Lapadula, Paola

Mecca, Giansalvatore

Santoro, Donatello

Solimando, Luisa

Veltri, Enzo

Archivio della Ricerca - Università della Basilicata

Large-scale Machine Learning for HealthcarePaola Lapadula1, Giansalvatore Mecca1, Donatello Santoro1, LuisaSolimando2, and Enzo Veltri11 University of Basilicata, Potenza, Italy2 Svelto! Big Data Cleaning and Analytics, Potenza, Italy1 IntroductionGreg, ML [4] is a machine learning system that generates automatic diagnosticsuggestions based on patient profiles. In summary, Greg takes as input a digitalprofile of a patient, and suggests one or more diagnosis that, according to itsinternal learned models, fit the profile with a given probability. We assume thata doctor inspects these diagnostic suggestions, and takes informed actions aboutthe patients and the related medical treatments.We do not reinvent the wheel, the idea of using machine learning for thepurpose of examining medical data is not new [3, 6, 5]. In fact, several efforts thathave been taken in this direction [1, 2]. However, to the best of our knowledge,all of the existing tools concentrate on rather specific learning tasks, for exampleidentifying a single pathology – like heart disease [9, 7], or pneumonia [8], orcancer, where results of remarkable quality have been reported [10]. On the otherhand, Greg has the distinguishing feature of being a broad-scope diagnostic-suggestion tool. In fact, at the core of the tool stands a multi-label learningmodel that allows to suggest large numbers of pathologies, currently about fifty,and in perspective several hundreds.Greg is a research project developed by Svelto!, a spin-off of the data-managementgroup at University of Basilicata.2 Architecture of GregThe architecture and the overall flow of Greg is depicted in Figure 1.At the core of Greg there is a classifier for patient profiles that providesdoctors with diagnostic suggestions. Patients profiles are entirely anonymous,i.e., Greg does not store nor requires any sensitive data, and are composed ofthree main blocks:– anonymous biographical data, mainly age and gender, and medical historyof the patient, i.e., past medical events, the past and the current medicaltherapy and pathologies, especially the chronic ones;– result of lab exams, in numerical format;– textual reports from instrumental exams, like RX, ultrasounds etc.2 P. Lapadula et al.numericallab resultstextualreportspatient profilediagnosticsuggestionspatientbiographic dataand medicalhistorypathologyindicatorsdoctorFig. 1: Architecture of Greg.These items compose what we called the patient profile that is fed to theprofile classifier in order to propose diagnostic suggestions to doctors. Noticethat, while biographic data, medical history and lab exam results are essentiallystructured data, and therefore can be easily integrated into the profile, reports ofinstrumental exams are essentially text, and consequently, they are in the formof unstructured data. As a consequence, Greg relies on a second learning moduleto extract what we call pathology indicators, i.e., structured labels indicatinganomalies in the report that may suggest the presence of a pathology.Actually, the report classifier is a natural-language processing module. Itidentifies pathology indicators from the text of the report in natural language.Then, the pathology indicators can be integrated within the patient profile.The crucial module for the generation of the patient profile is the reportclassifier. In fact, reports of instrumental exams often carry crucial informationfor the goal of identifying the correct diagnostic suggestions. At the same time,their treatment is language-dependent, and learning is labor-intensive step, sinceit requires to manually label a large set of reports in order to train the classifier.Once the profile for a new patient has been generated, it is fed to the profileclassifier that outputs diagnostic suggestions to the physician. There are a fewimportant aspects to be observed here.– First, Greg is trained to predict only a finite set of diagnoses. This means thatit is intended mainly as a tool to gain positive evidence about pathologiesthat might be present, rather than as a tool to exclude pathologies that arenot present. In other terms, the fact that Greg does not predict a specificdiagnosis does not mean that that can be ignored or excluded, since it mightLarge-scale Machine Learning for Healthcare 3only be the case that Greg has not be trained for that particular pathology.It can be seen that handling a large number of diagnoses is crucial, in thisrespect.– Second, with each diagnostic suggestion Greg associated a degree of proba-bility, i.e., it ranks suggestions with a confidence measure. This is important,since the tool may provide several different suggestions for a given profile,and not all of them are to be considered as equally relevant.It can be seen that a tool like Greg is as effective as seamless its integrationwith the everyday procedures of a medical institution is. To foster this kind ofadoption, Greg can be used as a stand-alone tool, with its own user-interface,but it has been developed primarily as an engine-backed API, that can be eas-ily integrated with any medical information system that is already deployed inmedical units and wards. Ideally, with this kind of integration, accessing med-ical suggestions provided by Greg should cost no more than clicking a button,in addition of the standard procedure for patient-data gathering and medical-record compilation. Finally, we also developed a stand-alone Web app called theGreg Playground. The app is available at URL https://demo.svelto-spinoff.-it/Greg-ML-Playground/.References1. Deo, R.C.: Machine learning in medicine. Circulation 132(20), 1920–1930 (2015)2. Holzinger, A.: Machine learning for health informatics. In: Machine Learning forHealth Informatics, pp. 1–24. Springer (2016)3. Kononenko, I.: Machine learning for medical diagnosis: history, state of the art andperspective. Artificial Intelligence in medicine 23(1), 89–109 (2001)4. Lapadula, P., Mecca, G., Santoro, D., Solimando, L., Veltri, E.: Humanity Is Over-rated. or Not. Automatic Diagnostic Suggestions by Greg, ML. In: New Trends inDatabases and Information Systems. pp. 305–313. Springer International Publish-ing (2018)5. Mohammed, O., Benlamri, R.: Developing a semantic web model for medical dif-ferential diagnosis recommendation. Journal of medical systems 38(10), 79 (2014)6. Peek, N., Combi, C., Marin, R., Bellazzi, R.: Thirty years of artificial intelligencein medicine (aime) conferences: A review of research themes. Artificial Intelligencein Medicine 65(1), 61–73 (2015)7. Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., Ng, A.Y.: Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprintarXiv:1707.01836 (2017)8. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul,A., Langlotz, C., Shpanskaya, K., et al.: Chexnet: Radiologist-level pneumonia de-tection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)9. Soni, J., Ansari, U., Sharma, D., Soni, S.: Predictive data mining for medical diag-nosis: An overview of heart disease prediction. International Journal of ComputerApplications 17(8), 43–48 (2011)10. Steadman, I.: IBM’s Watson is better at diagnosing cancer than human doctors.WIRED (2013), http://www.wired.co.uk/article/ibm-watson-medical-doctor

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