| Date | Topic | Reading | Speaker(s) | Notes |
| Feb 11 | Introduction + Administrivia | None. Handouts: Some online; Some will be distributed. | Prof. Shatkay | Class Notes 1 |
| Feb 13 | Intro to Machine Learning Methods, 1 | Parts of Ch. 18: Russell and Norvig. (HO) | Prof. Shatkay | |
| Feb 18 |
Guest Lecture: On DNA, RNA, Genes and Proteins in Practice |
Molecular Biology for Computer Scientists by Larry Hunter Uniqueness of Medical Data Mining by Cios and Moore |
Guest Lecturer: Prof. Carl Schmidt (Dept. of Animal and Food Sciences) | |
| Feb 20 | Intro to Machine Learning Methods, 2 | Parts of Ch. 6: Duda & Hart. (HO) | Prof. Shatkay | |
| Feb 25 | Introductory Odds and Ends | Prof. Shatkay | ||
| Feb 27 | On Talks and Seminar | Your Favorite Topic + HO2 | All | |
| Mar 3 |
Mixture Models and Probabilistic Clustering: AutoClass |
AutoClass: A Bayesian Classification System. Cheeseman et al., 1988. Helpful Reference: Bayesian classification (AutoClass): Theory and Results. by Cheeseman & Stutz |
Michael | |
|
Still on Mar 3, 3:30PM Ewing 336 |
Guest Lecture: Patient-specific modeling for virtual treatment planning in pediatric cardiology |
Talk Abstract and Bio |
Prof. Alison Marsden, Cardiovascular Biomechanics Computation Lab, Stanford | |
| Mar 5 | Gene Expression Clusters: Clinical Implications |
Clustering Cancer Gene Expression Data: A Comparative Study by de Souto et al. Helpful References: Molecular Classification of Cancer... by Golub, Slonim, Tamayo et al. , Science, 1999 Getting Started with Gene Expression Microarray Analysis Slonim & Yanai |
Guna | |
| Mar 10 | Clinical Data Clustering and Subtyping (1): Autism Spectrum Disorders |
Comorbidity Clusters in Autism Spectrum Disorders: Electronic Health Record Time-Series Analysis
by Doshi-Velez, Ge & Kohane, Pediatrics, 2014 Secondary Papers: Using Cluster Ensemble and Validation to Identify Subtypes of Pervasive Developmental Disorders by Shen et al. , AMIA, 2007 A Cluster Analysis of Challenging Behaviors in Autism Spectrum Disorder by Stevens et al , ICMLA, 2017. |
Hamed | |
| Mar 12 - Mar 25 | COVID19 Induced Spring Break - No Classes | |||
| Mar 31 | Classifiers: Networks and Convolutional NN. |
Learning Algorithms for Classification: A Comparison of Handwritten Digit Recognition (1995). LeCun et al. Secondary paper: Dermatologist-level Classification of Skin Cancer with Deep Neural Networks. Esteva et al. Nature, 2017. | Mayara | |
| Apr 2 | More Clinical Applications of Networks and Convolutional NN. |
A
Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records. Dong, Rashidian et al. Helpful References: Predicting opioid dependence from electronic health records with machine learning Ellis, Wang et al. |
Mauricio | |
| Apr 7 | Applications of vital signal and sequence analysis |
Rhythmic Sonic Feedback for Speed Skating by Real-Time Movement Synchronization by Godbout & Boyd Secondary paper: Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data... by Schulam et al., AAAI, 2015 Helpful References: Audio Visual Synchronization of Rhythm by Godbout & Boyd Subtyping: What It Is and Its Role in Precision Medicine by Saria and Goldberg, IEEE Intelligent Systems, 2015 Using Unsupervised Learning to Determine Risk Level for Left Ventricular Diastolic Dysfunction by Ma et al., BIBM, 2014 |
Jaysheel | |
| Apr 9 |
Guest Lecture: Introduction to the Clinical Time Series Data Analysis |
Making Sense of the ECG (Ch 1) by Houghton & Gray (Handout) |
Dr. John Zaleski, Chief Analytics Officer, Bernoulli Health | |
| Apr 14 | Introduction to Support Vector Machines. |
A
Primer on Kernel Methods. Vert, Tsuda, Scholkopf Helpful References: A Tutorial on Support Vector Machines for Pattern Recognition Burges A Gentle Introduction to Support Vector Machines in Biomedicine by Statnikov et al. |
Mina | |
| Apr 16 | Introduction to Probabilistic Graphical Models: Hidden Markov Models and Bayesian Networks |
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Rabiner Handouts: S. Russel & P. Norvig, AI a Modern Approach, Ch 14.1-14.4 + (optional) F. Jenssen, Introduction to Bayesian Netwroks Helpful References: An Introduction to Hidden Markov Models by Juang & Rabiner |
Prof. Shatkay | Class Notes 2 |
| Apr 21 | Cardiovascular Signal Clustering |
Clustering and Sybmbolic Analysis of Cardiovascular Signals...
by Syed, Guttag & Stultz Helpful References: Making Sense of the ECG (Ch 1) by Houghton & Gray (Handout) A Framework for the Analysis of Acoustical Cardiac Signals by Syed et al. Approximate Queries and Representations for Large Data Sequences Shatkay and Zdonik, ICDE, 1996. |
Jaysheel | Project Proposals Due |
| Apr 23 | Cardio Signal Classification: Heartbeat and ECG Categorization |
Identifying Hypertrophic Cardiomyopathy Patients.... Rahman et al, BIBM, 2014.
Secondary paper: Time Series Classification With Support Vector Machines Kampouraki et al Helpful References: Making Sense of the ECG (Ch 1) by Houghton & Gray (Handout) ECG Beat Classification by Using Discrete Wavelet Transform and Random Forest... Emanet |
Hamed | |
| Apr 28 : Date/Hour may change | Protein Subcellular Location Prediction |
MultiLoc: Prediction of Protein Subcellular Localization Using.... Hoeglund et al
Secondary, Basis paper: Predicting Sub cellular Localization of Proteins based on their...Emanuelsson, et al Helpful References: A Knowledge Base for Predicting Protein Localization..., 1992. Nakai & Kanehisa. (HO7) Better Prediction of Protein Cellular Localization Sites with the K Nearest.... Horton & Nakai Prediction of Protein Subcellular Locations by Support Vector Machines Using Compositions of Amino Acids and Amino Acid Pairs Park & Kanehisa |
Mayara | |
| Apr 30 : Date/Hour may change |
Guest Lecture: Introduction to the Electronic Health Records |
Dr. Claudine Jurkovitz, MD, MPH, Director of Clinical Research, Value Institute, Christiana Care. | ||
| May 5 | Markov Models Applications: Post-operation Complications |
Modeling the Temporal Evolution of Postoperative Complications. Feld et al. AMIA, 2016 Helpful References: Markov Models in Medical Decision Making: A Practical Guide. Sonnenberg & Beck, Medical Decision Making, 1993 |
Mauricio | |
| May 7 | HMM Applications: Prostate Cancer Detection |
Prostate Cancer: Improved Detection by Temporal Analysis of Radio-Frequency Ultrasound... Nahlawi et al ,
MICCAI, 2016 Helpful References: Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties. Nahlawi et al , IEEE Trans. BioMed Eng, 2017 Augmenting Detection of Prostate Cancer in Transrectal Ultrasound Images Moradi et al IEEE Trans. BioMed Eng, 2009 |
Mina | |
| May 12 | Clinical Applications of Bayesian Networks |
The design and evaluation of a Bayesian system for detecting and characterizing outbreaks of influenza by N. Millet et al., OJPHI, 2019 Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes by Y. Xue, G.Cooper et al., Nature Research Scientific Reports, 2019 |
Michael | |
| May 14 | Biomedical Text Mining and Language Models |
Mining the Biomedical Literature, Ch. 1. Shatkay & Craven (2012). (HO) Frontiers of Biomedical Text Mining: Current Progress Zweignebaum et al. Optional Reading: Text as Data: Using Text-Based Features for Proteins Representation and for Computational Prediction of their Characteristics. Shatkay, Brady & Wong. Extracting Information from Textual Documents in the Electronic Health Record... Meystre et al. 2008 Co-occurrence of Medical Conditions: Exposing Patterns through Probabilistic Topic Modeling... by M. Bhattacharya et al. , 2018. Helpful References: Finding Themes in Medline Documents:... , Shatkay and Wilbur, 2000 Introduction to Probabilistic Topic Models. by D. Blei, CACM, 2012 SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and... by Shatkay, Hoeglund et al , 2007 Constructing Biological Knowledge Bases by Extracting Information from Text, by M. Craven & J. Kumlien, 1999 |
Prof. Shatkay | |
| Project Reports Due May 21 by Midnight. |