in forensic science and forensic medicine.
Anahtar Kelimeler
Kaynakça
- Mitchell TM, Machine Learning. 1st. New York: McGraw-Hill, 1997:p.414.
- Michie D, Spiegelhalter D, Taylor C. Machine Learning, Neural and Statistical Classification. Technometrics 1999:37.
- Miller DD. Machine Intelligence in Cardiovascular Medicine. CardiolRev 2020 ;28(2):53-64.
- Cabitza F, Locoro A, Banfi G. Machine Learning in Orthopedics: A Literature Review. Front Bioeng Biotechnol. 2018;27(6):75.
- Erbay LG, Celbiş O, Oruç M, Karlıdağ R.Investigation using psychological autopsy method of completed suicide cases coming to the Council of Forensic Medicine, Malatya Regional Office. Journal Of Forensıc Medıcıne. 2020;34(1):1-6.
- Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. NatRevGenet. 2015;16(6):321-32.
- Guan X, Zhang B, Fu M, Li M, Yuan X, Zhu Y, Peng J, Guo H, Lu Y. Clinical and inflammatory features based machine learning model forfatal risk prediction of hospitalized COVID-19 patients: resultsfrom a retrospective cohort study. AnnMed. 2021;53(1):257-66.
- Liu R, Gu Y, Shen M, Li H, Zhang K, Wang Q, Wei X, Zhang H, Wu D, Yu K, Cai W, Wang G, Zhang S, Sun Q, Huang P, Wang Z. Predicting postmortem interval based on microbial community sequences and machine learning algorithms. Environ Microbiol. 2020;22(6):2273-91.
- Gallidabino MD, Barron LP, Weyermann C, Romolo FS. Quantitative profile-profile relationship (QPPR) modelling: a novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR). Analyst. 2019;11;144(4):1128-39.
- Vidaki A, Montiel González D, Planterose Jiménez B, Kayser M. Male-specific age estimation based on Y-chromosomal DNA methylation. Aging (Albany NY). 2021;11;13(5):6442-58.
- Ortega RF, Irurita J, Campo EJE, Mesejo P. Analysis of the performance of machine learning and deep learning methods for sex estimation of infant individuals from the analysis of 2D images of the ilium. Int J Legal Med. 2021;135(6):2659-2666.
- Navega D, Vicente R, Vieira DN, Ross AH, Cunha E. Sex estimation from the tarsal bones in a Portuguese sample: a machine learning approach. Int J Legal Med. 2015;129(3):651-9.
- He H, ve Garcia EA, Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering 2009;21:1263–84.
- Ge Z, Song Z, Ding SX, Huang B. Data Mining and Analytics in the Process Industry: The Role of Machine Learning. IEEE Access 2017;5:20590–616.
- Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine Learning and Data Mining Methods in Diabetes Research. Computational and Structural Biotechnology Journal 2017;15:104–16.
- Kern C, Klausch T, Kreuter F. Tree-based machine learning methods for survey research. In Survey research methods 2019;13(1):73.
- Cutler A, Cutler DR, Stevens JR. Tree-based methods. In High-Dimensional Data Analysis in Cancer Research. Springer, New York. 2008:p.1-19.
- Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 2015: 521(7553);452-9.
- Cortes C, Vapnik V, Support Vector Networks. Mach Learn 1995;20, 273–97.
- Fung G, Sandilya S, Rao RB. Rule extraction from linear support vector machines. Conference: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2005;21-4. Chicago, Illinois, USA.
- Kustrin SA, Beresford R, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis 2020; 22(5)717-27.
- Zou J, Han Y, So SS. Overview of Artificial Neural Networks. In: Livingstone D.J. (eds) Artificial Neural Networks. Methods in Molecular Biology™. 2008;458:15-23. Humana Press.
- Bilgin M. Performance Analysis of Classical Machine Learning Methods on Real Datasets [in Turkish]. Breast, 2017:2(9);683.
- Nizam H, Akın SS. Comparison of the Performance of Balanced and Unbalanced Datasets in Sentiment Analysis with Machine Learning in Social Media [in Turkish]. XIX. Internet Conference in Turkey, 2014:1-6.
- Karslı ÖB. Diagnosis of Liver Disease with Machine Learning Methods [in Turkish]. Ağrı İbrahim Çeçen University. Master Thesis. Ağrı. 2019.
- Mikkonen HG, Clarke BO, Dasika R, Wallis CJ, Reichman SM, Evaluation of methods for managing censored results when calculating the geometric mean, Chemosphere. 2018;191:412-16.
- McIntyre S H, Montgomery DB, Srinivasan V, Weitz BA, Evaluating the statistical significance of models developed by stepwise regression. Journal of Marketing Research. 1983;20(1):1–11.
- Hocking RR, A Biometrics invited paper: The analysis and selection of variables in linear regression. Biometrics 1976;32(1):1–49.
- Kruse C, Eiken P, Vestergaard P. Machine Learning Principles Can Improve Hip Fracture Prediction. Calcif. Tissue Int. 2017;100(4):348–360.
- Liu Z, Tang D, Cai Y, Wang R, Chen F, A hybrid method based on ensemble WELM for handling multi class imbalance in cancer microarray data. Neurocomputing 2017;266:641-50.
- Turhan S, Özkan Y, Suner A, Doğu E. Comparison of Ensemble Learning Methods for Disease Diagnosis in Presence of Class Unbalanced: Case of Diabetes. Turkiye Klinikleri J Biostat. 2020;12(1):16-26
- Gu H, Song T. Balanced Sampling Method for Imbalanced Big Data Using AdaBoost. In Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies. 2015;)189–94.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Adli Tıp |
| Bölüm | Derlemeler |
| Yazarlar |
|
| Yayımlanma Tarihi | 1 Nisan 2022 |
| Gönderilme Tarihi | 23 Ocak 2022 |
| Yayımlandığı Sayı | Yıl 2022Cilt: 36 Sayı: 1 |
Adli Tıp Dergisi Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır.
Dergimiz Açık Erişim Politikasını benimsemiş olup, gönderilen makaleler için yayının hiçbir aşamasında yazarlardan ücret talep edilmeyecektir.
Dergimiz Açık Erişim Politikasını benimsemiş olup, gönderilen makaleler için yayının hiçbir aşamasında yazarlardan ücret talep edilmeyecektir.