Browsing by Author "Miled, Zina Ben"

Now showing 1 - 8 of 8
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    Attached Learning Model for First Digital System Design Course in ECE Program
    (American Society for Engineering Education, 2016-06) Shayesteh, Seemein; Rizkalla, Maher E.; Christopher, Lauren; Miled, Zina Ben; Department of Electrical and Computer Engineering, School of Engineering and Technology
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    Automated Assessment of Psychiatric Patients Using Medical Notes
    (2022-12) Wang, Shuo; Miled, Zina Ben; King, Brain; Lee, John
    Psychiatric patients require continuous monitoring on par with their severity status. Unfortunately, current assessment instruments are often time-consuming. The present thesis introduces several passive digital markers (PDMs) that can help reduce this burden by automating the assessment using medical notes. The methodology leverages medical notes already annotated according to the General Assessment of Functioning (GAF) scale to develop a disease severity PDM for schizophrenia, bipolar type I or mixed bipolar and non-psychotic patients. Topic words that are representative of three disease severity levels (severe impairment, serious impairment, moderate to no impairment) are identified and the top 50 words from each severity level are used to summarize the raw text of the medical notes. The summary of the text is processed by a classifier that generates a disease severity level. Two classifiers are considered: BERT PDM and Clinical BERT PDM. The evaluation of these classifiers showed that the BERT PDM delivered the best performance. The PDMs developed using the BERT PDM can assign medical notes from each encounter to a severe impairment level with a positive predictive value higher than 0.84. These PDMs are generalizable and their development was facilitated by the availability of a substantial number of medical notes from multiple institutions that were annotated by several health care providers. The methodology introduced in the present thesis can support the automated monitoring of the progression of the disease severity for psychiatric patients by digitally processing the medical note produced at each encounter without additional burden on the health care system. Applying the same methodology to other diseases is possible subject to availability of the necessary data.
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    Comparing PSO-based clustering over contextual vector embeddings to modern topic modeling
    (Elsevier, 2022-05) Miles, Samuel; Yao, Lixia; Meng, Weilin; Black, Christopher M.; Miled, Zina Ben; Electrical and Computer Engineering, School of Engineering and Technology
    Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In the present paper, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on two datasets. The first dataset consists of posts from the online health forum r/Cancer and the second dataset is a standard benchmark for topic modeling which consists of a collection of messages posted to 20 different news groups. When compared to the state-of-the-art generative document models (i.e., ETM and NVDM), pPSO is able to produce interpretable clusters. The results indicate that pPSO is able to capture both common topics as well as emergent topics. Moreover, the topic coherence of pPSO is comparable to that of ETM and its topic diversity is comparable to NVDM. The assignment parity of pPSO on a document completion task exceeded 90% for the 20NewsGroups dataset. This rate drops to approximately 30% when pPSO is applied to the same Skip-Gram embedding derived from a limited, corpus-specific vocabulary which is used by ETM and NVDM.
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    A Novel Conceptual Architecture for Person-Centered Health Records
    (American Medical Informatics Association, 2017-02-10) Schleyer, Titus; King, Zachary; Miled, Zina Ben; Department of Medicine, IU School of Medicine
    Personal health records available to patients today suffer from multiple limitations, such as information fragmentation, a one-size-fits-all approach and a focus on data gathered over time and by institution rather than health conditions. This makes it difficult for patients to effectively manage their health, for these data to be enriched with relevant information from external sources and for clinicians to support them in that endeavor. We propose a novel conceptual architecture for person-centered health record information systems that transcends many of these limitations and capitalizes on the emerging trend of socially-driven information systems. Our proposed personal health record system is personalized on demand to the conditions of each individual patient; organized to facilitate the tracking and review of the patient's conditions; and able to support patient-community interactions, thereby promoting community engagement in scientific studies, facilitating preventive medicine, and accelerating the translation of research findings.
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    Peer-to-Peer Personal Health Record
    (2019-08) Horne, William Connor; Miled, Zina Ben; Christopher, Lauren; Rizkalla, Maher
    Patients and providers need to exchange medical records. Electronic Health Records and Health Information Exchanges leave a patient’s health record fragmented and controlled by the provider. This thesis proposes a Peer-to-Peer Personal Health Record network that can be extended with third-party services. This design enables patient control of health records and the tracing of exchanges. Additionally, as a demonstration of the functionality of a potential third-party, a Hypertension Predictor is developed using MEPS data and deployed as a service in the proposed framework.
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    Predicting body mass index in early childhood using data from the first 1000 days
    (Springer Nature, 2023-05-31) Cheng, Erika R.; Cengiz, Ahmet Yahya; Miled, Zina Ben; Pediatrics, School of Medicine
    Few existing efforts to predict childhood obesity have included risk factors across the prenatal and early infancy periods, despite evidence that the first 1000 days is critical for obesity prevention. In this study, we employed machine learning techniques to understand the influence of factors in the first 1000 days on body mass index (BMI) values during childhood. We used LASSO regression to identify 13 features in addition to historical weight, height, and BMI that were relevant to childhood obesity. We then developed prediction models based on support vector regression with fivefold cross validation, estimating BMI for three time periods: 30-36 (N = 4204), 36-42 (N = 4130), and 42-48 (N = 2880) months. Our models were developed using 80% of the patients from each period. When tested on the remaining 20% of the patients, the models predicted children's BMI with high accuracy (mean average error [standard deviation] = 0.96[0.02] at 30-36 months, 0.98 [0.03] at 36-42 months, and 1.00 [0.02] at 42-48 months) and can be used to support clinical and public health efforts focused on obesity prevention in early life.
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    Social Media Sensing Framework for Population Health
    (IEEE, 2019) Esperanca, Alvaro; Miled, Zina Ben; Mahoui, Malika; Electrical and Computer Engineering, School of Engineering and Technology
    Conducting large health population studies is expensive. For instance, collecting field information about the efficacy of health campaigns or the impact of a disease may require the involvement of many health providers over an extended period of time and sometimes may not reach the target population. In fact, due to the aforementioned difficulties, health-related population statistics may be unavailable or lag by several years. Recently, social media networks have emerged as a source of sensory data for various aspects of social behavior. This source of information is used to drive marketing campaigns, conduct threat analysis and profile groups of individuals among numerous other applications. However, these applications are usually limited to specific case studies and do not provide a systematic approach to translating social media data into knowledge. In this paper, we propose a framework that can extract knowledge from social media networks in support of large scale health studies. The framework consists of an automated workflow designed to collect data from social media platforms, filter the data based on geographical criteria, and extract information relevant to a target hypothesis. The framework is demonstrated in the case of mortality and incidence of three chronic diseases, namely asthma, cancer, and diabetes. Twitter data is extracted over the period 2010 to 2015 for each target geographical region and classified based on its relevance to each of the aforementioned diseases. Due to the large number of extracted records, a simple random sampling approach is used to support the supervised training and testing of the classifier in the framework. Despite the limited number of records used for the training of the classifiers as a result of this approach, high classification accuracies are achieved for all three diseases. While the focus of the case studies in this paper is on the three chronic diseases asthma, diabetes and cancer, the utility of the proposed framework extends to other areas in the health sector. The proposed framework can help automate data-driven hypothesis validation for social media health-related studies. This paper describes the underlying methodology as well as the limitations associated with using social media data as a sensor for trends in population health.
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    A Water Demand Prediction Model for Central Indiana
    (AAAI, 2018) Shah, Setu; Hosseini, Mahmood; Miled, Zina Ben; Shafer, Rebecca; Berube, Steve; Electrical and Computer Engineering, School of Engineering and Technology
    Due to the limited natural water resources and the increase in population, managing water consumption is becoming an increasingly important subject worldwide. In this paper, we present and compare different machine learning models that are able to predict water demand for Central Indiana. The models are developed for two different time scales: daily and monthly. The input features for the proposed model include weather conditions (temperature, rainfall, snow), social features (holiday, median income), date (day of the year, month), and operational features (number of customers, previous water demand levels). The importance of these input features as accurate predictors is investigated. The results show that daily and monthly models based on recurrent neural networks produced the best results with an average error in prediction of 1.69% and 2.29%, respectively for 2016. These models achieve a high accuracy with a limited set of input features.