United States Patent
`Schauss et al.
`
`[19]
`
`[54] MEDICAL DIAGNOSTIC ANALYSIS SYSTEM
`
`[75] Inventors: Mark A. Schauss, Incline Village,
`Nev.; Patricia Kane, Millville, NJ.
`
`[73] Assignee: Carbon Based Corporation, Incline
`Village, Nev.
`
`[21] Appl. No.: 08/620,385
`[22]
`Filed:
`Mar. 22, 1996
`
`Related US. Application Data
`
`[63]
`
`Continuation-in-part of application No. 08/568,752, Dec. 7,
`1995, Pat. NO. 5,746,204.
`
`[51] Int. Cl.7 ...................................................... .. A61B 5/00
`[52] US. Cl. ........................................... .. 600/300; 128/923
`[58] Field of Search ................................... .. 128/630, 920,
`128/921, 923, 924, 898, 670; 600/300
`
`[56]
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`9/1981 Sinay .
`4,290,114
`3/1988 Suto et al. .
`4,731,725
`3/1988 Potter et al. .
`4,733,354
`6/1991 Adrion et al. .
`5,023,785
`4/1993 Zimmerman et al. .
`5,199,439
`5,255,187 10/1993 Sorensen.
`5,404,292
`4/1995 Hendrickson.
`5,437,278
`8/1995 Wilk.
`5,463,548 10/1995 Asada et al. .
`5,551,436
`9/1996 Yago ..................................... .. 128/670
`5,594,638
`1/1997 Lliff.
`5,618,729
`4/1997 IZraelevitZ et al. ................ .. 435/2887
`5,642,731
`7/1997 Kelli ...................................... .. 128/630
`
`OTHER PUBLICATIONS
`
`Brief ?led Mar., 1998 in Civil Action, Kane et al. vs. Carbon
`Based Corporation, No. CUM—L001368—97(S.P. Ct.
`NS,Cumberland County).
`Certi?cation of Kent Myles ?led Mar., 1998 in Civil Action,
`Kane et
`al.
`vs Carbon Based Corporation,No.
`CUM—L001368—97(S.P. Ct. NS, Cumberland County)(EX
`hibit A from Brief
`
`US006063026A
`Patent Number:
`Date of Patent:
`
`[11]
`[45]
`
`6,063,026
`May 16,2000
`
`Certi?cationof Timothy Cunninghamm ?led Mar., 1998 in
`Civil Action, Kane et al. vs. Carbon Based Corporation,No.
`CUM—L001368—97 (S.P. Ct. NS, Cumberland County)(EX
`hibit A from Brief
`Supplemental Certi?cation of EdWard Kane ?led Mar., 1998
`in Civil Action, Kane et al. vs. Carbon Based Corporation,
`No. CUM—L001368—97 (S.P. Ct. NS, Cumberland County).
`Lendon H. Smith, Feed Your Body Right, pp. 114—171,
`184—189, 1994, M.Evans and Company, Inc., NeW York
`(Exhibit A from Supplemental Certi?cation of EdWard Kane
`
`Supplemental Certi?cation of Patricia Kane ?led Mar., 1998
`in Civil Action, Kane et al. vs. Carbon Based Corporation,
`No. CUM—L001368—97 (S.P. Ct. NS, Cumberland County).
`
`(List continued on neXt page.)
`
`Primary Examiner—Samuel G. Gilbert
`Attorney, Agent, or Firm—Robert O. Guillot
`
`[57]
`
`ABSTRACT
`
`The present invention is a computerized medical diagnostic
`method. It includes a ?rst database containing a correlation
`of a plurality of diseases With a plurality of indicators
`associated With each such disease. A second database
`includes human experience test results associated With each
`indicator. An individual’s test results are then compared With
`the second database data to determine presence levels for
`each indicator. Thereafter the presence levels are compared
`With the data in the ?rst database to provide a pattern
`matching determination of diseases associated With the
`various indicator presence levels.
`
`The presence level indicators for an individual may be
`affected by many environmental and/or personal factors
`such as age, seX, race, pregnancy, residence location, pre
`vious or current diseases, previous or current drug usage,
`etc., all of Which are factors to be considered in creating an
`accurate analysis system. The present invention provides a
`method for correlating such factors With the various test
`indicators to identify therapeutic and/or contraindicated
`treatments and drugs.
`
`38 Claims, 9 Drawing Sheets
`
`Cmrrelaic Diseases
`mac )Wlih
`Disease Indicators
`(1.2.3 12 )
`
`climpeie Presence Levels
`(nun) for Indicators (s)
`with Disease Indicator
`Data (1) m oeieniilne
`Diwase Pattern Match
`RktsulLv'
`
`Correlate Human FYpericl-lce
`ofliidieeleie with Renee
`and Mean ol'Eacb Indicator
`(1,2, 1. l2.)
`
`Compare Results (i) ii llll
`Human E
`(2)
`>—— all InLhLaluXS m Determine
`Yemen: Status
`
`_
`
`r
`
`if
`
`DEtEImInC Presence Luci
`(IND) 6f llzch li-ldlemor
`Percent Slams
`
`CFAD VI 1004-0001
`
`

`
`6,063,026
`Page 2
`
`OTHER PUBLICATIONS
`
`Fischbach et al., Manual ofLaboratory and diagnostic Tests,
`pp. I—III,4—22,4—23,276,277,1992J.B.LippincottCornpany,
`Pennsylvania.
`Young,DonaldS., E?rects of Drugs on Clinical Laboratory
`Tests, pp. I,3—236,3—237,1995, AACC Press, Fourth Edition,
`WashingtonD.C.
`Young, Donald S., E?rects of Preanalytical Variables on
`Clinical Laboratory Tests, pp. I, II, 4—472, 4—473, 1996,
`AACC Press, Second Edition, Washing D.C.
`Health Equations, Blood Test Evaluation of Patricia Kenney
`on Mar. 23, 1995 (2 pages).
`
`Blood Test Evaluation Copyright 1988 by Life Balances,
`Inc. (2 pages).
`Friedman, Richard B. et al.,E?”ects of Disease on Clinical
`Laboratory Tests, p. 4—111, .1989, AACC Press, Washin
`gonD.C.
`Young, Donald S.,E?”ects of Preanalytical Variables on
`Clinical Laboratory Tests, pp. 3—287,1993,AACCPRESS,
`First Edition, WashingD.C.
`Young, Donald S., E?rects of Drugs on Clinical Laboratory
`Tests, pp. 4—57,1995, AACC Press, Fourth Edition, Wash
`ington D.C.
`
`CFAD VI 1004-0002
`
`

`
`U.S. Patent
`
`May 16, 2000
`
`Sheet 1 0f 9
`
`6,063,026
`
`Correlate Diseases
`(A,B,C. . .) with
`Disease Indicators _\ 1
`(l,2,3...l2...)
`
`/
`
`6
`J
`
`Compare Presence Levels
`(I,N,D) for Indicators (5)
`with Disease Indicator
`Data (1) to Determine
`Disease Pattern Match
`Results
`
`/\
`
`2
`/
`Correlate Human Experience
`of Indicators with Range
`and Mean of Each Indicator
`(1,2,3...12...)
`
`3
`j
`
`Develop Personal Test
`Results for Each
`Indicator (1, 2, 3...l2...)
`
`4
`
`Compare Results (3) with
`Human Experience (2) for
`L,———— all Indicators to Determine
`Percent Status
`
`/
`
`J 5
`
`Determine Presence Level
`(I,N,D) of Each Indicator
`Percent Status
`
`FIG. 1
`
`CFAD VI 1004-0003
`
`

`
`U.S. Patent
`
`May 16, 2000
`
`Sheet 2 0f 9
`
`6,063,026
`
`1
`
`2
`
`6
`
`j
`
`3
`
`/
`
`4
`
`\
`
`5
`
`1O
`\
`PANEL A
`
`12
`
`\ \
`PANEL B
`
`14
`
`\ \
`PANEL C
`
`13
`
`\
`
`FIG. 2
`
`CFAD VI 1004-0004
`
`

`
`U.S. Patent
`
`May 16, 2000
`
`Sheet 3 0f 9
`
`6,063,026
`
`I
`
`6
`
`I
`I
`|
`I
`I
`I
`I
`I
`I
`I
`I
`I
`I
`l
`I
`I
`|
`I
`
`2
`
`3
`
`,
`
`4
`
`/
`5
`
`1
`
`6A
`J
`
`I
`|
`|
`I
`7
`I
`I/
`I
`I
`I
`I
`I
`I
`I
`|
`I
`I
`I
`I
`
`I
`I
`I
`I
`I
`|
`|
`7A\-I
`I
`I
`I
`I
`I
`I
`I
`I
`I
`I
`
`2
`
`I
`I
`|
`__I I
`I
`M I
`I
`I
`I
`I
`I
`I
`I
`I
`I
`I
`M I
`I
`I
`
`I
`4A
`
`15 \j
`
`COMPARE DISEASE PATTERN MATCH
`RESULTS OF FIRST DATE (6) WITH
`DISEASE PATTERN MATCH RESULTS OF
`LATER DATE (6A)
`
`16
`I
`I
`IDENTIFY CHANGES IN DISEASE PATTERN
`MATCH RESULTS
`
`FIG. 3
`
`CFAD VI 1004-0005
`
`

`
`U.S. Patent
`
`May 16, 2000
`
`Sheet 4 0f 9
`
`6,063,026
`
`10A
`
`12A
`
`14A
`
`18]
`,
`COMPARE PANEL A %
`STATUS RESULTS OF FIRST
`DATE (10) WITH PANEL A
`% STATUS RESULTS OF
`SECOND DATE (10A)
`
`22)
`
`V
`COMPARE 12
`AND 12A RESULTS
`
`/
`IDENTIFY CHANGES
`IN % STATUS POR
`EACH INDICATOR AND
`CHANGES TN DEvIATIATION
`AND SKEW
`
`\4
`2352551;
`
`\
`24
`
`_\ 7
`-0
`
`FIG 4
`
`/
`COMPARE 14
`AND 14A
`RESULTS
`
`_\
`26
`
`IDENTIPY \
`CHANGES
`, 8
`‘
`
`CFAD VI 1004-0006
`
`

`
`U.S. Patent
`
`May 16, 2000
`
`Sheet 5 0f 9
`
`6,063,026
`
`30]
`
`CORRELATE EFFECTS OF
`DRUGS (a, b, c. . .)
`WITH DISEASE INDICATORS
`(1, 2, 3 _ _ _ 12_ _ .)
`
`I
`
`32
`
`]
`
`COMPARE ABNORMAL PRESENCE
`LEVELS (I, D) (5) WITH KNOWN
`, DRUG EFFECTS (30) THAT CAN
`CAUSE, AGGRAVATE, ABNORMAL
`PRESENCE LEVELS
`
`I
`
`34
`
`J
`
`IDENTIFY SPECIFIC DRUGS
`WITH HIGH REPORT INCIDENCE
`
`FIG. 5
`
`CFAD VI 1004-0007
`
`

`
`U.S. Patent
`
`May 16, 2000
`
`Sheet 6 0f 9
`
`6,063,026
`
`L0)
`
`J40
`
`CORRELATE EFFECTS 40 OF
`ELEMENTS (a1, bl, (:1...)
`WITH DISEASE INDICATORS
`(1,2, 3...12...)
`
`42
`
`COMPARE ABNORMAL PRESENCE
`LEVELS (I, D) (5) WITH KNOWN
`ELEMENT EFFECTS (40) THAT CAN
`ADJUST ABNORMAL
`PRESENCE LEVELS
`
`/44
`
`I
`
`IDENTIFY SPECIFIC ELEMENTS
`WITH HIGH REPORT INCIDENCE
`
`FIG. 6
`
`CFAD VI 1004-0008
`
`

`
`U.S. Patent
`
`May 16, 2000
`
`Sheet 7 0f 9
`
`6,063,026
`
`1
`
`2
`
`CORRELATE EFFECTS OF ENVIRONMENTAL
`FACTORS (a2, b2, c2...) WITH RANGE AND
`MEAN OF EACH INDICATOR (I, 2, 3...I2...)
`
`3
`
`6
`
`\
`
`5
`
`‘
`
`3
`
`DEVELOP PERSONAL DATABASE OF
`ENVIRONMENTAL FACTORS
`
`54
`J
`v
`COMPARE ENVIRONMENTAL RANGE
`ADJUSTMENTS (50) WITH PERSONAL
`DATABASE (52) TO IDENTIFY PERSONAL
`RANGE ADJUSTIVIENTS
`
`56
`j
`l
`COMPARE HUMAN EXPERIENCE RANGE (2)
`WITH PERSONAL RANGE ADJUSTMENTS
`(54) TO DETERMINE ADJUSTED RANGE
`AND MEAN FOR EACH INDICATOR (1, 2,
`3,. . . 12. . .)
`
`9
`COMPARE RESULTS (3) WITH ADJUSTED
`EXPERIENCE (56) FOR EACH INDICATOR
`(1, 2, 3,...12...) TO DETERMINE
`PERCENT STATUS
`
`FIG. 7
`
`CFAD VI 1004-0009
`
`

`
`U.S. Patent
`
`May 16, 2000
`
`Sheet 8 0f 9
`
`6,063,026
`
`2
`
`5O
`
`52
`
`V
`
`54
`
`\
`
`56
`
`/
`58
`
`3 __
`
`5
`
`4O
`
`42
`
`\
`
`44
`
`FIG. 8
`
`14
`
`30
`
`32
`
`\
`
`34
`
`CFAD VI 1004-0010
`
`

`
`U.S. Patent
`
`May 16, 2000
`
`Sheet 9 0f 9
`
`6,063,026
`
`om
`
`cm
`
`/ 1 ¢
`
`mmm NW
`
`/ r
`
`m? _ 3
`7 mm me P \ m
`9 m3 m5 m2 3 2 \v
`
`9% wiv a \_, \ a 9 ? @
`
`\
`
`‘r
`
`Em mm \ m2 imam j Q: Ma m
`
`
`mam m3 mom
`
`m3 m3 ,1, E. a A, m, 4
`
`Q 8 m2 A \7 e E % m2
`
`Q .0;
`
`9
`
`mg 2 m2 3 Q T 3 mm 2
`
`CFAD VI 1004-0011
`
`

`
`1
`MEDICAL DIAGNOSTIC ANALYSIS SYSTEM
`
`This application is a continuation-in-part of application
`Ser. No. 08/568,752 ?led on Dec. 7, 1995 now US. Pat. No.
`5,746,204.
`
`BACKGROUND OF THE INVENTION
`
`1. Field of the Invention
`The present invention relates generally to automated
`medical diagnosis systems, and more particularly to such
`systems Which compare patient diagnostic data With prede
`termined ranges of speci?c indicators to provide a speci?c
`disease diagnosis and suggested or contraindicated treat
`ment strategies.
`2. Description of the Prior Art
`Medical research in the second half of the 20th century
`has produced, and continues to produce, an ever increasing
`body of knowledge. The complexity and interrelationships
`of various diseases and the indicators that may be detected
`in various diagnostic tests for the diseases are more than
`sufficient to taX the capacity of most medical practitioners.
`To aid medical practitioners in disease diagnosis, comput
`eriZed eXpert systems have been, and are being developed to
`collate medical diagnostic data With various diseases to
`guide physicians in prescribing treatments for their patients.
`Such prior art medical diagnostic systems do not adequately
`provide an analytical framework for analyZing the individual
`patient’s diagnostic results to collate such results into a
`disease indicator pattern. Furthermore, such systems do not
`address therapeutic and/or contraindicated treatment strate
`gies.
`
`SUMMARY OF THE INVENTION
`
`The present invention is a computeriZed medical diag
`nostic method. It includes a ?rst database containing a
`correlation of a plurality of diseases With a plurality of
`indicators associated With each such disease. A second
`database includes human experience test results associated
`With each indicator. An individual’s test results are then
`compared With the second database data to determine pres
`ence levels for each indicator. Thereafter the presence levels
`are compared With the data in the ?rst database to provide a
`determination of disease pattern matches associated With the
`various indicator presence level.
`The presence level indicators for an individual may be
`affected by many environmental and/or personal factors
`such as age, seX, race, pregnancy, residence location, pre
`vious or current diseases, previous or current drug usage,
`etc., all of Which are factors to be considered in creating an
`accurate analysis system. The present invention provides a
`method for correlating such factors With the various test
`indicators to identify therapeutic and/or contraindicated
`treatments and drugs.
`It is an advantage of the present invention that it provides
`a method for automated analysis of an individual’s test
`results to provide increased accuracy in disease identi?ca
`tion.
`It is another advantage of the present invention that it
`provides increased accuracy in automated disease identi?
`cation systems by determining indicator presence levels for
`use in the disease identi?cation analysis.
`It is a further advantage of the present invention that it
`provides an automated medical diagnostic database system
`Wherein indicator test results for speci?c individuals are
`automatically categoriZed as increased, normal or decreased
`for increased accuracy in disease determination.
`It is yet another advantage of the present invention that it
`provides an automated medical diagnostic database system
`
`10
`
`15
`
`20
`
`25
`
`30
`
`35
`
`40
`
`45
`
`55
`
`60
`
`65
`
`6,063,026
`
`2
`Wherein indicator test results are combined in various panels
`to provide diagnostic information regarding various bodily
`conditions and functions.
`It is yet a further advantage of the present invention that
`it provides an automated medical diagnostic database system
`Wherein diagnostic data from a ?rst date and a second date
`can be compared to provide information regarding the
`change in an individual’s medical health and the effective
`ness of an ongoing medical treatment program.
`It is still another advantage of the present invention that
`it provides an automated medical diagnostic database system
`Wherein the knoWn effects of various drugs and other
`nutritional-biochemical elements can be utiliZed to better
`analyZe an individual’s health status, and to identify thera
`peutic and/or contraindicated drugs and elements.
`It is still a further advantage of the present invention that
`it provides an automated medical diagnostic database system
`Wherein the effects of personal and/or environmental factors
`such as age, seX, pregnancy, residence location, prior or
`current diseases and drug usage, may be utiliZed to provide
`a more accurate medical health analysis.
`These and other features and advantages of the present
`invention Will become Well understood upon reading the
`folloWing detailed description of the invention.
`
`IN THE DRAWINGS
`
`FIG. 1 is a block diagram of the basic disease pattern
`matching analytical method of the present invention.
`FIG. 2 is a block diagram shoWing the derivation of
`various panel status data results;
`FIG. 3 is a block diagram shoWing the comparison of
`disease pattern match results of tWo separate dates;
`FIG. 4 is a block diagram depicting the comparison of
`panel status data for tWo separate dates;
`FIG. 5 is a block diagram shoWing the incorporation of
`knoWn drug effect data With indicator status levels of the
`present invention;
`FIG. 6 is a block diagram shoWing the utiliZation of
`knoWn effects of nutritional-biochemical elements With indi
`cator levels;
`FIG. 7 is a block diagram shoWing the utiliZation of the
`knoWn effects of various personal and/or environmental
`factors With the diagnostic system of the present invention;
`FIG. 8 is a block diagram shoWing the incorporation of
`the various analytical methods of FIGS. 2, 5, 6 and 7 With
`the basic diagnostic method of FIG. 1; and
`FIG. 9 is a block diagram shoWing the analytical method
`depicted in FIG. 8 utiliZing individual test data from tWo
`separate dates and including data comparisons from those
`dates, including those shoWn in FIGS. 3 and 4.
`
`DETAILED DESCRIPTION OF THE
`PREFERRED EMBODIMENTS
`Generally, the basic system of the present invention
`involves the comparison of test results, typically from blood
`or other bodily ?uids of an individual With knoWn indicators
`for various diseases to determine the likelihood that an
`individual might have particular ones of the diseases. The
`method is basically accomplished in siX steps Which are
`depicted in FIG. 1 and described herebeloW.
`FIG. 1 is a schematic diagram setting forth the various
`steps in the analytical disease indication method of the
`present invention. As depicted therein, step 1 is the creation
`of a database for utiliZation Within a computer diagnostic
`system. The database is a correlation of various diseases,
`denoted generally as A, B, C .
`.
`.
`, With levels (Increased,
`Normal, Decreased) of various speci?c indicators, denoted
`generally as 1, 2, 3 .
`.
`. 12 .
`.
`. , in a computeriZed database.
`
`CFAD VI 1004-0012
`
`

`
`6,063,026
`
`3
`Table 1 depicts the step 1 database relationship of various
`diseases (denoted A, B, C .
`.
`. With known indicators for the
`particular disease (denoted 1, 2, 3 .
`.
`. 12). It is seen that
`various ones of the indicators in increased (I), normal (N) or
`decreased (D) levels are associated With various ones of the
`diseases.
`
`TABLE 1
`
`DISEASE (A, B, c, .
`
`.
`
`. )
`
`INDICATORS
`(1, 2, 3, 4, 5, 6, 7, s, 9, 10, 11, 12, .
`
`.
`
`.
`
`10
`
`A
`B
`
`H, 2D, 7D, 9I, 1OI
`1D, 3D, 6D, so, 1OI, 12I
`
`By Way of speci?c example, Table 2 describes three speci?c
`diseases, acute myocardial infarction, acquired hemolytic
`anemia and acromegaly, With related indicators. There are,
`of course, many diseases and several signi?cant indicators
`for each, and medical research daily discovers, neW diseases
`and derives neW indicators for particular diseases. Thus, step
`1 actually comprises a tabulation of knoWn medical research
`of diseases and the indicator levels indicative of those
`diseases.
`
`TABLE 2
`
`15
`
`20
`
`25
`
`4
`As depicted in FIG. 1, step 2 of the method of the present
`invention is the creation of a second database Which com
`prises a correlation of human diagnostic experience With
`each of the many indicators that are identi?ed in the database
`of step 1. In the preferred embodiment, the database of step
`2 includes a loW value, a high value and a mean value for
`each of the indicators.
`
`Table 3 represents the database of step 2, comprising the
`human experience values related to each of the indicators
`(1—12). Thus, the range of human experience for indicator 1
`reveals a loW of 0.9 units, a high of 2 units and a math
`ematical mean of 1.45 units.
`
`TABLE 3
`
`INDICATOR
`
`LOW
`
`HIGH
`
`MEAN
`
`1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`9
`
`3.5
`
`60
`
`4
`
`0
`
`0
`
`2
`
`2
`
`5
`
`415
`
`14
`
`3
`
`200
`
`1.3
`
`20
`
`1.45
`
`4.25
`
`237.5
`
`9
`
`1.5
`
`100
`
`.75
`
`14
`
`Indicators
`
`ACUTE MYOCARDIAL INFARCT ION
`
`Increased levels:
`
`Alkaline Phosphatase, Cholesterol, Creatinine,
`GGT, LDH, WBC, Neutrophils, Triglycerides, BUN,
`Uric Acid
`Total Bilirubin, Calcium
`Normal levels:
`Decreased levels: Albumin, Iron, Sodium
`ACQUIRED HEMOLYTIC ANEMIA (AUTOIMMUNE)
`
`Increased levels: SGOT, SGPT, Basophils, Total Bilirubin, Creatinine,
`LDH, Monocytes, Phosphorus, BUN, Uric Acid
`none
`
`Normal levels:
`Decreased levels:
`
`Hematocrit, Hemoglobin
`ACROMEGALY
`
`Increased levels:
`
`Normal levels:
`Decreased levels:
`
`Alkaline Phosphatase, Calcium, Creatinine, Glucose,
`Phosphorous, Potassium, Sodium, BUN
`none
`
`H0116
`
`30
`
`35
`
`40
`
`45
`
`8
`
`9
`
`10
`
`11
`
`12
`
`8
`
`6
`
`8.8
`
`1.3
`
`25
`
`10.1
`
`3.3
`
`15.5
`
`9.45
`
`2.3
`
`95
`
`105
`
`100
`
`Table 4 presents a typical tabulation of some knoWn indi
`cators With test results to provide added understanding by
`Way of speci?c example. These test results and human
`experience high, loW and mean are derived from knoWn in
`medical research, and step 2 thus comprises a database of
`knoWn medical research.
`
`INDICATOR
`
`1. A/G Ratio
`2. Albumin
`3. Alkaline Phosphatase
`4. Anion Gap
`5. Basophils
`6. Basophil Count
`7. Bilirubin, Total
`8. B.U.N.
`9. B.U.N./Creatinine
`Ratio
`10. Calcium
`11. Calcium/Phosphorus
`Ratio
`12. Chloride
`
`TABLE 4
`
`PRESENCE
`%
`RESULT LOW HIGH MEAN STATUS LEVEL
`
`1.71
`4.1
`114
`16.2
`0
`0
`0.5
`9
`18.00
`
`0.9
`3.5
`60
`4
`0
`0
`0.2
`8
`6
`
`2
`5
`415
`14
`3
`200
`1.3
`20
`25
`
`1.45
`4.25
`237.5
`9
`1.5
`100
`0.75
`14
`15.5
`
`23.48 N
`—10.00 N
`—34.79 D
`72.00 I
`—50.00 D
`—50.00 D
`—22.73 N
`—41.67 D
`13.16 N
`
`9.77
`2.69
`
`8.8
`1.3
`
`10.1
`3.3
`
`9.45
`2.3
`
`19.23 N
`19.72 N
`
`105
`
`95
`
`105
`
`100
`
`50.00 I
`
`CFAD VI 1004-0013
`
`

`
`6,063,026
`
`5
`
`TABLE 4-continued
`
`INDICATOR
`
`%
`PRESENCE
`RESULT LOW HIGH MEAN STATUS LEVEL
`
`10
`
`As depicted in FIG. 1, step 5 of the method of the present
`Returning to FIG. 1, step 3 of the method of the present
`invention is the further analysis of the results of step 4 to
`invention is the development of test results for a speci?c
`determine the degree of presence of the various indicators in
`individual. In the present invention, the individual test
`the speci?c individual’s test results. In the present invention,
`results are determined from testing blood, serum, urine or
`other bodily ?uids through medical laboratory facilities. The
`Where the percent status is greater than 25%, it is determined
`that an “increased level” (I) of that indicator is present.
`results are correlated in a third database Which includes the
`appropriate numerical values for each of the various indi
`Where the percent status value of an indicator is less than
`cators found in the databases of steps 1 and 2 hereabove.
`—25%, it is determined that a “decreased level” (D) of that
`Table 5 is a simple test result tabulation for a speci?c
`indicator is present. Where the percent status of an indicator
`individual as regards each of the indicators (1—12). These
`is betWeen —25% and +25%, it is determined that a “normal
`test results are the common output of a blood test, urine test, 20 level” (N) of that indicator is present in the individual’s test
`etc. With regard to the knoWn indicators. For further
`results. Table 6 includes the results of step 5, Wherein an “I”
`understanding, these test results are also presented in Table
`represents an increased level presence, an “N” represents a
`4.
`normal level presence and a “D” indicates a decreased level
`
`15
`
`TABLE 5
`
`PATIENT TEST RESULTS
`
`INDICATOR
`RESULT
`
`1
`1.71
`
`2
`4.1
`
`3
`114
`
`4
`16.2
`
`5
`0
`
`6 7
`0
`.5
`
`8
`9
`
`12
`11
`9 10
`18
`9.77 2.69 105
`
`As depicted in FIG. 1, step 4 of the method of the present
`invention is the computeriZed comparison of the individual’s
`indicator test results from the database developed in step 3
`With the human experience database for the indicators
`developed in step 2. The comparison of step 4 is conducted
`utiliZing the equation:
`
`35
`
`presence of the various indicators. For further
`understanding, the presence indicator results of step 5 (I, N
`or D) are also presented in Table 4.
`As depicted in FIG. 1, step 6 of the method of the present
`invention is the comparison of the indicator presence results
`of step 5 With the database of step 1. This correlation seeks
`to determine from the presence levels of various indicators
`40 in the individual’s test results (I, N or D), the likelihood that
`Result _ Mean
`% Status = % particular diseases identi?ed by the presence of speci?c
`Range (HIghTLOW)
`combinations of indicators are af?icting the individual. This
`likelihood is derived by determining hoW many “pattern
`malches exlst between ,the PreS?n‘?e1eVe1S(L N or D) of the
`This comparison yields a result denoted as “percent status”,
`which is a mathematical Value which expresses a compari_ 45 1nd1cator test results With the indicator data of the step 1
`son of the individual’s test results for a speci?c indicator
`database‘
`With the typical human experience test result values for that
`particular indicator. It is an indication of Where the indi-
`vidual’s test results fall in comparison With the human 5
`experience test results of Table 3. Table 6 represents the step 0
`4 comparison of the individual test results of Table 5 With the
`indicator statistics of Table 3 to derive a “percent status”
`according to the comparison equation presented above. For
`further understanding, the comparison results of step 4 (%
`status) are also presented in Table 4.
`
`TABLE 7
`DISEASE INDICATOR
`
`# INDICATORS
`5
`6
`5
`
`# MATCHES
`O
`4
`2
`
`% MATCH
`0%
`67%
`40%
`
`DISEASE
`A
`B
`C
`
`TABLE 6
`
`PRESENCE OF THE INDICATOR
`
`INDICATOR
`% STATUS
`PRESENCE
`LEVEL
`
`1
`23.4
`N
`
`2
`-10
`N
`
`5
`4
`3
`-34 72 -50
`D I
`D
`
`6
`-50
`D
`
`7
`-22
`N
`
`9 10 11 12
`8
`-41 13 19 19 50
`D N N N I
`
`CFAD VI 1004-0014
`
`

`
`7
`
`TABLE 7-continued
`
`DISEASE INDICATOR
`
`6,063,026
`
`8
`
`TABLE 9-continued
`
`Panel A
`
`Panel B
`
`Panel C
`
`DISEASE
`
`# INDICATORS
`
`# MATCHES
`
`% MATCH
`
`5 Indicator
`
`% Status Indicator
`
`% Status Indicator
`
`% Status
`
`10
`
`For instance, as depicted in Table 7, the presence levels (I,
`N or D) of the various indicators are compared with various
`diseases A, B, C, .
`.
`. from the step 1 database as shown in
`Table 1 to determine the degree to which any of the diseases
`are indicated by the matching of the presence levels of 15
`various indicators with the disease data. Thus, as set forth in
`Table 7, it is seen that disease B is very likely present
`because 4 of 6 of the indicator levels are matched, whereas
`diseases A and C are not as likely present because fewer of
`the indicators levels for these diseases are matched. Table 8
`is merely exempli?cative of a portion of a typical result
`tabulation that is similar to Table 7 for added understanding.
`
`20
`
`Deviation
`Skew
`
`Deviation
`17.91
`—9.23 Skew
`
`24.55 Deviation
`—14.08 Skew
`
`29.56
`3.78
`
`As depicted in FIG. 2 and shown in Table 9, panel A (see
`reference numeral 10) refers to a speci?c bodily condition or
`function, and information related to the panelAcondition or
`function is obtainable from a combined analysis of indica
`tors 1, 3, 18 and 32 (for example) wherein a percent status
`?gure from step 4 is utilized for each indicator. A math
`ematical data deviation (the average of percent status with
`out regard to the sign), and a data skew (the average of the
`percent status wherein the sign is taken into account), is
`calculated for each panel data set. The deviation and skew
`provide a numerical framework for referencing the status of
`the bodily condition or function of panel A. Also shown in
`Table 9 and depicted in FIG. 2 is a panel B (see reference
`
`TABLE 8
`
`DISEASE
`
`Anterior Pituitary
`Hypofunction
`Pernicious Anemia
`Vitamin C De?ciency
`Rheumatoid Arthritis
`Acute Myocardial Infarction
`
`PERCENT
`# OF
`# OF
`ICD-9 CODE MATCHES INDICATORS MATCH
`
`253.40
`
`281.00
`267.00
`714.00
`410.00
`
`5
`
`6
`3
`5
`5
`
`10
`
`15
`8
`15
`15
`
`50.00%
`
`40.00%
`37.50%
`33.33%
`33.33%
`
`Therefore, the basic method presented in FIG. 1 herein
`enables a medical practitioner to input a patient’s test results
`into a computerized system and have the system produce a
`listing of possible diseases that the patient may have based
`upon the variation between the individual’s test results and
`the known human experience results for various indicators.
`
`40
`
`FIG. 2 depicts a further usage of the percent status data 45
`that was developed in step 4 of the basic method depicted in
`FIG. 1, and described above. It is well known in medical
`research that various ones of the speci?c indicators, denoted
`generally as 1, 2, 3 .
`.
`. 12 .
`.
`. are useful for the analysis of 50
`certain bodily conditions and functions, and a database
`which references a particular condition or function is
`referred to herein as a panel. Table 9 presents hypothetical
`data for three panels (Panel A, Panel B and Panel C) of many
`contemplated panels.
`
`55
`
`numeral 12) which (for example) is represented by percent
`status data from indicators 3, 7, 8, 18, 47 and 85, with a
`deviation and skew being reported for panel B. Additionally,
`in Table 9 and in FIG. 2, a panel C (see reference panel 14)
`with indicators (for example 7, 12 and 71 with percent status
`data from step 4 and deviation and skew data) represents yet
`another bodily condition or function. Current medical
`knowledge teaches that many such bodily functions and
`conditions can be represented by data panels comprising a
`plurality of speci?c indicators, and while only panels A, B
`and C are shown in Table 9 and depicted in FIG. 2, arrow 13
`is presented in FIG. 2 to indicate that many more such panels
`are contemplated by the inventor and considered part of the
`present invention.
`Speci?c panels for bodily conditions and functions that
`are contemplated by the inventor include nitrogen status,
`electrolyte status, protein status, cardiac marker status, liver
`status, kidney function status, lipid status, allergy status,
`hematology status, leukocyte percentage differential status,
`blood element ratio status, leukocyte count status, acid PH
`0 indicator status, alkaline PH indicator status.
`By way of speci?c examples to further the comprehension
`of the present invention, Table 10 hereof presents the elec
`trolyte panel of an individual, the cardiac marker panel of
`the speci?c individual, the kidney function status panel of
`the individual and the blood elements ratio status panel of
`the individual.
`
`TABLE 9
`
`Panel A
`
`Panel B
`
`Panel C
`
`Indicator
`
`% Status Indicator
`
`% Status Indicator
`
`% Status
`
`1
`3
`18
`32
`
`3
`23.4
`7
`—34.
`8
`7.80
`—6.43 18
`47
`85
`
`7
`13
`71
`
`—34.
`—22
`—41
`7.80
`-1s.ss
`23.61
`
`—22.
`50.
`—16.66
`
`65
`
`CFAD VI 1004-0015
`
`

`
`9
`
`6,063,026
`
`INDICATOR
`
`TABLE 10
`Result
`
`% Status
`
`CO2
`
`22
`
`Sodium
`Potassium
`Chloride
`Calcium
`Phosphorus
`Panel Status Skew
`KIDNEY FUNCTION
`
`Panel Status Deviation
`
`-30.00
`
`10
`
`23.62
`
`10
`It is to be understood that other and further panels as
`identi?ed above are Within the contemplation of the inventor
`and 'Will be liIlOWIl'tO ‘those skilled in the art, and that
`5 medical research daily identi?es other panels and further
`w indicators that are suitable for usage in the various panels
`139
`-10.00
`that may be derived utilizing the present invention.
`4.2
`—12.50
`105
`5000
`97
`1923
`3.6
`—20.00
`0.54
`
`.
`
`.
`
`.
`
`.
`
`The present invention contemplates the comparison of
`analytical test results data developed for an individual on a
`?rst date With test results data developed for the individual
`at a later date, in order to determine changes ‘In the'indi
`vidual’s medical condition. FIG. 3 is a schematic depiction
`
`-
`
`-
`
`-
`
`-
`
`-
`
`Creatinine
`
`B U N
`pilogpl'loms
`Cholesterol
`Uric Acid
`Calcium
`LDH
`Total PfOtem
`Globulin
`A/G Ratio
`Panel Status Devlanon
`Panel Status Skew
`
`Albumin
`
`0.5
`
`9 O
`3:6
`181
`41
`9_7
`414
`6'5
`Z4
`1-7
`
`4.1
`
`RATIO’S
`
`0.00
`
`41 67
`_2O:OO
`—1721
`26.00
`1923
`—31-95
`_3O'OO
`_6O_OO
`23-48
`25'41
`—12.92
`
`—10.00
`
`BUN/Cream“?
`Sodium/Potassium
`Calcium/Phosphorus
`A/G Ratio
`Amon Gap
`
`_
`
`_
`
`1800
`33.10
`269
`1-71
`1620
`
`13'16
`9.13
`1972
`23-48
`7200
`
`30
`
`15 of such a comparison, speci?cally a comparison of disease
`pattern match results and eXempli?cative data is provided in
`Table 11. As depicted in FIG. 3 and set forth in Table 11, a
`?rst set of disease pattern match data is derived from blood,
`20 urine or other ?uid testing on a ?rst date; this data is derived
`using portion 7 of the FIG. 3 schematic as discussed
`hereinabove With regard to FIG. 1 and shoWn in Table 8. On
`a second date (date A) further testing of the individual is
`25 accomplished, as represented by schematic portion 7A,
`Wherein neW personal bodily ?uid test results 3A are devel
`_
`oped. The test results 3A are compared With the human
`experience data 2 to yield neW percent status data 4A for all
`indicators, Which data 4A is utiliZed to develop in neW
`_
`presence levels 5A, and neW disease pattern matches 6A as
`set forth in Table 11. The disease pattern match data of 6 and
`6A is compared 15 and changes in disease pattern matches
`
`-
`
`-
`
`-
`
`_
`
`_
`
`_
`
`_
`
`_
`
`_
`
`_
`
`_
`
`_
`
`Panel Status Deviation
`
`Panel Status Skew
`
`27.50
`
`2750
`
`16 are identi?ed (see Table 11) as a means of providing
`health status data related to the individual.
`
`TABLE 11
`
`First Date
`
`Date A
`
`DISEASE
`
`Anterior Pituitary
`Hypofunction
`Pernicious Anemia
`Vitamin C De?ciency
`Rheumatoid Arthritis
`Acute Myocardial
`Infarction
`
`%
`%
`# OF
`%
`# OF
`# OF
`ICD-9
`CODE MATCHES INDICATORS MATCH MATCHES MATCH CHANGE
`
`253.40
`
`281.00
`267.00
`714.00
`410.00
`
`5
`
`6
`3
`5
`5
`
`10
`
`15
`8
`15
`15
`
`50.00
`
`40.00
`37.50
`33.33
`33.33
`
`6
`
`5
`3
`5
`4
`
`60.00
`
`—10.00
`
`33.33
`37.50
`37.50
`26.66
`
`+6.67
`0
`0
`+6.67
`
`_
`TABLE 10-continued
`
`55
`
`The neW percent status data developed for date A in step
`_
`_
`4A of FIG. 3 can be utiliZed to develop neW panel status
`
`INDICATOR
`
`Result
`
`% Status
`
`information for date A in the same manner as is taught
`
`CARDIAC MARKER
`
`hereinabove With regard to FIG. 2. Thereafter, the panel
`
`Cholesterol
`Triglycerides
`
`SGOT
`
`LDH
`Panel Status Deviation
`Panel Status SkeW
`
`181
`98-0
`
`23.0
`
`4140
`
`_17_21
`28-75
`
`-5.00
`
`_31_95
`20-73
`—6.35
`
`6O status data of the ?rst test date can be compared With the neW
`panel status data for date A to provide information on the
`
`_
`
`_
`
`_
`
`65
`
`5
`
`_
`
`_
`
`individual s medical health changes. FIG. 4 depicts such a
`panel status data comparison from a ?rst date and a su