Preventive medicine by means of deep learning techniques applied in healthcare prognosis

• Grant number: 18/17620-5
• Support Opportunities: Scholarships abroad - Research
• Start date: February 16, 2019
• End date: February 15, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: José Fernando Rodrigues Júnior
• Grantee: José Fernando Rodrigues Júnior
• Host Investigator: Sihem Amer Yahia
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Institution abroad: Laboratoire d'Informatique de Grenoble (LIG), France

Abstract

Deep Learning (DL) describes a class of algorithms capable of combining raw inputs into successive layers of intermediate features to achieve computational intelligence. These algorithms have shown impressive results across several domains. In Medicine, for example, which is a data-rich discipline, the data are complex and often not yet understood. Deep Learning techniques may be particularly well-suited to solve problems in this field. In that context, the aim of this project is to explore the possibilities of DL in the context of computer-aided medicine; the activities include dealing with the various problems related to the application of DL to the specific context of clinical data. The issues include pre-processing demands to produce large, labeled, and cleaned datasets of clinical data; modeling complex information according to the demands of DL processing; fine-tuning DL architectures with respect to the specific problems of prognostic care; iterative training-testing rounds to achieve highly accurate methods; clinical validation of the results; and dissemination of the methods in the form of real-world applications with actual social impact. These tasks will be carried out over two initial problems: (i) the automatic detection of skin tumors from skin lesion photos; (ii) the prognostic of patients based on the clinical history as given by Electronic Medical Records (EMRs). The first problem will use open-access data from the ISIC Dermoscopic Archive, and from the Edinburgh Dermofit Library to transfer-learn over the ResNet-152 network modeled to the ILSVRC competition. The second problem will use data from the French institution AGIR to guide the modeling and training of a Long Short-Term Memory DL architecture capable of inferring over large contexts of sequential information, as those seen in EMRs. The two methods shall impact on practices of preventive medicine, allowing for early detection of skin tumors and early recommendation of treatments/procedures. This impact is of special importance to the Brazilian scenario, in which basic health care is inaccessible in many regions of the country. The postdoc period will contribute to the expertise of the proponent in a currently active research field, promoting new investigative fronts to his research group. (AU)

Scientific publications

(10)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

BRANDOLI, BRUNO; DE GEUS, ANDRE R.; SOUZA, JEFFERSON R.; SPADON, GABRIEL; SOARES, AMILCAR; RODRIGUES, JR., JOSE F.; KOMOROWSKI, JERZY; MATWIN, STAN. Aircraft Fuselage Corrosion Detection Using Artificial Intelligence. SENSORS, v. 21, n. 12, JUN 2021. (17/08376-0, 18/17620-5, 19/04461-9, 20/07200-9, 16/17078-0, 14/25337-0)

RODRIGUES-, JR., JOSE F.; GUTIERREZ, MARCO A.; SPADON, GABRIEL; BRANDOLI, BRUNO; AMER-YAHIA, SIHEM. LIG-Doctor: Efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks. INFORMATION SCIENCES, v. 545, p. 813-827, FEB 4 2021. (18/17620-5, 16/17078-0, 17/08376-0)

RODRIGUES-JR, JOSE F.; SPADON, GABRIEL; BRANDOLI, BRUNO; AMER-YAHIA, SIHEM; DEHERRERA, AGS; GONZALEZ, AR; SANTOSH, KC; TEMESGEN, Z; KANE, B; SODA, P. Lig-Doctor: real-world clinical prognosis using a bi-directional neural network. 2020 IEEE 33RD INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS(CBMS 2020), v. N/A, p. 4-pg., 2020-01-01. (16/17078-0, 18/17620-5, 17/08376-0)

SPADON, GABRIEL; HONG, SHENDA; BRANDOLI, BRUNO; MATWIN, STAN; RODRIGUES-JR, JOSE F.; SUN, JIMENG. Pay Attention to Evolution: Time Series Forecasting With Deep Graph-Evolution Learning. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 44, n. 9, p. 17-pg., 2022-09-01. (16/17078-0, 20/07200-9, 18/17620-5, 17/08376-0, 14/25337-0, 19/04461-9)

RODRIGUES, JOSE F., JR.; PEPIN, JEAN-LOUIS; GOEURIOT, LORRAINE; AMER-YAHIA, SIHEM; ASSOC COMP MACHINERY. An Extensive Investigation of Machine Learning Techniques for Sleep Apnea Screening. CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, v. N/A, p. 8-pg., 2020-01-01. (18/17620-5, 16/17078-0)

FLOREZ, ALEXANDER Y. C.; SCABORA, LUCAS; ELER, DANILO M.; RODRIGUES-JR, JOSE F.; ALMEIDA, JR; GONZALEZ, AR; SHEN, L; KANE, B; TRAINA, A; SODA, P; et al. APEHR: Automated Prognosis in Electronic Health Records using multi-head self-attention. 2021 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), v. N/A, p. 6-pg., 2021-01-01. (16/17330-1, 18/17620-5, 16/17078-0)

RODRIGUES-JR, JOSE F.; BRANDOLI, BRUNO; AMER-YAHIA, SIHEM; DEHERRERA, AGS; GONZALEZ, AR; SANTOSH, KC; TEMESGEN, Z; KANE, B; SODA, P. DermaDL: advanced Convolutional Neural Networks for automated melanoma detection. 2020 IEEE 33RD INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS(CBMS 2020), v. N/A, p. 6-pg., 2020-01-01. (18/17620-5, 16/17078-0)

ZAGHIR, JAMIL; RODRIGUES-JR, JOSE F.; GOEURIOT, LORRAINE; AMER-YAHIA, SIHEM. Real-world Patient Trajectory Prediction from Clinical Notes Using Artificial Neural Networks and UMLS-Based Extraction of Concepts. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH, JUN 2021. (18/17620-5, 16/17078-0)

BRANDOLI, BRUNO; SPADON, GABRIEL; ESAU, TRAVIS; HENNESSY, PATRICK; CARVALHO, ANDRE C. P. L.; AMER-YAHIA, SIHEM; RODRIGUES, JR., JOSE F.. DropLeaf: A precision farming smartphone tool for real-time quantification of pesticide application coverage. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 180, JAN 2021. (19/04461-9, 17/08376-0, 13/07375-0, 18/17620-5)

SCABORA, LUCAS C.; SPADON, GABRIEL; OLIVEIRA, PAULO H.; RODRIGUES-JR, JOSE F.; TRAINA-JR, CAETANO; ACM. Enhancing recursive graph querying on RDBMS with data clustering approaches. PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), v. N/A, p. 8-pg., 2020-01-01. (16/17078-0, 16/17330-1, 18/17620-5, 18/20360-5, 17/08376-0, 19/04461-9)